Data Warehousing - Quick Guide

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Data Warehousing - Overview

The term "Data Warehouse" was first coined by Bill Inmon in 1990. He said that Data warehouse is subject Oriented, Integrated, Time-Variant and nonvolatile collection of data.This data helps in supporting decision making process by analyst in an organization

The operational database undergoes the per day transactions which causes the frequent changes to the data on daily basis.But if in future the business executive wants to analyse the previous feedback on any data such as product,supplier,or the consumer data. In this case the analyst will be having no data available to analyse because the previous data is updated due to transactions.

The Data Warehouses provide us generalized and consolidated data in multidimensional view. Along with generalize and consolidated view of data the Data Warehouses also provide us Online Analytical Processing (OLAP) tools. These tools help us in interactive and effective analysis of data in multidimensional space. This analysis results in data generalization and data mining.

The data mining functions like association,clustering ,classification, prediction can be integrated with OLAP operations to enhance interactive mining of knowledge at multiple level of abstraction. That's why data warehouse has now become important platform for data analysis and online analytical processing.

Understanding Data Warehouse

  • The Data Warehouse is that database which is kept separate from the organization's operational database.

  • There is no frequent updation done in data warehouse.

  • Data warehouse possess consolidated historical data which help the organization to analyse it's business.

  • Data warehouse helps the executives to organize,understand and use their data to take strategic decision.

  • Data warehouse systems available which helps in integration of diversity of application systems.

  • The Data warehouse system allows analysis of consolidated historical data analysis.

Definition

Data warehouse is Subject Oriented, Integrated, Time-Variant and Nonvolatile collection of data that support management's decision making process.

Why Data Warehouse Separated from Operational Databases

The following are the reasons why Data Warehouse are kept separate from operational databases:

  • The operational database is constructed for well known tasks and workload such as searching particular records, indexing etc but the data warehouse queries are often complex and it presents the general form of data.

  • Operational databases supports the concurrent processing of multiple transactions.Concurrency control and recovery mechanism are required for operational databases to ensure robustness and consistency of database.

  • Operational database query allow to read, modify operations while the OLAP query need only read only access of stored data.

  • Operational database maintain the current data on the other hand data warehouse maintain the historical data.

Data Warehouse Features

The key features of Data Warehouse such as Subject Oriented, Integrated, Nonvolatile and Time-Variant are are discussed below:

  • Subject Oriented - The Data Warehouse is Subject Oriented because it provide us the information around a subject rather the organization's ongoing operations. These subjects can be product, customers, suppliers, sales, revenue etc. The data warehouse does not focus on the ongoing operations rather it focuses on modelling and analysis of data for decision making.

  • Integrated - Data Warehouse is constructed by integration of data from heterogeneous sources such as relational databases, flat files etc. This integration enhance the effective analysis of data.

  • Time-Variant - The Data in Data Warehouse is identified with a particular time period. The data in data warehouse provide information from historical point of view.

  • Non Volatile - Non volatile means that the previous data is not removed when new data is added to it. The data warehouse is kept separate from the operational database therefore frequent changes in operational database is not reflected in data warehouse.

Note: - Data Warehouse does not require transaction processing, recovery and concurrency control because it is physically stored separate from the operational database.

Data Warehouse Applications

As discussed before Data Warehouse helps the business executives in organize, analyse and use their data for decision making. Data Warehouse serves as a soul part of a plan-execute-assess "closed-loop" feedback system for enterprise management. Data Warehouse is widely used in the following fields:

  • financial services

  • Banking Services

  • Consumer goods

  • Retail sectors.

  • Controlled manufacturing

Data Warehouse Types

Information processing, Analytical processing and Data Mining are the three types of data warehouse applications that are discussed below:

  • Information processing - Data Warehouse allow us to process the information stored in it.The information can be processed by means of querying, basic statistical analysis, reporting using crosstabs, tables, charts, or graphs.

  • Analytical Processing - Data Warehouse supports analytical processing of the information stored in it.The data can be analysed by means of basic OLAP operations,including slice-and-dice,drill down,drill up, and pivoting.

  • Data Mining - Data Mining supports knowledge discovery by finding the hidden patterns and associations, constructing analytical models, performing classification and prediction.These mining results can be presented using the visualization tools.

SNData Warehouse (OLAP)Operational Database(OLTP)
1This involves historical processing of information.This involves day to day processing.
2OLAP systems are used by knowledge workers such as executive, manager and analyst.OLTP system are used by clerk, DBA, or database professionals.
3This is used to analysis the business.This is used to run the business.
4It focuses on Information out.It focuses on Data in.
5This is based on Star Schema, Snowflake Schema and Fact Constellation Schema.This is based on Entity Relationship Model.
6It focuses on Information out.This is application oriented.
7This contains historical data.This contains current data.
8This provides summarized and consolidated data.This provide primitive and highly detailed data.
9This provide summarized and multidimensional view of data.This provides detailed and flat relational view of data.
10The number or users are in Hundreds.The number of users are in thousands.
11The number of records accessed are in millions.The number of records accessed are in tens.
12The database size is from 100GB to TBThe database size is from 100 MB to GB.
13This are highly flexible.This provide high performance.

Data Warehousing - Concepts

What is Data Warehousing?

Data Warehousing is the process of constructing and using the data warehouse. The data warehouse is constructed by integrating the data from multiple heterogeneous sources. This data warehouse supports analytical reporting, structured and/or ad hoc queries and decision making. Data Warehousing involves data cleaning, data integration and data consolidations.

Using Data Warehouse Information

There are decision support technologies available which help to utilize the data warehouse. These technologies helps the executives to use the warehouse quickly and effectively. They can gather the data, analyse it and take the decisions based on the information in the warehouse. The information gathered from the warehouse can be used in any of the following domains:

  • Tuning production strategies - The product strategies can be well tuned by repositioning the products and managing product portfolios by comparing the sales quarterly or yearly.

  • Customer Analysis - The customer analysis is done by analyzing the customer's buying preferences, buying time, budget cycles etc.

  • Operations Analysis - Data Warehousing also helps in customer relationship management, making environmental corrections.The Information also allow us to analyse the business operations.

Integrating Heterogeneous Databases

To integrate heterogeneous databases we have the two approaches as follows:

  • Query Driven Approach

  • Update Driven Approach

Query Driven Approach

This is the traditional approach to integrate heterogeneous databases. This approach was used to build wrappers and integrators on the top of multiple heterogeneous databases. These integrators are also known as mediators.

Process of Query Driven Approach:

  • when the query is issued to a client side, a metadata dictionary translate the query into the queries appropriate for the individual heterogeneous site involved.

  • Now these queries are mapped and sent to the local query processor.

  • The results from heterogeneous sites are integrated into a global answer set.

Disadvantages

  • The Query Driven Approach needs complex integration and filtering processes.

  • This approach is very inefficient.

  • This approach is very expensive for frequent queries.

  • This approach is also very expensive for queries that requires aggregations.

Update Driven Approach

We are provided with the alternative approach to traditional approach. Today's Data Warehouse system follows update driven approach rather than the traditional approach discussed earlier. In Update driven approach the information from multiple heterogeneous sources is integrated in advance and stored in a warehouse. This information is available for direct querying and analysis.

Advantages

This approach has the following advantages:

  • This approach provide high performance.

  • The data are copied, processed, integrated, annotated, summarized and restructured in semantic data store in advance.

  • Query processing does not require interface with the processing at local sources.

Data Warehouse Tools and Utilities Functions

The following are the functions of Data Warehouse tools and Utilities:

  • Data Extraction - Data Extraction involves gathering the data from multiple heterogeneous sources.

  • Data Cleaning - Data Cleaning involves finding and correcting the errors in data.

  • Data Transformation - Data Transformation involves converting data from legacy format to warehouse format.

  • Data Loading - Data Loading involves sorting, summarizing, consolidating, checking integrity and building indices and partitions.

  • Refreshing - Refreshing involves updating from data sources to warehouse.

Note: Data Cleaning and Data Transformation are important steps in improving the quality of data and data mining results.

Data Warehousing - Terminologies

In this article, we will discuss some of the commonly used terms in Data Warehouse.


Data Warehouse

Data warehouse is subject Oriented, Integrated, Time-Variant and nonvolatile collection of data that support of management's decision making process. Let's explore this Definition of data warehouse.

  • Subject Oriented - The Data warehouse is subject oriented because it provide us the information around a subject rather the organization's ongoing operations. These subjects can be product, customers, suppliers, sales, revenue etc. The data warehouse does not focus on the ongoing operations rather it focuses on modelling and analysis of data for decision making.

  • Integrated - Data Warehouse is constructed by integration of data from heterogeneous sources such as relational databases, flat files etc. This integration enhance the effective analysis of data.

  • Time-Variant - The Data in Data Warehouse is identified with a particular time period. The data in data warehouse provide information from historical point of view.

  • Non Volatile - Non volatile means that the previous data is not removed when new data is added to it. The data warehouse is kept separate from the operational database therefore frequent changes in operational database is not reflected in data warehouse.

  • Metadata - Metadata is simply defined as data about data. The data that are used to represent other data is known as metadata. For example the index of a book serve as metadata for the contents in the book.In other words we can say that metadata is the summarized data that lead us to the detailed data.

In terms of data warehouse we can define metadata as following:

  • Metadata is a road map to data warehouse.

  • Metadata in data warehouse define the warehouse objects.

  • The metadata act as a directory.This directory helps the decision support system to locate the contents of data warehouse.

Metadata Respiratory

The Metadata Respiratory is an integral part of data warehouse system. The Metadata Respiratory contains the following metadata:

  • Business Metadata - This metadata has the data ownership information, business definition and changing policies.

  • Operational Metadata -This metadata includes currency of data and data lineage. Currency of data means whether data is active, archived or purged. Lineage of data means history of data migrated and transformation applied on it.

  • Data for mapping from operational environment to data warehouse -This metadata includes source databases and their contents, data extraction,data partition, cleaning, transformation rules, data refresh and purging rules.

  • The algorithms for summarization - This includes dimension algorithms, data on granularity, aggregation, summarizing etc.

Data cube

Data cube help us to represent the data in multiple dimensions. The data cube is defined by dimensions and facts. The dimensions are the entities with respect to which an enterprise keep the records.

Illustration of Data cube

Suppose a company wants to keep track of sales records with help of sales data warehouse with respect to time, item, branch and location. These dimensions allow to keep track of monthly sales and at which branch the items were sold.There is a table associated with each dimension. This table is known as dimension table. This dimension table further describes the dimensions. For example "item" dimension table may have attributes such as item_name, item_type and item_brand.

