Vertipaq is an engine which was found in Power Pivot for Analysis server in SQL Server 2008 R2. Basically, it is used for data compression. Data retrieval and calculations are happening at much faster rate as it holds the compressed data in memory. It is also called xVelocity and it has been added to SQL Server 2012. As a result, it is delivering very huge performance improvements for data warehouse and business intelligence solutions.
Columnstore index using this compression algorithm technology.
This algorithm using following compression techniques.
Check this link, for detailed information about VertiPaq Compression techniques.
Business intelligence (BI) is a broad category of application programs and technologies for gathering, storing, analyzing, and providing access to data to help enterprise users make better business decisions. BI applications include the activities of decision support, query and reporting, online analytical processing (OLAP), statistical analysis, forecasting, and data mining. In particular to Microsoft, it supports 3 types of OLAP technology, they are
1. MOLAP: Multidimensional OLAP
2. ROLAP : Relational OLAP
3. HOLAP: Hybrid OLAP
I shall post these definitions in my further posts.
As promised in my previous post please find the definitions, PLUS some more information on the characteristic nature of the post title.
This is the traditional mode in OLAP analysis. In MOLAP data is stored in form of multidimensional cubes and not in relational databases. The advantages of this mode is that it provides excellent query performance and the cubes are built for fast data retrieval. All calculations are pre-generated when the cube is created and can be easily applied while querying data. The disadvantages of this model are that it can handle only a limited amount of data. Since all calculations have been pre-built when the cube was created, the cube cannot be derived from a large volume of data. This deficiency can be bypassed by including only summary level calculations while constructing the cube. This model also requires huge additional investment as cube technology is proprietary and the knowledge base may not exist in the organization.
The underlying data in this model is stored in relational databases. Since the data is stored in relational databases this model gives the appearance of traditional OLAP’s slicing and dicing functionality. The advantages of this model is it can handle a large amount of data and can leverage all the functionalities of the relational database. The disadvantages are that the performance is slow and each ROLAP report is an SQL query with all the limitations of the genre. It is also limited by SQL functionalities. ROLAP vendors have tried to mitigate this problem by building into the tool out-of-the-box complex functions as well as providing the users with an ability to define their own functions.
HOLAP technology tries to combine the strengths of the above two models. For summary type information HOLAP leverages cube technology and for drilling down into details it uses the ROLAP model.
Comparing the use of MOLAP, HOLAP and ROLAP
The type of storage medium impacts on cube processing time, cube storage and cube browsing speed. Some of the factors that affect MOLAP storage are:
Cube browsing is the fastest when using MOLAP. This is so even in cases where no aggregations have been done. The data is stored in a compressed multidimensional format and can be accessed quickly than in the relational database. Browsing is very slow in ROLAP about the same in HOLAP. Processing time is slower in ROLAP, especially at higher levels of aggregation.
MOLAP storage takes up more space than HOLAP as data is copied and at very low levels of aggregation it takes up more room than ROLAP. ROLAP takes almost no storage space as data is not duplicated. However ROALP aggregations take up more space than MOLAP or HOLAP aggregations.
All data is stored in the cube in MOLAP and data can be viewed even when the original data source is not available. In ROLAP data cannot be viewed unless connected to the data source.
MOLAP can handle very limited data only as all data is stored in the cube.