Tuesday, July 26, 2016

DW - Multidimensional OLAP

Data warehousing depended on the performance of Meta data, OLTP and OLAP performance. In the data warehousing move toward, information is requested, processed, and merged continuously, so the information is readily available for direct querying OLAP and analysis at the warehouse. 
OLAP is a powerful analysis tool for forecasting, statistical computations, aggregations and involves more than just the multidimensional display of information. OLAP tools also must be able to extract and summarise requested data according to the needs of an end user, and there are two approaches for this data extraction that need to be discussed.

Types of OLAP Servers
Cubes in a data warehouse are stored in three different modes and we can have four types of OLAP servers which are given below:
MOLAP stands for Multidimensional Online Analytical Processing which is associated with Data Warehouse. MOLAP analytic is designed to allow analysis of data through the use of a multidimensional data model which requires the pre-computation data. It's based on optimized multidimensional array storage. MOLAP systems are more optimized for fast query performance and retrieval of summarised data because they map multidimensional views directly to data cube array structures.
Many MOLAP servers adopt a 2-level storage representation to handle dense and sparse data sets: denser sub cubes are identified and stored as array structures, whereas sparse sub cubes employ compression technology for efficient storage utilisation.

Advantages of MOLAP - Multidimensional OLAP is generally thought of as the traditional multidimensional database (MDDB) where database structure optimized for storing facts categorised along many dimensions. Because all the calculations have already been performed, multidimensional OLAP offers astounding response times. 
  1. Excellent Performance: A MOLAP cube is built for fast data retrieval, and is optimal for Slicing and Dicing operations.
  2. Perform complex calculations:  All calculations have been pre-generated whenever the cube is created. Hence, complex calculations are not only feasible, but they return quickly.
  3. Fast query performance due to optimized storage, multidimensional indexing and caching.
  4. Smaller on-disk size of data compared to data stored in relational database due to compression techniques.
  5. Array models provide natural indexing.
  6. Effective data extraction achieved through the pre-structuring of aggregated data. 
Disadvantages of MOLAP
  1. Limited in the amount of data it can handle: Because all calculations are performed when the cube is built, it is not possible to include a large amount of data in the cube itself.
  2. The data in cube cannot be derived from a large amount of data. Indeed, this is possible.
  3. Only summary-level information will be included in the cube itself.
  4. Some MOLAP methodologies introduce data redundancy.

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