The following table represents 2-D view of Sales Data for a company with respect to time,item and location dimensions.

data cube 2D

But here in this 2-D table we have records with respect to time and item only. The sales for New Delhi are shown with respect to time and item dimensions according to type of item sold. If we want to view the sales data with one new dimension say the location dimension. The 3-D view of the sales data with respect to time, item, and location is shown in the table below:

data cube 3D

The above 3-D table can be represented as 3-D data cube as shown in the following figure:

data cube 3D

Data mart

Data mart contains the subset of organisation-wide data. This subset of data is valuable to specific group of an organisation. in other words we can say that data mart contains only that data which is specific to a particular group. For example the marketing data mart may contain only data related to item, customers and sales. The data mart are confined to subjects.

Points to remember about data marts:

  • window based or Unix/Linux based servers are used to implement data marts. They are implemented on low cost server.

  • The implementation cycle of data mart is measured in short period of time i.e. in weeks rather than months or years.

  • The life cycle of a data mart may be complex in long run if it's planning and design are not organisation-wide.

    Data mart are small in size.
  • Data mart are customized by department.

  • The source of data mart is departmentally structured data warehouse.

  • Data mart are flexible.

Graphical Representation of data mart.

data mart

Virtual Warehouse

The view over a operational data warehouse is known as virtual warehouse. It is easy to built the virtual warehouse. Building the virtual warehouse requires excess capacity on operational database servers.

Data Warehousing - Delivery Process

Introduction

The data warehouse are never static. It evolves as the business increases. The today's need may be different from the future needs.We must design the data warehouse to change constantly. The real problem is that business itself is not aware of its requirement for information in the future.As business evolves it's need also changes therefore the data warehuose must be designed to ride with these changes. Hence the data warehouse systems need to be flexible.

There should be a delivery process to deliver the data warehouse.But there are many issues in data warehouse projects that it is very difficult to complete the task and deliverables in the strict, ordered fashion demanded by waterfall method because the requirements are hardly fully understood. Hence when the requirements are completed only then the architectures designs, and build components can be completed.

Delivery Method

The delivery method is a variant of the joint application development approach, adopted for delivery of data warehouse. We staged the data warehouse delivery process to minimize the risk. The approach that i will discuss does not reduce the overall delivery time-scales but ensures business benefits are delivered incrementally through the development process.

Note: The delivery process is broken into phases to reduce the project and delivery risk.

Following diagram Explain the Stages in delivery process:

Delivery Method

IT Strategy

Data warehouse are strategic investments, that require business process to generate the project benefits. IT Strategy is required to procure and retain funding for the project.

Business Case

The objective of Business case is to know the projected business benefits that should be derived from using the data warehouse. These benefits may not be quantifiable but the projected benefits need to be clearly stated.. If the data warehouse does not have a clear business case then the business tend to suffer from the credibility problems at some stage during the delivery process.Therefore in data warehouse project we need to understand the business case for investment.

Education and Prototyping

The organization will experiment with the concept of data analysis and educate themselves on the value of data warehouse before determining that a data warehouse is prior solution. This is addressed by prototyping. This prototyping activity helps in understanding the feasibility and benefits of a data warehouse. The Prototyping activity on a small scale can further the educational process as long as:

  • The prototype address a defined technical objective.

  • The prototype can be thrown away after the feasibility concept has been shown.

  • The activity addresses a small subset of eventual data content if the data warehouse.

  • The activity timescale is non- critical.

Points to remember to produce an early release of a part of a data warehouse to deliver business benefits.

  • Identify the architecture that is capable of evolving.

  • Focus on the business requirements and technical blueprint phases.

  • Limit the scope of the first build phase to the minimum that delivers business benefits.

  • Understand the short term and medium term requirements of the data warehouse.

Business Requirements

To provide the quality deliverables we should make sure that overall requirements are understood. The business requirements and the technical blueprint stages are required because of the following reasons:

  • If we understand the business requirements for both short and medium term then we can design a solution that satisfies the short term need.

  • This would be capable of growing to the full solution.

Things to determine in this stage are following.

  • The business rule to be applied on data.

  • The logical model for information within the data warehouse.

  • The query profiles for the immediate requirement.

  • The source systems that provide this data.

Technical Blueprint

This phase need to deliver an overall architecture satisfying the long term requirements. This phase also deliver the components that must be implemented in a short term to derive any business benefit. The blueprint need to identify the followings.

  • The overall system architecture.

  • The data retention policy.

  • The backup and recovery strategy.

  • The server and data mart architecture.

  • The capacity plan for hardware and infrastructure.

  • The components of database design.


Building the version

  • In this stage the first production deliverable is produced.

  • This production deliverable smallest component of data warehouse.

  • This smallest component adds business benefit.

History Load

This is the phase where the remainder of the required history is loaded into the data warehouse. In this phase we do not add the new entities but additional physical tables would probably be created to store the increased data volumes.

Let's have an example, Suppose the build version phase has delivered a retail sales analysis data warehouse with 2 months worth of history. This information will allow the user to analyse only the recent trends and address the short term issues. The user can not identify the annual and seasonal trends. So the 2 years worth of sales history could be loaded from the archive to make user to analyse the sales trend yearly and seasonal. Now the 40GB data is extended to 400GB.

Note:The backup and recovery procedures may become complex therefore it is recommended that perform this activity within separate phase.

Ad hoc Query

  • In this phase we configure an ad hoc query tool.

  • This ad hoc query tool is used to operate the data warehouse.

  • These tools can generate the database query.

Note:It is recommended that not to use these access tolls when database is being substantially modified.

Automation

In this phase operational management processes are fully automated. These would include:

  • Transforming the data into a form suitable for analysis.

  • Monitoring query profiles and determining the appropriate aggregations to maintain system performance.

  • Extracting and loading the data from different source systems.

  • Generating aggregations from predefined definitions within the data warehouse.

  • Backing Up, restoring and archiving the data.

Extending Scope

In this phase the data warehouse is extended to address a new set of business requirements. The scope can be extended in two ways:

  • By loading additional data into the data warehouse.

  • By introducing new data marts using the existing information.

Note:This phase should be performed separately since this phase involves substantial efforts and complexity.

Requirements Evolution

From the perspective of delivery process the requirement are always changeable. They are not static.The delivery process must support this and allow these changes to be reflected within the system.

This issue is addressed by designing the data warehouse around the use of data within business processes, as opposed to the data requirements of existing queries.

The architecture is designed to change and grow to match the business needs,the process operates as a pseudo application development process, where the new requirements are continually fed into the development activities. The partial deliverables are produced.These partial deliverables are fed back to users and then reworked ensuring that overall system is continually updated to meet the business needs.

Data Warehousing - System Processes

We have fixed number of operations to be applied on operational databases and we have well defined techniques such as use normalized data,keep table small etc. These techniques are suitable for delivering a solution. But in case of decision support system we do not know what query and operation need to be executed in future. Therefore techniques applied on operational databases are not suitable for data warehouses.

In this chapter We'll focus on designing data warehousing solution built on the top open-system technologies like Unix and relational databases.

Process Flow in Data Warehouse

There are four major processes that build a data warehouse. Here is the list of four processes:

  • Extract and load data.

  • Cleaning and transforming the data.

  • Backup and Archive the data.

  • Managing queries & directing them to the appropriate data sources.

Process Flow

Extract and Load Process

  • The Data Extraction takes data from the source systems.

  • Data load takes extracted data and loads it into data warehouse.

Note: Before loading the data into data warehouse the information extracted from external sources must be reconstructed.

Points to remember while extract and load process:

  • Controlling the process

  • When to Initiate Extract

  • Loading the Data

Controlling the process

Controlling the process involves determining that when to start data extraction and consistency check on data. Controlling process ensures that tools, logic modules, and the programs are executed in correct sequence and at correct time.

When to Initiate Extract

Data need to be in consistent state when it is extracted i.e. the data warehouse should represent single, consistent version of information to the user.

For example in a customer profiling data warehouse in telecommunication sector it is illogical to merge list of customers at 8 pm on wednesday from a customer database with the customer subscription events up to 8 pm on tuesday. This would mean that we are finding the customers for whom there are no associated subscription.

Loading the Data

After extracting the data it is loaded into a temporary data store.Here in the temporary data store it is cleaned up and made consistent.

Note: Consistency checks are executed only when all data sources have been loaded into temporary data store.

Clean and Transform Process

Once data is extracted and loaded into temporary data store it is the time to perform Cleaning and Transforming. Here is the list of steps involved in Cleaning and Transforming:

  • Clean and Transform the loaded data into a structure.

  • Partition the data.

  • Aggregation

Clean and Transform the loaded data into a structure

This will speed up the queries.This can be done in the following ways:

  • Make sure data is consistent within itself.

  • Make sure data is consistent with other data within the same data source.

  • Make sure data is consistent with data in other source systems.

  • Make sure data is consistent with data already in the warehouse.

Transforming involves converting the source data into a structure. Structuring the data will result in increases query performance and decreases operational cost. Information in data warehouse must be transformed to support performance requirement from the business and also the ongoing operational cost.

Partition the data

It will optimize the hardware performance and simplify the management of data warehouse. In this we partition each fact table into a multiple separate partitions.

Aggregation

Aggregation is required to speed up the common queries. Aggregation rely on the fact that most common queries will analyse a subset or an aggregation of the detailed data.

Backup and Archive the data

In order to recover the data in event of data loss, software failure or hardware failure it is necessary to backed up on regular basis.Archiving involves removing the old data from the system in a format that allow it to be quickly restored whenever required.

For example in a retail sales analysis data warehouse, it may be required to keep data for 3 years with latest 6 months data being kept online. In this kind of scenario there is often requirement to be able to do month-on-month comparisons for this year and last year. In this case we require some data to be restored from the archive.

Query Management Process

This process performs the following functions

  • This process manages the queries.

  • This process speed up the queries execution.

  • This Process direct the queries to most effective data sources.

  • This process should also ensure that all system sources are used in most effective way.

  • This process is also required to monitor actual query profiles.

  • Information in this process is used by warehouse management process to determine which aggregations to generate.

  • This process does not generally operate during regular load of information into data warehouse.

Data Warehousing - Architecture

In this article, we will discuss the business analysis framework for data warehouse design and architecture of a data warehouse.

Business Analysis Framework

The business analyst get the information from the data warehouses to measure the performance and make critical adjustments in order to win over other business holders in the market. Having data warehouse has the following advantages for the business.

  • Since the data warehouse can gather the information quickly and efficiently therefore it can enhance the business productivity.

  • The data warehouse provides us the consistent view of customers and items hence help us to manage the customer relationship.

  • The data warehouse also helps in bringing cost reduction by tracking trends, patterns over a long period in a consistent and reliable manner.

