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OLAP and ETL
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OLAP-Online Analytical Processing Techniques 

We create an advanced data analysis environment that supports decision making, business modeling, and operations research activities. We share four main characteristics:
  1. Use multidimensional data analysis techniques
  2. Provide advanced database support
  3. Provide easy-to-use end user interface
  4. Support client/server architecture

 

ROLAP-Relational Online Analytical Processing Techniques

We add the following extensions to traditional RDBMS - Relational Database Management System technology:
  1. Multidimensional data schema support within the RDBMS
  2. Data access language and query performance are optimized for multidimensional data
  3. Support for very large databases (VLDBs)

 

Conceptually, MDBMS end users visualize the stored data as a three-dimensional cube known as a data cube. Data cubes are created by extracting data from the operational databases or from the data warehouse. We utilize ETL - Extraction, Transformation and Load techniques for data acquisition.

ROLAP

MOLAP

Our ROLAP provides OLAP functionality by using relational databases and familiar relational query tools to store and analyze multidimensional data. This approach builds on existing relational technologies and represents a natural extension to all our clients who already use relational database management systems within their organizations.

 

Our MOLAP - Multidimensional Online Analytical Processing extends OLAP functionality to MDBMSs - Multidimensional Database Management Systems.  (An MDBMS uses special proprietary techniques to store data in matrix like n-dimensional arrays.) MOLAP's premise is that multidimensional databases are best suited to manage, store, and analyze multidimensional data.

 

 

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