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Data warehouses contain huge volumes of data. OLAP servers demand that decision support queries be acknowledged in the order of seconds. Thus, it is essential for data warehouse systems to provide highly effective cube computation techniques, access techniques, and query processing techniques.Efficient Computation of Data CubesAt the core of multidimensional data analysis is the efficient computation of aggregations across many sets of dimensions. In SQL terms, these aggregations are referred to as group-by’s. Each group-by can be represented by a cuboid, where the set of group-by’s forms a lattice of cuboids defining a data cube.There are three choices for data ... Read More
OLAP stands for On-Line Analytical Processing. OLAP is a categorization of software technology that authorizes analysts, managers, and executives to profit insight into information through quick, consistent, interactive access in a broad variety of possible views of information that has been transformed from raw data to reflect the real dimensionality of the enterprise as learned by the users.OLAP servers present business users with multidimensional data from data warehouses or data marts, without concerns regarding how or where the data are stored. The physical architecture and implementation of OLAP servers must consider data storage issues.There are three main types of OLAP ... Read More
Data Warehousing is a technique that is mainly used to collect and manage data from various sources to give the business a meaningful business insight. A data warehouse is specifically designed to support management decisions.In simple terms, a data warehouse refers to a database that is maintained separately from an organization’s operational databases. Data warehouse systems enables the integration of multiple application systems. They provide data processing by supporting a solid platform of consolidated, historical information for analysis.Data warehouses generalize and consolidate information in the multidimensional area. The construction of data warehouses includes data cleaning, data integration, and data transformation ... Read More
Practically, the scatterplots are well visualized on white background just like on white paper. If we want to create a scatterplot with white background and without gridlines using ggplot2 then we can apply classic theme to the plot.Check out the below given example to understand how it can be done.ExampleFollowing snippet creates a sample data frame −x
Data Warehouses usually have a three-level (tier) architecture that involves −The bottom tier is a warehouse database server that is relatively always a relational database system. Back-end tools and utilities are used to feed records into the bottom tier from operational databases or other external sources (including user profile data supported by external consultants).These tools and utilities implement data extraction, cleaning, and transformation (e.g., to merge the same data from multiple sources into a unified format), and load and refresh functions to update the data warehouse. The data are extracted using application program interfaces referred to as gateways.A gateway is ... Read More
If we have comma separated values that contains duplicate and unique values then we might want to find the number of unique values within each comma separated value. To find the unique values in comma separated strings stored in an R data frame column, we can use stri_extract_all_regex function of stringi package along with sapply function.Check out the below examples to understand how it can be done.Example 1Following snippet creates a sample data frame −x
A data warehouse can be built using three approaches −A top-down approachA bottom-up approachA combination of both approachesThe top-down approach starts with the complete design and planning. It is helpful in cases where the technology is sophisticated and familiar, and where the business issues that must be solved are clear and well-understood.The bottom-up approach starts with experiments and prototypes. This is beneficial in the beginning phase of business modeling and technology development. It enables an organisation to move forward at considerably less expense and to compute the advantage of the technology before creating significant commitments.In the combined approach, an organisation ... Read More
Data Warehousing is a technique that is mainly used to collect and manage data from various sources to give the business a meaningful business insight. A data warehouse is specifically designed to support management decisions.In simple terms, a data warehouse defines a database that is maintained independently from an organization’s operational databases. Data warehouse systems enable the integration of several application systems. They provide data processing by supporting a solid platform of consolidated, historical information for analysis.The technology of the Data warehouse includes data cleaning, data integration, and online analytical processing (OLAP), that is, analysis techniques with functionalities such as ... Read More
The major components of a data warehouse are as follows −Data Sources − Data sources define an electronic repository of records that includes data of interest for administration use or analytics. The mainframe of databases (e.g. IBM DB2, ISAM, Adabas, Teradata, etc.), client-server databases (e.g. Teradata, IBM DB2, Oracle database, Informix, Microsoft SQL Server, etc.), PC databases (e.g. Microsoft Access, Alpha Five), spreadsheets (e.g. Microsoft Excel) and any other electronic storage of data.Data Warehouse − The data warehouse is normally a relational database. It should be organized to hold data in a structure that best supports not only query and ... Read More
Data Warehousing is a technique that is mainly used to collect and manage data from various sources to give the business a meaningful business insight. A data warehouse is specifically designed to support management decisions.In simple terms, a data warehouse refers to a database that is maintained separately from an organization’s operational databases. Data warehouse systems enable for integration of several application systems. They provide data processing by supporting a solid platform of consolidated, historical information for analysis.Data Warehouse queries are complicated because they contain the computation of huge groups of information at summarized levels. It can require the use ... Read More