To design an effective and efficient data warehouse we are required to understand and analyze the business needs and construct a business analysis framework. Each person has different views regarding the design of a data warehouse. These views are as follows:

  • The top-down view - This view allows the selection of relevant information needed for data warehouse.

  • The data source view - This view presents the information being captured, stored, and managed by operational system.

  • The data warehouse view - This view includes the fact tables and dimension tables.This represent the information stored inside the data warehouse.

  • The Business Query view - It is the view of the data from the viewpoint of the end user.

Three-Tier Data Warehouse Architecture

Generally the data warehouses adopt the three-tier architecture. Following are the three tiers of data warehouse architecture.

  • Bottom Tier - The bottom tier of the architecture is the data warehouse database server.It is the relational database system.We use the back end tools and utilities to feed data into bottom tier.these back end tools and utilities performs the Extract, Clean, Load, and refresh functions.

  • Middle Tier - In the middle tier we have OLAp Server. the OLAP Server can be implemented in either of the following ways.

    • By relational OLAP (ROLAP), which is an extended relational database management system. The ROLAP maps the operations on multidimensional data to standard relational operations.

    • By Multidimensional OLAP (MOLAP) model, which directly implements multidimensional data and operations.

  • Top-Tier - This tier is the front-end client layer. This layer hold the query tools and reporting tool, analysis tools and data mining tools.

Following diagram explains the Three-tier Architecture of Data warehouse:

Data Warehousing Architecture

Data Warehouse Models

From the perspective of data warehouse architecture we have the following data warehouse models:

  • Virtual Warehouse

  • Data mart

  • Enterprise Warehouse

Virtual Warehouse

  • The view over a operational data warehouse is known as virtual warehouse. It is easy to built the virtual warehouse.

  • Building the virtual warehouse requires excess capacity on operational database servers.

Data Mart

  • Data mart contains the subset of organisation-wide data.

  • This subset of data is valuable to specific group of an organisation

Note: in other words we can say that data mart contains only that data which is specific to a particular group. For example the marketing data mart may contain only data related to item, customers and sales. The data mart are confined to subjects.

Points to remember about data marts

  • window based or Unix/Linux based servers are used to implement data marts. They are implemented on low cost server.

  • The implementation cycle of data mart is measured in short period of time i.e. in weeks rather than months or years.

  • The life cycle of a data mart may be complex in long run if it's planning and design are not organisation-wide.

  • Data mart are small in size.

  • Data mart are customized by department.

  • The source of data mart is departmentally structured data warehouse.

  • Data mart are flexible.

Enterprise Warehouse

  • The enterprise warehouse collects all the information all the subjects spanning the entire organization

  • This provide us the enterprise-wide data integration.

  • This provide us the enterprise-wide data integration.

  • The data is integrated from operational systems and external information providers.

  • This information can vary from a few gigabytes to hundreds of gigabytes, terabytes or beyond.

Load Manager

  • This Component performs the operations required to extract and load process.

  • The size and complexity of load manager varies between specific solutions from data warehouse to data warehouse.

Load Manager Architecture

The load manager performs the following functions:

  • Extract the data from source system.

  • Fast Load the extracted data into temporary data store.

  • Perform simple transformations into structure similar to the one in the data warehouse.

Load Manager

Extract Data from Source

The data is extracted from the operational databases or the external information providers. Gateways is the application programs that are used to extract data. It is supported by underlying DBMS and allows client program to generate SQL to be executed at a server. Open Database Connection( ODBC), Java Database Connection (JDBC), are examples of gateway.

Fast Load

  • In order to minimize the total load window the data need to be loaded into the warehouse in the fastest possible time.

  • The transformations affects the speed of data processing.

  • It is more effective to load the data into relational database prior to applying transformations and checks.

  • Gateway technology proves to be not suitable, since they tend not be performant when large data volumes are involved.

Simple Transformations

While loading it may be required to perform simple transformations. After this has been completed we are in position to do the complex checks. Suppose we are loading the EPOS sales transaction we need to perform the following checks:

  • Strip out all the columns that are not required within the warehouse.

  • Convert all the values to required data types.

Warehouse Manager

  • Warehouse manager is responsible for the warehouse management process.

  • The warehouse manager consist of third party system software, C programs and shell scripts.

  • The size and complexity of warehouse manager varies between specific solutions.

Warehouse Manager Architecture

The warehouse manager includes the following:

  • The Controlling process

  • Stored procedures or C with SQL

  • Backup/Recovery tool

  • SQL Scripts

Warehouse Manager

Operations Performed by Warehouse Manager

  • Warehouse manager analyses the data to perform consistency and referential integrity checks.

  • Creates the indexes, business views, partition views against the base data.

  • Generates the new aggregations and also updates the existing aggregation. Generates the normalizations.

  • Warehouse manager Warehouse manager transforms and merge the source data into the temporary store into the published data warehouse.

  • Backup the data in the data warehouse.

  • Warehouse Manager archives the data that has reached the end of its captured life.

Note: Warehouse Manager also analyses query profiles to determine index and aggregations are appropriate.

Query Manager

  • Query Manager is responsible for directing the queries to the suitable tables.

  • By directing the queries to appropriate table the query request and response process is speed up.

  • Query Manager is responsible for scheduling the execution of the queries posed by the user.

Query Manager Architecture

Query Manager includes the following:

  • The query redirection via C tool or RDBMS.

  • Stored procedures.

  • Query Management tool.

  • Query Scheduling via C tool or RDBMS.

  • Query Scheduling via third party Software.


Query Manager

Detailed information

The following diagram shows the detailed information

Detailed Information

The detailed information is not kept online rather is aggregated to the next level of detail and then archived to the tape. The detailed infomation part of data warehouse keep the detailed information in the starflake schema. the detailed information is loaded into the data warehouse to supplement the aggregated data.

Note: If the detailed information is held offline to minimize the disk storage we should make sure that the data has been extracted, cleaned up, and transformed then into starflake schema before it is archived.

Summary Information

  • In this area of data warehouse the predefined aggregations are kept.

  • These aggregations are generated by warehouse manager.

  • This area changes on ongoing basis in order to respond to the changing query profiles.

  • This area of data warehouse must be treated as transient.

Points to remember about summary information.

  • The summary data speed up the performance of common queries.

  • It increases the operational cost.

  • It need to be updated whenever new data is loaded into the data warehouse.

  • It may not have been backed up, since it can be generated fresh from the detailed information.

Data Warehousing - OLAP

Introduction

Online Analytical Processing Server (OLAP) is based on multidimensional data model. It allows the managers , analysts to get insight the information through fast, consistent, interactive access to information. In this chapter we will discuss about types of OLAP, operations on OLAP, Difference between OLAP and Statistical Databases and OLTP.

Types of OLAP Servers

We have four types of OLAP servers that are listed below.

  • Relational OLAP(ROLAP)

  • Multidimensional OLAP (MOLAP)

  • Hybrid OLAP (HOLAP)

  • Specialized SQL Servers


Relational OLAP(ROLAP)

The Relational OLAP servers are placed between relational back-end server and client front-end tools. To store and manage warehouse data the Relational OLAP use relational or extended-relational DBMS.

ROLAP includes the following.

  • implementation of aggregation navigation logic.

  • optimization for each DBMS back end.

  • additional tools and services.

Multidimensional OLAP (MOLAP)

Multidimensional OLAP (MOLAP) uses the array-based multidimensional storage engines for multidimensional views of data.With multidimensional data stores, the storage utilization may be low if the data set is sparse. Therefore many MOLAP Server uses the two level of data storage representation to handle dense and sparse data sets.

Hybrid OLAP (HOLAP)

The hybrid OLAP technique combination of ROLAP and MOLAP both. It has both the higher scalability of ROLAP and faster computation of MOLAP. HOLAP server allows to store the large data volumes of detail data. the aggregations are stored separated in MOLAP store.

Specialized SQL Servers

specialized SQL servers provides advanced query language and query processing support for SQL queries over star and snowflake schemas in a read-only environment.

OLAP Operations

As we know that the OLAP server is based on the multidimensional view of data hence we will discuss the OLAP operations in multidimensional data.

Here is the list of OLAP operations.

  • Roll-up

  • Drill-down

  • Slice and dice

  • Pivot (rotate)


Roll-up

This operation performs aggregation on a data cube in any of the following way:

  • By climbing up a concept hierarchy for a dimension

  • By dimension reduction.

Consider the following diagram showing the roll-up operation.

Roll-up
  • The roll-up operation is performed by climbing up a concept hierarchy for the dimension location.

  • Initially the concept hierarchy was "street < city < province < country".

  • On rolling up the data is aggregated by ascending the location hierarchy from the level of city to level of country.

  • The data is grouped into cities rather than countries.

  • When roll-up operation is performed then one or more dimensions from the data cube are removed.

Drill-down

Drill-down operation is reverse of the roll-up. This operation is performed by either of the following way:

  • By stepping down a concept hierarchy for a dimension.

  • By introducing new dimension.

Consider the following diagram showing the drill-down operation:


Drill-Down
  • The drill-down operation is performed by stepping down a concept hierarchy for the dimension time.

  • Initially the concept hierarchy was "day < month < quarter < year."

  • On drill-up the time dimension is descended from the level quarter to the level of month.

  • When drill-down operation is performed then one or more dimensions from the data cube are added.

  • It navigates the data from less detailed data to highly detailed data.


Slice

The slice operation performs selection of one dimension on a given cube and give us a new sub cube. Consider the following diagram showing the slice operation.

Slice
  • The Slice operation is performed for the dimension time using the criterion time ="Q1".

  • It will form a new sub cube by selecting one or more dimensions.

Dice

The Dice operation performs selection of two or more dimension on a given cube and give us a new subcube. Consider the following diagram showing the dice operation:

Dice

The dice operation on the cube based on the following selection criteria that involve three dimensions.

  • (location = "Toronto" or "Vancouver")

  • (time = "Q1" or "Q2")

  • (item =" Mobile" or "Modem").

Pivot

The pivot operation is also known as rotation.It rotates the data axes in view in order to provide an alternative presentation of data.Consider the following diagram showing the pivot operation.

Pivot

In this the item and location axes in 2-D slice are rotated.

OLAP vs OLTP

SNData Warehouse (OLAP)Operational Database(OLTP)
1This involves historical processing of information.This involves day to day processing.
2OLAP systems are used by knowledge workers such as executive, manager and analyst.OLTP system are used by clerk, DBA, or database professionals.
3This is used to analysis the business.This is used to run the business.
4It focuses on Information out.It focuses on Data in.
5This is based on Star Schema, Snowflake Schema and Fact Constellation Schema.This is based on Entity Relationship Model.
6It focuses on Information out.This is application oriented.
7This contains historical data.This contains current data.
8This provides summarized and consolidated data.This provide primitive and highly detailed data.
9This provide summarized and multidimensional view of data.This provides detailed and flat relational view of data.
10The number or users are in Hundreds.The number of users are in thousands.
11The number of records accessed are in millions.The number of records accessed are in tens.
12The database size is from 100GB to TBThe database size is from 100 MB to GB.
13This are highly flexible.This provide high performance.

