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Considerations for maximizing analytical performance

Considerations for maximizing analytical performance
January 12
20:19 2015
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Considerations for maximizing analytical performance

There are three key non-functional requirements for the running business analytics that must be met: usability, affordability and fast performance. To maximize the analytic performance following are four important solutions:

  • On DB2 IBM Cognos BI running with BLU acceleration.
  • SAP business projects that are running on SAP HANA.
  • Oracle business intelligence enterprise edition that are running on oracle Exadata.
  • Microsoft business intelligence that are running on Microsoft SQL server 2012.

Here we will discuss these solutions in comparison form for better understanding.

IBM Cognos business intelligence:

There are two things that are notable with respect to Cognos business intelligence; the first thing is that it is the best for the products in the space. It will detect the version of data base that is in use and adjust that in appropriate way. The second feature is called as dynamic cubes. It is an optimization feature in the intelligence of business product.

SAP business objects:

It focused on the SAP HANA instead of general purpose product. The one major feature is that HANA allows is analytic view. HANA is enough fast for the calculation these on fly instead of storing materialized views. It implies that you will be able to get consistent performance.

Oracle business intelligence enterprise edition and Microsoft business intelligence:

Neither of these products contains particular features that can encourage high performance. At start-ups Microsoft targets business intelligence solution which indicates that the company is not targeting deployments of large scale and it does not perform or scale beyond these environments.

IBM DB2 with BLU Acceleration:

BLU affectively does followings that are not available previously

  • It introduces the use of actionable compressions and that is the main advantage of it. The techniques are being used for preserve order compressions. It means that without de-compressing data it predicts joins, grouping, count queries and evaluation. By the use of these techniques the rate of compression by DB2 with BLU acceleration is improved.
  • May be data is held in the columnar format. You can use columns and rows along with conventional indexes. Tables that are column organized do not contain secondary structure.
  • It advances technology of the Zone map achieved when IBM acquired Netezza. It can speed up the relevant quires significantly.
  • It uses process of parallel vector. It is cross-core parallelism. Within a single CPU you can parallelise across the cores as well as across sockets.
  • It is worth stating that at present partitions are not supported with BLU acceleration with DB2.

Microsoft SQL server:

The key feature is known as x-velocity. It offers memory optimized column store indexes. It means that only columns are needed to satisfy query that is need to be read. It will worsen the performance of some other quires as well. X velocity allows superior compression rates either by use of run-length encoding or dictionary based, it depends on the kind of data.

Oracle Exadata:

Oracle claimed that Exadata is based on columns but it is not like that. It used columns to allow better rate of compression than other way. In storage server the data is compressed. It claims to contain in-memory capabilities as well. Smart scans are the major is the major weakness of this technique. Thus it is best suited for static situations or environment where updating of data is acceptable on the periodic base. Workload management is another great issue. But Exadata brings hardware in to the table for the improvement of performance.

SAP HANA:

It is a kind of in-memory columnar database system that works only on x86 platforms. The whole database is designed to be held in memory. It has some benefits like the ability to support the analytic views that do not need views to be materialized.

Conclusion:

Neither Microsoft SQL server nor SAP HANA without or with their respective business intelligence product is in the same ballpark like oracle and IBM but the reasons are different. DB2 with BLU acceleration do not only offer better performance but also consistent performance as well.

Followings are some other items to maximize analytical performance:

Consolidated chart of accounts:

An effective design COA offers end users with appropriate level of details for management, external reporting and reduce the risk of offshoot hierarchies and duplicate.

Structure of legal entity:

Reorganizing of legal entities that experience minimal activity or are inactive can decrease the maintaining requirements and result in the effective savings.

Profit center and cost center standardization:

Consolidating profit centers and cost centers that are infrequently/inactive used or have less dollar values can streamline the structure, decrease cost of maintenance and increase performance.

Quality of data:

It is a deciding factor for integration cost and complexity as well as decrease risk of reconciliations.

Reporting management:

Have single source of trusted streamlines data the reporting process and enables organizations to make better performance to targets directly.

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