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Suppose an organization wants to implement an OLAP system. OLAP can be implemented in different ways, such as MOLAP, ROLAP, DOLAP and HOLAP. The organization demands extensive read operations on the database for real time decision making. In order to get real benefit from the business, data must be retrieved very fast to make decision making process quick and on time. Keeping in view the performance and storage cost, which option you will choose to implement OLAP and why? Justify your answer with solid reasoning.
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Cubes in a data warehouse are stored in three different modes. A relational storage model is called Relational Online Analytical Processing mode or ROLAP, while a Multidimensional Online Analytical processing mode is called MOLAP. When dimensions are stored in a combination of the two modes then it is known as Hybrid Online Analytical Processing mode or HOLAP.
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 OLAPs slicing and dicing functionality. The advantages of this model are 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.
i think HOLAP is suitable.
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Hybrid On-Line Analytic Processing (HOLAP) is a mixture of MOLAP and ROLAP technologies. For summary type query, HOLAP leverages cube technology for faster performance. When detail information is needed, it can drill through from the cube into the underlying relational database. Cubes stored as HOLAP are smaller than equivalent MOLAP cubes and respond quicker than ROLAP cubes for queries involving summary data. HOLAP storage is generally suitable for cubes that require rapid query response for summaries based on a large amount of base data.
In order to deliver the combined strengths of MOLAP and ROLAP technologies, HOLAP systems must comply with the following rules:
The chart below highlights advantages and disadvantages of HOLAP.
|Advantages||Combined advantages of both MOLAP and ROLAP (for a full list, look at the MOLAP and ROLAP sections).|
|Can combine the ROLAP technology for sparse regions and MOLAP for dense regions. Also ROLAP for storing the detailed data and MOLAP for higher-level summary data.|
|Disadvantages||Complex - HOLAP server must support both MOLAP and ROLAP engines and tools to combine both storage engines and operations.|
|Functionality overlap - between storage and optimization techniques in ROLAP and MOLAP engines.|
|Major Players||Express from Oracle, IBM DB 2 OLAP Server, Microsoft OLAP Services, Sagent Holos|
I will select & recommend HOLAP keeping in view the performance and storage cost.
No doubt ROLAP is used when you need real time OLAP but performance can be slow because ROLAP uses relational or extended relational DBMS. MOLAP is limited in the amount of data it can handle. because all calculations are pre built in cube. Molap also cost additional money to impliment. Whereas HOLAP addresses the shortcomings of MOLAP and ROLAP by combining the capabilities of both approaches. It offers higher scalability of Rolap and faster computation of Molap. HOLAP servers allows to store the large data volumes of detailed information. The aggregations are stored separately in MOLAP store.
Currently there are two technologies for the implementation of OLAPS servers, namely ROLAP and MOLAP.
MOLAP generally delivers better performance due to specialized indexing and storage optimizations. MOLAP also needs less storage space compared to ROLAP because the specialized storage typically includes compression techniques.
ROLAP is generally more scalable. However, large volume pre-processing is difficult to implement efficiently so it is frequently skipped. ROLAP query performance can therefore suffer.
But I prefer the HOLAP because it is a combination of relational OLAP (ROLAP) and multidimensional OLAP (MOLAP). HOLAP was developed to combine the greater data capacity of ROLAP with the superior processing capability of MOLAP. HOLAP can use varying combinations of ROLAP and OLAP technology. Typically it stores data in a both a relational database (RDB) and a multidimensional database (MDDB) and uses whichever one is best suited to the type of processing desired. The databases are used to store data in the most functional way. For heavy data processing, the data is more efficiently stored in a RDB, while for speculative processing, the data is more effectively stored in an MDDB. HOLAP users can choose to store the results of queries to the MDDB to save the effort of looking for the same data over and over which saves time. Although this technique - called "materializing cells" - improves performance, it takes a toll on storage. The user has to strike a balance between performance and storage demand to get the most out of HOLAP. Nevertheless, because it offers the best features of both MOLAP and ROLAP, HOLAP is increasingly preferred.
According to the situation, the HOLAP is best because HOLAP storage mode combines attributes of both MOLAP and ROLAP. Like MOLAP, HOLAP causes the aggregations of the partition to be stored in a multidimensional structure in an SQL Server Analysis Services instance. HOLAP does not cause a copy of the source data to be stored. For queries that access only summary data in the aggregations of a partition, HOLAP is the equivalent of MOLAP. Queries that access source data.. For example, if you want to drill down to an atomic cube cell for which there is no aggregation data must retrieve data from the relational database and will not be as fast as they would be if the source data were stored in the MOLAP structure. With HOLAP storage mode, users will typically experience substantial differences in query times depending upon whether the query can be resolved from cache or aggregations versus from the source data itself.