Data Warehousing - Relational OLAP

Introduction

The Relational OLAP servers are placed between relational back-end server and client front-end tools. To store and manage warehouse data the Relational OLAP use relational or extended-relational DBMS.

ROLAP includes the following.

  • implementation of aggregation navigation logic.

  • optimization for each DBMS back end.

  • additional tools and services.

Note: The ROLAP servers are highly scalable.

Points to remember

  • The ROLAP tools need to analyze large volume of data across multiple dimensions.

  • The ROLAP tools need to store and analyze highly volatile and changeable data.

Relational OLAP Architecture

The ROLAP includes the following.

  • Database Server

  • ROLAP Server

  • Front end tool

Advantages

  • The ROLAP servers are highly scalable.

  • They can be easily used with the existing RDBMS.

  • Data Can be stored efficiently since no zero facts can be stored.

  • ROLAP tools do not use pre-calculated data cubes.

  • DSS server of microstrategy adopts the ROLAP approach.

Disadvantages

  • Poor query performance.

  • Some limitations of scalability depending on the technology architecture that is utilized.

Data Warehousing - Multidimensional OLAP

Introduction

Multidimensional OLAP (MOLAP) uses the array-based multidimensional storage engines for multidimensional views of data. With multidimensional data stores, the storage utilization may be low if the data set is sparse. Therefore many MOLAP Server uses the two level of data storage representation to handle dense and sparse data sets.

Points to remember:

  • MOLAP tools need to process information with consistent response time regardless of level of summarizing or calculations selected.

  • The MOLAP tools need to avoid many of the complexities of creating a relational database to store data for analysis.

  • The MOLAP tools need fastest possible performance.

  • MOLAP Server adopts two level of storage representation to handle dense and sparse data sets.

  • Denser subcubes are identified and stored as array structure.

  • Sparse subcubes employs compression technology.

MOLAP Architecture

MOLAP includes the following components.

  • Database server

  • MOLAP server

  • Front end tool

Advantages

Here is the list of advantages of Multidimensional OLAP

  • MOLAP allows fastest indexing to the precomputed summarized data.

  • Helps the user who are connected to a network and need to analyze larger, less defined data.

  • Easier to use therefore MOLAP is best suitable for inexperienced user.

Disadvantages

  • MOLAP are not capable of containing detailed data.

  • The storage utilization may be low if the data set is sparse.

MOLAP vs ROLAP

SNMOLAPROLAP
1The information retrieval is fast.Information retrieval is comparatively slow.
2It uses the sparse array to store the data sets.It uses relational table.
3MOLAP is best suited for inexperienced users since it is very easy to use.ROLAP is best suited for experienced users.
4The separate database for data cube.It may not require space other than available in Data warehouse.
5DBMS facility is weak.DBMS facility is strong.

Data Warehousing - Schemas

Introduction

The schema is a logical description of the entire database. The schema includes the name and description of records of all record types including all associated data-items and aggregates. Likewise the database the data warehouse also require the schema. The database uses the relational model on the other hand the data warehouse uses the Stars, snowflake and fact constellation schema. In this chapter we will discuss the schemas used in data warehouse.

Star Schema

  • In star schema each dimension is represented with only one dimension table.

  • This dimension table contains the set of attributes.

  • In the following diagram we have shown the sales data of a company with respect to the four dimensions namely, time, item, branch and location.

Start Schema
  • There is a fact table at the centre. This fact table contains the keys to each of four dimensions.

  • The fact table also contain the attributes namely, dollars sold and units sold.

Note: Each dimension has only one dimension table and each table holds a set of attributes. For example the location dimension table contains the attribute set {location_key,street,city,province_or_state,country}. This constraint may cause data redundancy. For example the "Vancouver" and "Victoria" both cities are both in Canadian province of British Columbia. The entries for such cities may cause data redundancy along the attributes province_or_state and country.

Snowflake Schema

  • In Snowflake schema some dimension tables are normalized.

  • The normalization split up the data into additional tables.

  • Unlike Star schema the dimensions table in snowflake schema are normalized for example the item dimension table in star schema is normalized and split into two dimension tables namely, item and supplier table.

Snowflake Schema
  • Therefore now the item dimension table contains the attributes item_key, item_name, type, brand, and supplier-key.

  • The supplier key is linked to supplier dimension table. The supplier dimension table contains the attributes supplier_key, and supplier_type.

Note: Due to normalization in Snowflake schema the redundancy is reduced therefore it becomes easy to maintain and save storage space.

Fact Constellation Schema

  • In fact Constellation there are multiple fact tables. This schema is also known as galaxy schema.

  • In the following diagram we have two fact tables namely, sales and shipping.

Fact Constellation Schema
  • The sale fact table is same as that in star schema.

  • The shipping fact table has the five dimensions namely, item_key, time_key, shipper-key, from-location.

  • The shipping fact table also contains two measures namely, dollars sold and units sold.

  • It is also possible for dimension table to share between fact tables. For example time, item and location dimension tables are shared between sales and shipping fact table.

Schema Definition

The Multidimensional schema is defined using Data Mining Query Language( DMQL). the two primitives namely, cube definition and dimension definition can be used for defining the Data warehouses and data marts.

Syntax for cube definition

define cube < cube_name > [ < dimension-list > }: < measure_list >

Syntax for dimension definition

define dimension < dimension_name > as ( < attribute_or_dimension_list > )

Star Schema Definition

The star schema that we have discussed can be defined using the Data Mining Query Language (DMQL) as follows:

define cube sales star [time, item, branch, location]:   
    	   
dollars sold = sum(sales in dollars), units sold = count(*)    	  

define dimension time as (time key, day, day of week, month, quarter, year)
define dimension item as (item key, item name, brand, type, supplier type)        	
define dimension branch as (branch key, branch name, branch type)              	
define dimension location as (location key, street, city, province or state, country)

Snowflake Schema Definition

The Snowflake schema that we have discussed can be defined using the Data Mining Query Language (DMQL) as follows:

         	
define cube sales snowflake [time, item, branch, location]:

dollars sold = sum(sales in dollars), units sold = count(*)

define dimension time as (time key, day, day of week, month, quarter, year)
define dimension item as (item key, item name, brand, type, supplier
(supplier key, supplier type))
define dimension branch as (branch key, branch name, branch type)
define dimension location as (location key, street, city
(city key, city, province or state, country))

Fact Constellation Schema Definition

The Snowflake schema that we have discussed can be defined using the Data Mining Query Language (DMQL) as follows:

      
define cube sales [time, item, branch, location]:

dollars sold = sum(sales in dollars), units sold = count(*)

define dimension time as (time key, day, day of week, month, quarter, year)
define dimension item as (item key, item name, brand, type, supplier type)
define dimension branch as (branch key, branch name, branch type)
define dimension location as (location key, street, city, province or state,country)
define cube shipping [time, item, shipper, from location, to location]:

dollars cost = sum(cost in dollars), units shipped = count(*)

define dimension time as time in cube sales
define dimension item as item in cube sales
define dimension shipper as (shipper key, shipper name, location as
location in cube sales, shipper type)
define dimension from location as location in cube sales
define dimension to location as location in cube sales

Data Warehousing - Partitioning Strategy

Introduction

The partitioning is done to enhance the performance and make the management easy. Partitioning also helps in balancing the various requirements of the system. It will optimize the hardware performance and simplify the management of data warehouse. In this we partition each fact table into a multiple separate partitions. In this chapter we will discuss about the partitioning strategies.

Why to Partition

Here is the list of reasons.

  • For easy management

  • To assist backup/recovery

  • To enhance performance


For easy management

The fact table in data warehouse can grow to many hundreds of gigabytes in size. This too large size of fact table is very hard to manage as a single entity. Therefore it needs partition.

To assist backup/recovery

If we do not have partitioned the fact table then we have to load the complete fact table with all the data.Partitioning allow us to load that data which is required on regular basis. This will reduce the time to load and also enhances the performance of the system.

Note: To cut down on the backup size all partitions other than the current partitions can be marked read only. We can then put these partition into a state where they can not be modified.Then they can be backed up .This means that only the current partition is to be backed up.

To enhance performance

By partitioning the fact table into sets of data the query procedures can be enhanced. The query performance is enhanced because now the query scans the partitions that are relevant. It does not have to scan the large amount of data.

Horizontal Partitioning

There are various way in which fact table can be partitioned. In horizontal partitioning we have to keep in mind the requirements for manageability of the data warehouse.

Partitioning by Time into equal Segments

In this partitioning strategy the fact table is partitioned on the bases of time period. Here each time period represents a significant retention period within the business. For example if the user queries for month to date data then it is appropriate to partition into monthly segments. We can reuse the partitioned tables by removing the data in them.

Partitioning by time into different-sized segments

This kind of partition is done where the aged data is accessed infrequently. This partition is implemented as a set of small partitions for relatively current data, larger partition for inactive data.

Partitioning by time into different-sized segments

Following is the list of advantages.

  • The detailed information remains available online.

  • The number of physical tables is kept relatively small, which reduces the operating cost.

  • This technique is suitable where the mix of data dipping recent history, and data mining through entire history is required.

Following is the list of disadvantages.

  • This technique is not useful where the partitioning profile changes on regular basis, because the repartitioning will increase the operation cost of data warehouse.

Partition on a different dimension

The fact table can also be partitioned on basis of dimensions other than time such as product group,region,supplier, or any other dimensions. Let's have an example.

Suppose a market function which is structured into distinct regional departments for example state by state basis. If each region wants to query on information captured within its region, it would proves to be more effective to partition the fact table into regional partitions. This will cause the queries to speed up because it does not require to scan information that is not relevant.

Following is the list of advantages.

  • Since the query does not have to scan the irrelevant data which speed up the query process.

Following is the list of disadvantages.

  • This technique is not appropriate where the dimensions are unlikely to change in future. So it is worth determining that the dimension does not change in future.

  • If the dimension changes then the entire fact table would have to be repartitioned.

Note: We recommend that do the partition only on the basis of time dimension unless you are certain that the suggested dimension grouping will not change within the life of data warehouse.

Partition by size of table

When there are no clear basis for partitioning the fact table on any dimension then we should partition the fact table on the basis of their size. We can set the predetermined size as a critical point. when the table exceeds the predetermined size a new table partition is created.

Following is the list of disadvantages.

  • This partitioning is complex to manage.

Note: This partitioning required metadata to identify what data stored in each partition.

