What are the types of OLAP Servers?

Data MiningDatabaseData Structure

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 servers which are as follows −

ROLAP

ROLAP stands for Relational OLAP. It can store the data based on the already familiar relational DBMS technology. In this case, data and the related aggregations are stored in RDBMS, and OLAP middleware is used to implement handling and exploration of data cubes.

This architecture targets the optimization of the RDBMS back end and supports additional tools and services including data cube navigation logic. Because of the use of the RDBMS back end, the main benefit of ROLAP is scalability in managing large data volumes.

MOLAP

MOLAP stands for Multidimensional OLAP. It facilitates tuples as the data storage unit. The MOLAP facilitates a dedicated n-dimensional array storage engine and OLAP middleware to handle data. Thus, OLAP queries are performed through direct addressing to the related multidimensional views (data cubes).

This architecture focuses on pre-calculation of the transactional data into the aggregations, which results in fast query execution performance. More specifically, MOLAP pre-calculates and stores aggregated measures at every hierarchy level at load time, and stores and indexes these values for immediate retrieval.

The full pre-calculation requires a substantial amount of overhead, both in processing time and in storage space. For sparse data, MOLAP uses sparse matrix compression algorithms to improve storage utilization, and thus in general is characterized by a smaller on-disk size of data in comparison with data stored in RDBMS.

HOLAP

HOLAP stands for Hybrid OLAP. It can produce a tradeoff between ROLAP’s scalability and MOLAP’s query implementation, some commercial OLAP servers are based on the HOLAP approach. In this method, the user decides which portion of the information to save in the MOLAP and which in the ROLAP. For instance, often the low-level data are stored using a relational database, while higher-level data, such as aggregations, are stored in a separate MOLAP.

raja
Published on 22-Nov-2021 07:49:00

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