Partitioning Dimensions

If the dimension contain the large number of entries then it is required to partition dimensions. Here we have to check the size of dimension.

Suppose a large design which changes over time. If we need to store all the variations in order to apply comparisons, that dimension may be very large. This would definitely affect the response time.

Round Robin Partitions

In round robin technique when the new partition is needed the old one is archived. In this technique metadata is used to allow user access tool to refer to the correct table partition.

Following is the list of advantages.

  • This technique make it easy to automate table management facilities within the data warehouse.

Vertical Partition

In Vertical Partitioning the data is split vertically.

Vertical Partitioning

The Vertical Partitioning can be performed in the following two ways.

  • Normalization

  • Row Splitting

Normalization

Normalization method is the standard relational method of database organization. In this method the rows are collapsed into single row, hence reduce the space.

Table before normalization

Product_idQuantityValuesales_dateStore_idStore_name Location Region
30 5 3.67 3-Aug-1316 sunny Bangalore S
35 4 5.33 3-Sep-1316 sunny Bangalore S
40 5 2.50 3-Sep-1364 san Mumbai W
45 7 5.66 3-Sep-1316 sunny Bangalore S

Table after normalization

Store_id Store_name Location Region
16 sunny Bangalore W
64 san Mumbai S
Product_id Quantity Value sales_date Store_id
30 5 3.67 3-Aug-1316
35 4 5.33 3-Sep-1316
40 5 2.50 3-Sep-1364
45 7 5.66 3-Sep-1316

Row Splitting

The row splitting tend to leave a one-to-one map between partitions. The motive of row splitting is to speed the access to large table by reducing its size.

Note: while using vertical partitioning make sure that there is no requirement to perform major join operations between two partitions.

Identify Key to Partition

It is very crucial to choose the right partition key.Choosing wrong partition key will lead you to reorganize the fact table. Let's have an example. Suppose we want to partition the following table.

Account_Txn_Table
transaction_id
account_id
transaction_type
value
transaction_date
region
branch_name

We can choose to partition on any key. The two possible keys could be

  • region

  • transaction_date

Now suppose the business is organised in 30 geographical regions and each region have different number of branches.That will give us 30 partitions, which is reasonable. This partitioning is good enough because our requirements capture has shown that vast majority of queries are restricted to the user's own business region.

Now If we partition by transaction_date instead of region. Then it means that the latest transaction from every region will be in one partition. Now the user who wants to look at data within his own region has to query across multiple partition.

Hence it is worth determining the right partitioning key.

Data Warehousing - Metadata Concepts

What is Metadata

Metadata is simply defined as data about data. The data that are used to represent other data is known as metadata. For example the index of a book serve as metadata for the contents in the book. In other words we can say that metadata is the summarized data that leads us to the detailed data. In terms of data warehouse we can define metadata as following.

  • Metadata is a road map to data warehouse.

  • Metadata in data warehouse define the warehouse objects.

  • The metadata act as a directory.This directory helps the decision support system to locate the contents of data warehouse.

Note: In data warehouse we create metadata for the data names and definitions of a given data warehouse. Along with this metadata additional metadata are also created for timestamping any extracted data, the source of extracted data.

Categories of Metadata

The metadata can be broadly categorized into three categories:

  • Business Metadata - This metadata has the data ownership information, business definition and changing policies.

  • Technical Metadata - Technical metadata includes database system names, table and column names and sizes, data types and allowed values. Technical metadata also includes structural information such as primary and foreign key attributes and indices.

  • Operational Metadata - This metadata includes currency of data and data lineage.Currency of data means whether data is active, archived or purged. Lineage of data means history of data migrated and transformation applied on it.

Metadata Categories

Role of Metadata

Metadata has very important role in data warehouse. The role of metadata in warehouse is different from the warehouse data yet it has very important role. The various roles of metadata are explained below.

  • The metadata act as a directory.

  • This directory helps the decision support system to locate the contents of data warehouse.

  • Metadata helps in decision support system for mapping of data when data are transformed from operational environment to data warehouse environment.

  • Metadata helps in summarization between current detailed data and highly summarized data.

  • Metadata also helps in summarization between lightly detailed data and highly summarized data.

  • Metadata are also used for query tools.

  • Metadata are used in reporting tools.

  • Metadata are used in extraction and cleansing tools.

  • Metadata are used in transformation tools.

  • Metadata also plays important role in loading functions.

Diagram to understand role of Metadata.

Role of Metadata

Metadata Respiratory

The Metadata Respiratory is an integral part of data warehouse system. The Metadata Respiratory has the following metadata:

  • Definition of data warehouse - This includes the description of structure of data warehouse. The description is defined by schema, view, hierarchies, derived data definitions, and data mart locations and contents.

  • Business Metadata - This metadata has the data ownership information, business definition and changing policies.

  • Operational Metadata - This metadata includes currency of data and data lineage. Currency of data means whether data is active, archived or purged. Lineage of data means history of data migrated and transformation applied on it.

  • Data for mapping from operational environment to data warehouse - This metadata includes source databases and their contents, data extraction,data partition cleaning, transformation rules, data refresh and purging rules.

  • The algorithms for summarization - This includes dimension algorithms, data on granularity, aggregation, summarizing etc.

Challenges for Metadata Management

The importance of metadata can not be overstated. Metadata helps in driving the accuracy of reports, validates data transformation and ensures the accuracy of calculations. The metadata also enforces the consistent definition of business terms to business end users. With all these uses of Metadata it also has challenges for metadata management. The some of the challenges are discussed below.

  • The Metadata in a big organization is scattered across the organization. This metadata is spreaded in spreadsheets, databases, and applications.

  • The metadata could present in text file or multimedia file. To use this data for information management solution, this data need to be correctly defined.

  • There are no industry wide accepted standards. The data management solution vendors have narrow focus.

  • There is no easy and accepted methods of passing metadata.

Data Warehousing - Data Marting

Why to create Datamart

    The following are the reasons to create datamart:

  • To partition data in order to impose access control strategies.

  • To speed up the queries by reducing the volume of data to be scanned.

  • To segment data into different hardware platforms.

  • To structure data in a form suitable for a user access tool.

Note: Donot data mart for any other reason since the operation cost of data marting could be very high. Before data marting, make sure that data marting strategy is appropriate for your particular solution.

Steps to determine that data mart appears to fit the bill

Following steps need to be followed to make cost effective data marting:

  • Identify the Functional Splits

  • Identify User Access Tool Requirements

  • Identify Access Control Issues


Identify the Functional Splits

In this step we determine that whether the natural functional split is there in the organization. We look for departmental splits, and we determine whether the way in which department use information tends to be in isolation from the rest of the organization. Let's have an example...

suppose in a retail organization where the each merchant is accountable for maximizing the sales of a group of products. For this the information that is valuable is :

  • sales transaction on daily basis

  • sales forecast on weekly basis

  • stock position on daily basis

  • stock movements on daily basis

As the merchant is not interested in the products they are not dealing with, so the data marting is subset of the data dealing which the product group of interest. Following diagram shows data marting for different users.


Issues in determining the functional split:

  • The structure of the department may change.

  • The products might switch from one department to other.

  • The merchant could query the sales trend of other products to analyse what is happening to the sales.

These are issues that need to be taken into account while determining the functional split.

Note: we need to determine the business benefits and technical feasibility of using data mart.


Identify User Access Tool Requirements

For the user access tools that require the internal data structures we need data mart to support such tools. The data in such structures are outside the control of data warehouse but need to be populated and updated on regular basis.

There are some tools that populated directly from the source system but some can not. Therefore additional requirements outside the scope of the tool are needed to be identified for future.

Note: In order to ensure consistency of data across all access tools the data should not be directly populated from the data warehouse rather each tool must have its own data mart.

Identify Access Control Issues

There need to be privacy rules to ensure the data is accessed by the authorised users only. For example in data warehouse for retail baking institution ensure that all the accounts belong to the same legal entity. Privacy laws can force you to totally prevent access to information that is not owned by the specific bank.

Data mart allow us to build complete wall by physically separating data segments within the data warehouse. To avoid possible privacy problems the detailed data can be removed from the data warehouse.We can create data mart for each legal entity and load it via data warehouse, with detailed account data.

Designing Data Marts

The data marts should be designed as smaller version of starflake schema with in the data warehouse and should match to the database design of the data warehouse. This helps in maintaining control on database instances.

Designing Data Mart

The summaries are data marted in the same way as they would have been designed within the data warehouse. Summary tables helps to utilize all dimension data in the starflake schema.

Cost Of Data Marting

The following are the cost measures for Data marting:

  • Hardware and Software Cost

  • Network Access

  • Time Window Constraints

Hardware and Software Cost

Although the data marts are created on the same hardware even then they require some additional hardware and software.To handle the user queries there is need of additional processing power and disk storage. If the detailed data and the data mart exist within the data warehouse then we would face additional cost to store and manage replicated data.

Note: The data marting is more expensive than aggregations therefore it should be used as an additional strategy not as an alternative strategy.


Network Access

The data mart could be on different locations from the data warehouse so we should ensure that the LAN or WAN has the capacity to handle the data volumes being transferred within the data mart load process.


Time Window Constraints

The extent to which the data mart loading process will eat into the available time window will depend on the complexity of the transformations and the data volumes being shipped. Feasiblity of number of data mart depend on.

  • Network Capacity.

  • Time Window Available

  • Volume of data being transferred

  • Mechanisms being used to insert data into data mart

Data Warehousing - System Managers

Introduction

The system management is must for the successful implementation of data warehouse. In this chapter we will discuss the most important system managers such as following mentioned below.

  • System Configuration Manager

  • System Scheduling Manager

  • System Event Manager

  • System Database Manager

  • System Backup Recovery Manager

System Configuration Manager

  • The system configuration manager is responsible for the management of the setup and configuration of data warehouse.

  • The Structure of configuration manager varies from the operating system to operating system.

  • In unix structure of configuration manager varies from vendor to vendor.

  • Configuration manager have the single user interface.

  • The interface of configuration manager allow us to control of all aspects of the system.

Note: The most important configuration tool is the I/O manager.

System Scheduling Manager

The System Scheduling Manager is also responsible for the successful implementation of the data warehouse. The purpose of this scheduling manager is to schedule the ad hoc queries. Every operating system has its own scheduler with some form of batch control mechanism. Features of System Scheduling Manager are following.

  • Work across cluster or MPP boundaries.

  • Deal with international time differences.

  • Handle job failure.

  • Handle multiple queries.

  • Supports job priorities.

  • Restart or requeue the failed jobs.

  • Notify the user or a process when job is completed.

  • Maintain the job schedules across system outages.

  • Requeue jobs to other queues.

  • Support the stopping and starting of queues.

  • Log Queued jobs.

  • Deal with interqueue processing.

Note: The above are the evaluation parameters for evaluation of a good scheduler.

Some important jobs that the scheduler must be able to handle are as followed:

  • Daily and ad hoc query scheduling.

  • execution of regular report requirements.

  • Data load

  • Data Processing

  • Index creation

  • Backup

  • Aggregation creation

  • data transformation

Note: If the data warehouse is running on a cluster or MPP architecture, then the system scheduling manager must be capable of running across the architecture.

System Event Manager

The event manager is a kind of a software. The event manager manages the events that are defined on the data warehouse system. We cannot manage the data warehouse manually because the structure of data warehouse is very complex. Therefore we need a tool that automatically handle all the events without intervention of the user.

Note: The Event manager monitor the events occurrences and deal with them. the event manager also track the myriad of things that can go wrong on this complex data warehouse system.

Events

The question arises is What is an event? event is nothing but the action that are generated by the user or the system itself. It may be noted that the event is measurable, observable, occurrence of defined action.

The following are the common events that are required to be tracked.

  • hardware failure.

  • Running out of space on certain key disks.

  • A process dying.

  • A process returning an error.

  • CPU usage exceeding an 805 threshold.

  • Internal contention on database serialization points.

  • Buffer cache hit ratios exceeding or failure below threshold.

  • A table reaching to maximum of its size.

  • Excessive memory swapping.

  • A table failing to extend due to lack of space.

  • Disk exhibiting I/O bottlenecks.

  • Usage of temporary or sort area reaching a certain thresholds.

  • Any other database shared memory usage.

The most important thing about is that they should be capable of executing on their own. there event packages that defined the procedures for the predefined events. The code associated with each event is known as event handler. This code is executed whenever an event occurs.

System and Database Manager

System and Database manager are the two separate piece of software but they do the same job. The objective of these tools is to automate the certain processes and to simplify the execution of others. The Criteria of choosing the system and database manager are an abitlity to:

  • increase user's Quota.

  • assign and deassign role to the users.

  • assign and deassign the profiles to the users.

  • perform database space management

  • monitor and report on space usage.

  • tidy up fragmented and unused space.

  • add and expand the space.

  • add and remove users.

  • manage user password.

  • manage summary or temporary tables.

  • assign or deassign temporary space to and from the user.

  • reclaim the space form old or outofdate temporary tables.

  • manage error and trace logs.

  • to browse log and trace files.

  • redirect error or trace information.

  • switch on and off error and trace logging.

  • perform system space management.

  • monitor and report on space usage.

  • clean up old and unused file directories.

  • add or expand space.

System Backup Recovery Manager

The backup and recovery tool make it easy for operations and management staff to backup the data. It is worth noted that the system backup manager must be integrated with the schedule manager software being used. The important features that are required for the management of backups are following.

  • Scheduling

  • Backup data tracking

  • Database awareness.

The backup are taken only to protect the data against loss. Following are the important points to remember.

  • The backup software will keep some from of database of where and when the piece of data was backed up.

  • The backup recovery manager must have a good front end to that database.

  • The backup recovery software should be database aware.

  • Being aware of database the software then can be addressed in database terms, and will not perform backups that would not be viable.

Data Warehousing - Process Managers

Data Warehouse Load Manager

  • This Component performs the operations required to extract and load process.

  • The size and complexity of load manager varies between specific solutions from data warehouse to data warehouse.

Load Manager Architecture

The load manager does the following functions.

  • Extract the data from source system.

  • Fast Load the extracted data into temporary data store.

  • Perform simple transformations into structure similar to the one in the data warehouse.

Load Manager

Extract Data from Source

The data is extracted from the operational databases or the external information providers. Gateways is the application programs that are used to extract data. It is supported by underlying DBMS and allows client program to generate SQL to be executed at a server. Open Database Connection( ODBC), Java Database Connection (JDBC), are examples of gateway.

Fast Load

  • In order to minimize the total load window the data need to be loaded into the warehouse in the fastest possible time.

  • The transformations affects the speed of data processing.

  • It is more effective to load the data into relational database prior to applying transformations and checks.

  • Gateway technology proves to be not suitable, since they tend not be performant when large data volumes are involved.

Simple Transformations

While loading it may be required to perform simple transformations. After this has been completed we are in position to do the complex checks. Suppose we are loading the EPOS sales transaction we need to perform the following checks.

  • Strip out all the columns that are not required within the warehouse.

  • Convert all the values to required data types.

Warehouse Manager

  • Warehouse manager is responsible for the warehouse management process.

  • The warehouse manager consist of third party system software, C programs and shell scripts.

  • The size and complexity of warehouse manager varies between specific solutions.

Warehouse Manager Architecture

The warehouse manager includes the following.

  • The Controlling process

  • Stored procedures or C with SQL

  • Backup/Recovery tool

  • SQL Scripts

Warehouse Manager

Operations Performed by Warehouse Manager

  • Warehouse manager analyses the data to perform consistency and referential integrity checks.

  • Creates the indexes, business views, partition views against the base data.

  • Generates the new aggregations and also updates the existing aggregation

  • Generates the normalizations.

  • Warehouse manager Warehouse manager transforms and merge the source data into the temporary store into the published data warehouse.

  • Backup the data in the data warehouse.

  • Warehouse Manager archives the data that has reached the end of its captured life.

Note: Warehouse Manager also analyses query profiles to determine index and aggregations are appropriate.

Query Manager

  • Query Manager is responsible for directing the queries to the suitable tables.

  • By directing the queries to appropriate table the query request and response process is speed up.

  • Query Manager is responsible for scheduling the execution of the queries posed by the user.

Query Manager Architecture

Query Manager includes the following.

  • The query redirection via C tool or RDBMS.

  • Stored procedures.

  • Query Management tool.

  • Query Scheduling via C tool or RDBMS.

  • Query Scheduling via third party Software.

Operations Performed by Query Manager

  • Query Manager direct to the appropriate tables.

  • Query Manager schedule the execution of the queries posed by the end user.

  • Query Manager stores query profiles to allow the warehouse manager to determine which indexes and aggregations are appropriate.

Data Warehousing - Security

Introduction

The objective data warehouse is to allow large amount of data to be easily accessible by the users. Hence allowing user to extract the information about the business as a whole. But we know that there could be some security restrictions applied on the data which can prove an obstacle for accessing the information. If the analyst has the restricted view of data then it is impossible to capture a complete picture of the trends within the business.

The data from each analyst can be summarised and passed onto management where the different summarise can be created. As the aggregations of summaries cannot be same as that of aggregation as a whole so It is possible to miss some information trends in the data unless someone is analysing the data as a whole.

Requirements

Adding the security will affect the performance of the data warehouse, therefore it is worth determining the security requirements early as possible. Adding the security after the data warehouse has gone live, is very difficult.

During the design phase of data warehouse we should keep in mind that what data sources may be added later and what would be the impact of adding those data sources. We should consider the following possibilities during the design phase.

  • Whether the new data sources will require new security and/or audit restrictions to be implemented?

  • Whether the new users added who have restricted access to data that is already generally available?

This situation arises when the future users and the data sources are not well known. In such a situation we need to use the knowledge of business and the objective of data warehouse to know likely requirements.

Factor to Consider for Security requirements

The following are the parts that are affected by the security hence it is worth consider these factors.

  • User Access

  • Data Load

  • Data Movement

  • Query Generation

User Access

We need to classify the data first and then the users by what data they can access.In other word the users are classified according to the data, they can access.

Data Classification

The following are the two approaches that can be used to classify the data:

  • The data can be classified according to its sensitivity. The highly sensitive data is classified as highly restricted and less sensitive data is classified as less restrictive.

  • The data can also be classified according to the job function. This restriction allows only the specific users to view particular data. In this we restrict the users to view only that that in which they are interested and are responsible for.

There are some issues in the second approach. To understand let's have an example, suppose you are building the data warehouse for a bank. suppose further that data being stored in the data warehouse is the transaction data for all the accounts. The question here is who is allowed to see the transaction data. The solution lies in classifying the data according to the function.

User classification

    The following are the approaches that can be used to classify the users.

  • The users can be classified as per the hierarchy of users in an organisation i.e. users can be classified by department, section, group, and so on.

  • The user can also be classified according to their role, with people grouped across departments based on their role.

Classification on basis of Department

Let's have an example of a data warehouse where the users are from sales and marketing department. we can design the security by topdown company view, with access centered around the different departments. But they could be some restrictions on users at different level. This structure is shown in the following diagram.

User Access Hierarchy

But if each department accesses the different data then we should design the security access for each department separately. This can be achieved by the departmental data marts. Since these data marts are separated from the data warehouse hence we can enforce the separate security restrictions on each data mart. This approach is shown in the following figure.

using data mart enforce restrictions on access to data

Classification on basis of Role

If the data is generally available to all the departments.The it is worth to follow the role access hierarchy. In other words if the data is generally accessed by all the departments the apply the security restrictions as per the role of the user. The role accesshierarchy is shown in the following figure.

Role Access Hierarchy

Audit Requirements

The auditing is a subset of security. The auditing is a costly activity therefore it is worth understanding the audit requirements and reason for each audit requirement. The auditing can cause the heavy overheads on the system. To complete auditing in time we require the more hardware therefore it is recommended that where possible, auditing should be switch off. Audit requirements can be categorized into the following:

  • Connections

  • Disconnections

  • Data access

  • Data change

Note: For each of the above mentioned categories it is necessary to audit success, failure or both. From the perspective of security reasons the auditing of failures are very important. The auditing of failure are important because they can highlight the unauthorised or fraudulent access.

Network Requirements

The Network security is as important as other securities. We can not ignore the network security requirement. We need to consider the following issues.

  • Is it necessary to encrypt data before transferring it to the data warehouse machine?

  • Are there restrictions on which network routes the data can take?

These restrictions need to be considered carefully. Following are the points to remember.

  • The process of encryption and decryption will increase the overheads.It would require more processing power and processing time.

  • The cost of encryption can be high if the system is already a loaded system because the encryption is borne by the source system.

Data Movement

There exist potential security implications while moving the data. Suppose we need to transfer some restricted data as a flat file to be loaded. When the data is loaded into the data warehouse the following questions are raised?

  • Where is the flat file stored?

  • Who has access to that disk space?

If we talk about the backup of these flat files the following questions are raised?

  • Do you backup encrypted or decrypted versions?

  • Do these backup needs to be made to special tapes that are stored separately?

  • Who has access to these tapes?

Some other form of data movement like query result sets also need to be considered. The question here are raised when creating the temporary table are as follows.

  • Where is that temporary table to be held?

  • How do you make such table visible?

We should avoid the accidental flouting of security restrictions. If a user with access to the restricted data can generate accessible temporary tables, data can be made visible to nonauthorized users. We can overcome it by having separate temporary area for users with access to restricted data.

Documentation

The audit and security requirements need to be properly documented. This will be treated as part of justification. This document can contain all the information gathered on the following.

  • Data classification

  • User classification

  • Network requirements

  • Data movement and storage requirements

  • All auditable actions

Impact of Security on Design

The security affects the application code and the development timescales. The Security affects the following.

  • Application development

  • Database design

  • Testing

Application Development

The security affect the overall application development and it also affect the design of the important components of the data warehouse such as load manager, warehouse manager and the query manager. The load manager may require checking code to filter record and place them in different locations. The more transformation rule may also be required to hide certain data . Also there may be requirement of extra metadata to handle any extra objects.

To create and maintain the extra vies the warehouse manager may require extra code to enforce the security. There may be the requirement of the extra checks coded into the data warehouse to prevent it from being fooled into moving data into location where it should not be available. The query manager require the changes to handle any access restrictions. The query manager will need to be aware of all extra views and aggregations.

Database design

The database layout is also affected because when the security is added there is increase in number of views and tables. Adding security adds the size to the database and hence increase the complexity of the database design and management. it will also add complexity to the backup management and recovery plan.

Testing

The testing of the data warehouse is very complex and a lengthy process. Adding security to the data warehouse also affect the testing time complexity. It affects the testing in the following two ways.

  • It will increase the time required for integration and system testing.

  • There is added functionality to be tested which will cause increase in the size of the testing suite.

Data Warehousing - Backup

Introduction

There exist large volume of data into the data warehouse and the data warehouse system is very complex hence it becomes important to have backup of all the data which is available for the recovery in future as per the requirement. In this chapter I will discuss the issues on designing backup strategy.

Backup Terminologies

Before proceeding further we should know some of the backup terminologies discussed below.

  • Complete backup - In complete backup the entire database is backed up at the same time. This backup includes all the database files, control files and journal files.

  • Partial backup - Partial backup is not the complete backup of database. Partial backup are very useful in large databases because they allow a strategy whereby various parts of the database are backed up in a round robin fashion on daybyday basis, so that the whole database is backed up effectively once a week.

  • Cold backup - Cold backup is taken while the database is completely shut down. In multiinstance environment all the instances should be shut down.

  • Hot backup - The hot backup is take when the database engine is up and running. Hot backup requirements that need to be considered varies from RDBMS to RDBMS. Hot backups are extremely useful.

  • Online backup - It is same as the hot backup.

Hardware Backup

It is important to decide which hardware to use for the backup.We have to make the upper bound on the speed at which backup is can be processed. the speed of processing backup and restore depends not only on the hardware being use rather it also depends upon the how hardware is connected, bandwidth of the network, backup software and speed of server's I/O system. Here I will discuss about some of the hardware choices that are available and their pros and cons. These choices are as follows.

  • Tape Technology

  • Disk Backups

Tape Technology

The tape choice can be categorized into the following.

  • Tape media

  • Standalone tape drives

  • Tape stackers

  • Tape silos

Tape Media

There exists several varieties of tape media. The some tape media standard are listed in the table below:

Tape MediaCapacityI/O rates
DLT 40 GB 3 MB/s
3490e 1.6 GB3 MB/s
8 mm14 GB1 MB/s

Other factors that need to be considered are following:

  • Reliability of the tape medium.

  • Cost of tape medium per unit.

  • scalability.

  • Cost of upgrades to tape system.

  • Cost of tape medium per unit.

  • Shelf life of tape medium.

Standalone tape drives

The tape drives can be connected in the following ways.

  • Direct to the server.

  • As as networkavailable devices.

  • Remotely to other machine.

Issues of connecting the tape drives

  • Suppose the server is the 48node MPP machine so which node do you connect the tape drive, how do you spread them over the server nodes to get the optimal performance with least disruption of the server and least internal I/O latency?

  • Connecting the tape drive as a network available device require the network to be up to the job of the huge data transfer rates needed. make sure that sufficient bandwidth is available during the time you require it.

  • Connecting the tape drives remotely also require the high bandwidth.

Tape Stackers

The method of loading the multiple tapes into a single tape drive is known as tape stackers. The stacker dismounts the current tape when it has finished with it and load the next tape hence only one tape is available data a time to be accessed.The price and the capabilities may vary but the common ability is that they can perform unattended backups.

Tape Silos

The tape silos provide the large store capacities.Tape silos can store and manage the thousands of tapes. The tape silos can integrate the multiple tape drives. They have the software and hardware to label and store the tapes they store. It is very common for the silo to be connected remotely over a network or a dedicated link.We should ensure that the bandwidth of that connection is up to the job.

Other Technologies

The technologies other than the tape are mentioned below.

  • Disk Backups

  • Optical jukeboxes

Disk Backups

Methods of disk backups are listed below.

  • Disk-to-disk backups

  • Mirror breaking

These methods are used in OLTP system. These methods minimize the database downtime and maximize the availability.

Disk-to-disk backups

In this kind of backup the backup is taken on to disk rather than to tape. Reasons for doing Disktodisk backups are.

  • Speed of initial backups

  • Speed of restore

Backing up the data from Disk to disk is much faster than to the tape. However it is the intermediate step of backup later the data is backed up on the tape. The other advantage of Disk to disk backups is that it gives you the online copy of the latest backup.

Mirror Breaking

The idea is to have disks mirrored for resilience during the working day. When back is required one of the mirror sets can be broken out. This technique is variat of Disktodisk backups.

Note: The database may need to be shutdown to guarantee the consistency of the backup.

Optical jukeboxes

Optical jukeboxes allow the data to be stored near line. This technique allow large number of optical disks to be managed in same way as a tape stacker or tape silo. The drawback of this technique is that it is slow write speed than disks. But the optical media provide the long life and reliability make them good choice of medium of archiving.

Software Backups

There are software tools available which helps in backup process. These software tools come as a package.These tools not only take backup in fact they effectively manage and control the backup strategies. There are many software packages available in the market .Some of them are here listed in the following table.

Package Name Vendor
Networker Legato
ADSM IBM
Epoch Epoch Systems
Omniback IIHP
Alexandria Sequent

Criteria For Choosing Software Packages

The criteria of choosing the best software package is listed below:

  • How scalable is the product as tape drives are added?

  • Does the package have client server option, or must it run on database server itself?

  • Will it work in cluster and MPP environments?

  • What degree of parallelism is required?

  • What platforms are supported by the package?

  • Does package support easy access to information about tape contents?

  • Is the package database aware?

  • What tape drive and tape media are supported by package?

Data Warehousing - Tuning

Introduction

The data warehouse evolves throughout the period of time and the it is unpredictable that what query the user is going to be produced in future. Therefore it becomes more difficult to tune data warehouse system. In this chapter we will discuss about how to tune the different aspects of data warehouse such as performance, data load, queries ect.

Difficulties in Data Warehouse Tuning

Here is the list of difficulties that can occur while tuning the data warehouse.

  • The data warehouse never remain constant throughout the period of time.

  • It is very difficult to predict that what query the user is going to produce in future.

  • The need of the business also changes with time.

  • The users and their profile never remains the same with time.

  • The user can switch from one group to another.

  • the data load on the warehouse also changes with time.

Note: It is very important to have the complete knowledge of data warehouse.

Performance Assessment

Here is the list of objective measures of performance.

  • Average query response time

  • Scan rates.

  • Time used per day query.

  • Memory usage per process.

  • I/O throughput rates

Following are the points to be remembered.

  • It is necessary to specify the measures in service level agreement(SLA).

  • It is of no use to trying to tune response time if they are already better than those required.

  • It is essential to have realistic expectations while performance assessment.

  • It is also essential that the users have the feasible expectations.

  • To hide the complexity of the system from the user the aggregations and views should be used.

  • It is also possible that the user can write a query you had not tuned for.

Data Load Tuning

  • Data Load is very critical part of overnight processing.

  • Nothing else can run until data load is complete.

  • This is the entry point into the system.

Note: If there is delay in transferring the data or in arrival of data then the entire system is effected badly. Therefore it is very important to tune the data load first.

There are various approaches of tuning data load that are discussed below:

  • The very common approach is to insert data using the SQL Layer. In this approach the normal checks and constraints need to be performed. When the data is inserted into the table the code will run to check is there enough space available to insert the data. if the sufficient space is not available then more space may have to be allocated to these tables. These checks take time to perform and are costly to CPU. But pack the data tightly by making maximal use of space.

  • The second approach is to bypass all these checks and constraints and place the data directly into preformatted blocks. These blocks are later written to the database. It is faster than the first approach but it can work only with the whole blocks of data. This can lead to some space wastage.

  • The third approach is that while loading the data into the table that already contains the table, we can either maintain the indexes.

  • The fourth approach says that to load the data in tables that already contains the data, drop the indexes & recreate them when the data load is complete. Out of third and fourth, which approach is better depends on how much data is already loaded and how many indexes need to be rebuilt.

Integrity Checks

The integrity checking highly affects the performance of the load

Following are the points to be remembered.

  • The integrity checks need to be limited because processing required can be heavy.

  • The integrity checks should be applied on the source system to avoid performance degrade of data load.

Tuning Queries

We have two kinds of queries in data warehouse:

  • Fixed Queries

  • Ad hoc Queries

Fixed Queries

The fixed queries are well defined. The following are the examples of fixed queries.

  • regular reports

  • Canned queries

  • Common aggregations

Tuning the fixed queries in data warehouses is same as in relational database systems. the only difference is that the amount of data to be queries may be different. It is good to store the most successful execution plan while testing the fixed queries. Storing these executing plan will allow us to spot changing data size and data skew as this will cause the execution plan to change.

Note: We cannot do more on fact table but while dealing with the dimension table or the aggregations, the usual collection of SQL tweaking, storage mechanism and access methods can be used to tune these queries.

Ad hoc Queries

To know the ad hoc queries it is important to know the ad hoc users of the data warehouse. Here is the list of points that need to understand about the users of the data warehouse:

  • The number of users in the group.

  • Whether they use ad hoc queries at regular interval of time.

  • Whether they use ad hoc queries frequently.

  • whether they use ad hoc queries occasionally at unknown intervals.

  • The maximum size of query they tend to run

  • The average size of query they tend to run.

  • Whether they require drill-down access to the base data.

  • The elapsed login time per day

  • The peak time of daily usage

  • The number of queries they run per peak hour.

Following are the points to be remembered.

  • It is important to track the users profiles and identify the queries that are run on regular basis.

  • It is also important to identify tuning performed does not affect the performance.

  • Identify the similar and ad hoc queries that are frequently run.

  • If these queries are identified then the database will change and new indexes can be added for those queries.

  • If these queries are identified then new aggregations can be created specifically for those queries that would result in their efficient execution.


Data Warehousing - Testing

Introduction

Testing is very important for data warehouse systems to make them work correctly and efficiently. There are three basic level of testing that are listed below:

  • Unit Testing

  • Integration Testing

  • System testing

Unit Testing

  • In the Unit Testing each component is separately tested.

  • In this kind of testing each module i.e. procedure, program, SQL Script, Unix shell is tested.

  • This tested is performed by the developer.

Integration Testing

  • In this kind of testing the various modules of the application are brought together and then tested against number of inputs.

  • It is performed to test whether the various components do well after integration.

Sustem Testing

  • In this kind of testing the whole data warehouse application is tested together.

  • The purpose of this testing is to check whether the entire system work correctly together or not.

  • This testing is performed by the testing team.

  • Since the size of the whole data warehouse is very large so it is usually possible to perform minimal system testing before the test plan proper can be enacted.

Test Schedule

  • First of all the Test Schedule is created in process of development of Test Plan.

  • In this we predict the estimated time required for the testing of entire data warehouse system.

Difficulties in Scheduling the Testing

  • There are different methodologies available but none of them is perfect because the data warehouse is very complex and large. Also the data warehouse system is evolving in nature.

  • A simple problem may have large size of query which can take a day or more to complete i.e. the query does not complete in desired time scale.

  • There may be the hardware failure such as losing a disk, or the human error such as accidentally deleting the table or overwriting a large table.

Note: Due to the above mentioned difficulties it is recommended that always double the amount of time you would normally allow for testing.

Testing the backup recovery

This is very important testing that need to be performed. Here is the list of scenarios for which this testing is needed.

  • Media failure.

  • Loss or damage of table space or data file

  • Loss or damage of redo log file.

  • Loss or damage of control file

  • Instance failure.

  • Loss or damage of archive file.

  • Loss or damage of table.

  • Failure during data failure.

Testing Operational Environment

There are number of aspects that need to be tested. These aspects are listed below.

  • Security - A separate security document is required for security testing. This document contain the list of disallowed operations and devising test for each.

  • Scheduler - Scheduling software is required to control the daily operations of data warehouse. This need to be tested during the system testing. The scheduling software require interface with the data warehouse, which will need the scheduler to control the overnight processing and the management of aggregations.

  • Disk Configuration. - The Disk configuration also need to be tested to identify the I/O bottlenecks. The test should be performed with multiple times with different settings.

  • Management Tools. - It is needed to test all the management tools during system testing. Here is the list of tools that need to be tested.

    • Event manager

    • system Manager.

    • Database Manager.

    • Configuration Manager

    • Backup recovery manager.

Testing the Database

There are three set of tests that are listed below:

  • Testing the database manager and monitoring tools. - To test the database manager and the monitoring tools they should be used in the creation, running and management of test database.

  • Testing database features. - Here is the list of features that we have to test:

    • Querying in parallel

    • Create index in parallel

    • Data load in parallel

  • Testing database performance. - Query execution plays a very important role in data warehouse performance measures. There are set of fixed queries that need to be run regularly and they should be tested. To test ad hoc queries one should go through the user requirement document and understand the business completely. Take the time to test the most awkward queries that the business is likely to ask against different index and aggregation strategies.

Testing The Application

  • All the managers should be integrated correctly and work in order to ensure that the end-to-end load, index, aggregate and queries work as per the expectations.

  • Each function of each manager should work in correct manner.

  • It is also necessary to test the application over a period of time.

  • The week-end and month-end task should also be tested.

Logistic of the Test

There is a question that What you are really testing? The answer to this question is that you are testing a suite of data warehouse application code.

The aim of system test is to test all of the following areas.

  • Scheduling Software

  • Day-to Day operational procedures.

  • Backup recovery strategy.

  • Management and scheduling tools.

  • Overnight processing

  • Query Performance

Note: The most important point is to test the scalability. Failure to do so will leave us a system design that does not work when the system grow.

Data Warehousing - Future Aspects

Following are the future aspects of Data Warehousing.

  • As we have seen that the size of the open database has grown approximately double the magnitude in last few years. This change in magnitude is of greater significance.

  • As the size of the databases grow , the estimates of what constitutes a very large database continues to grow.

  • The Hardware and software that are available today do not allow to keep a large amount of data online. For example a Telco call record require 10TB of data to be kept online which is just a size of one month record. If It require to keep record of sales, marketing customer, employee etc. then the size will be more than 100 TB.

  • The record not only contain the textual information but also contain some multimedia data. Multimedia data cannot be easily manipulated as text data. Searching the multimedia data is not an easy task whereas the textual information can be retrieved by the relational software available today.

  • Apart from size planning, building and running ever-larger data warehouse systems are very complex. As the number of users increases the size of the data warehouse also increases. These users will also require to access to the system.

  • With growth of internet there is requirement of users to access data online.

Hence the Future shape of data warehouse will be very different from what is being created today.

Data Warehousing - Interview Questions

Dear readers, these Data Warehousing Interview Questions have been designed especially to get you acquainted with the nature of questions you may encounter during your interview for the subject of Data Warehousing. As per my experience, good interviewers hardly planned to ask any particular question during your interview, normally questions start with some basic concept of the subject and later they continue based on further discussion and what you answer:

Q: Define Data Warehouse?

A: Data warehouse is Subject Oriented, Integrated, Time-Variant and Nonvolatile collection of data that support management's decision making process.

Q: What does the subject oriented data warehouse signifies?

A: Subject oriented signifies that the data warehouse stores the information around a particular subject such as product, customer, sales etc.

Q: List any five applications of Data Warehouse?

A: Some applications include Financial services, Banking Services, Customer goods, Retail Sectors, Controlled Manufacturing.

Q: What does OLAP and OLTP stand for?

A: OLAP is acronym of Online Analytical Processing and OLAP is acronym of Online Transactional Processing

Q: What is the very basic difference between data warehouse and Operational Databases?

A: Data warehouse contains the historical information that is made available for analysis of the business whereas the Operational database contains the current information that is required to run the business.

Q: List the Schema that Data Warehouse System implements ?

A: Data Warehouse can implement Star Schema, Snowflake Schema or the Fact Constellation Schema

Q: What is Data Warehousing?

A: Data Warehousing is the process of constructing and using the data warehouse.

Q: List the process that are involved in Data Warehousing?

A: Data Warehousing involves data cleaning, data integration and data consolidations.

Q: List the functions of data warehouse tools and utilities?

A: The functions performed by Data warehouse tool and utilities are Data Extraction, Data Cleaning, Data Transformation, Data Loading and Refreshing

Q: What do you mean by Data Extraction?

A: Data Extraction means gathering the data from multiple heterogeneous sources.

Q: Define Metadata?

A: Metadata is simply defined as data about data. In other words we can say that metadata is the summarized data that lead us to the detailed data.

Q: What does MetaData Respiratory contains?

A: Metadata respiratory contains Definition of data warehouse, Business Metadata, Operational Metadata, Data for mapping from operational environment to data warehouse and the Alorithms for summarization

Q: How does a Data Cube help?

A: Data cube help us to represent the data in multiple dimensions. The data cube is defined by dimensions and facts.

Q: Define Dimension?

A: The dimensions are the entities with respect to which an enterprise keep the records.

Q: Explain Data mart?

A: Data mart contains the subset of organisation-wide data. This subset of data is valuable to specific group of an organisation. in other words we can say that data mart contains only that data which is specific to a particular group.

Q: What is Virtual Warehouse?

A: The view over a operational data warehouse is known as virtual warehouse.

Q: List the phases involved in Data warehouse delivery Process?

A: The stages are IT strategy, Education, Business Case Analysis, technical Blueprint, Build the version, History Load, Ad hoc query,Requirement Evolution, Automation, Extending Scope.

Q: Explain Load Manager?

A: This Component performs the operations required to extract and load process. The size and complexity of load manager varies between specific solutions from data warehouse to data warehouse.

Q: Define the function of Load Manager?

A: Extract the data from source system.Fast Load the extracted data into temporary data store.Perform simple transformations into structure similar to the one in the data warehouse.

Q: Explain Warehouse Manager?

A: Warehouse manager is responsible for the warehouse management process.The warehouse manager consist of third party system software, C programs and shell scripts.The size and complexity of warehouse manager varies between specific solutions.

Q: Define functions of Warehouse Manager?

A: The Warehouse Manager performs consistency and referential integrity checks, Creates the indexes, business views, partition views against the base data, transforms and merge the source data into the temporary store into the published data warehouse, Backup the data in the data warehouse and archives the data that has reached the end of its captured life.

Q: What is Summary Information?

A: Summary Information is the area in data warehouse where the predefined aggregations are kept.

Q: What does the Query Manager responsible for?

A: Query Manager is responsible for directing the queries to the suitable tables.

Q: List the types of OLAP server?

A: There are four types of OLAP Server namely Relational OLAP, Multidimensional OLAP, Hybrid OLAP, Specialized SQL Servers

Q: Which one is more faster Multidimensional OLAP or Relational OLAP?

A: Multidimensional OLAP is faster than the Relational OLAP

Q: List the functions performed by OLAP?

A: The functions such as roll-up, drill-down, slice, dice, and pivot are performed by OLAP

Q: How many dimensions are selected in Slice operation?

A: Only one dimension is selected for the slice operation.

Q: How many dimensions are selected in dice operation?

A: For dice operation two or more dimensions are selected for a given cube.

Q: How many fact tables are there in Star Schema?

A: There is only one fact table in Star Schema.

Q: What is Normalization?

A: The normalization split up the data into additional tables.

Q: Out of Star Schema and Snowflake Schema, the dimension table is normalised?

A: The snowflake schema uses the concept of normalization.

Q: What is the benefit of Normalization?

A: Normalization helps to reduce the data redundancy.

Q: Which language is used for defining Schema Definition

A: Data Mining Query Language (DMQL) id used for Schema Definition.

Q: What language is the base of DMQL

A: DMQL is based on Structured Query Language (SQL)

Q: What are the reasons for partitioning?

A: Partitioning is done for various reasons such as easy management, to assist backup recovery, to enhance performance.

Q: What kind of costs are involved in Data Marting?

A: Data Marting involves Hardware & Software cost, Network access cost and Time cost.

What is Next?

Further, you can go through your past assignments you have done with the subject and make sure you are able to speak confidently on them. If you are fresher then interviewer does not expect you will answer very complex questions, rather you have to make your basics concepts very strong.

Second it really doesn't matter much if you could not answer few questions but it matters that whatever you answered, you must have answered with confidence. So just feel confident during your interview. We at tutorialspoint wish you best luck to have a good interviewer and all the very best for your future endeavor. Cheers :-)



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