Parallel Technology to Enhance Data Warehousing
By Priyanka Akhouri
Mumbai, May 26, 2008

The term data warehousing generally refers to the combination of many different databases across an entire enterprise. End users are still caught up in their acceptance of data warehousing because they do not understand how it applies to their business and everyday jobs. There's no point in gathering data when you cannot access the data and make decisions based on that data to create a strategic business improvement.

Companies have become reluctant to invest in data warehouse products as the implementation is rather more expensive than their monetary capacity. And because of the complexity with regards to initial installment many companies don't have enough support of the employees either.

The problem arises when companies fail to do proper analysis of a data warehouse project. As highlighted by Sanjay Mehta, CEO of MAIA Intelligence, "It's challenging for companies to keep their data warehouses in tune with their production units. Companies need to properly analyze a data warehouse project before paying for it. By not doing detailed requirements analysis, a company sets up for failure. This, thereby results in wastage of time, money, and the careers of its employees."

So what's the solution here for enterprises?

Parallel technology is one such option for them. Parallel database technology makes it possible to process very large databases for data-driven decision support. According to Suresh A. Shanmugam, national head (IT) of Business Information Technology Solutions (BITS) and CIO of Mahindra & Mahindra Financial Services, "Data warehouses generally employ parallel technology to perform warehouse loading and query functions."

Organizations today are progressively adopting parallel technology in their databases. Issues such as the need for increased speed or performance for large databases, the need for scalability, and the need for high availability are factors that are driving the use of parallel processing in database environments.

According to Dennis Samuel, senior VP (India & SE Asia) of Teradata, "Parallel technology for database processing has been one of the most sweeping changes to impact the IT industry in last decade. It has transformed task workers into knowledge workers."

So how does it work?

In parallel technology, table compression settings are usually configured and managed by database administrators or architects, with little involvement of developers. The data tagged for compression is stored in the symbol table and not in the database rows. As data tagged for compression appears in a database row, the row stores a pointer to the relevant data in the symbol table, instead of the data itself. In parallel technology general demand on disk space can grow quickly allowing faster recovery of planned and distributed information.

Shan said, "It allows saving space by eliminating redundant copies of data in the table. This enables the developers to access a table regardless of whether the table is compressed or not. It has the ability to move table spaces for databases around the network."

Benefits of parallel technology in data warehouses:

Parallel technology purely speeds the retrieval and information required at all levels with the outflow which is required for online transactions. "It helps to capture centrally stored databases and distributed data. This in turn ensures that the information is static and ensures to take proposed timely decisions," stated Shan.

There are various types of Parallel technologies such as Complex Query Decomposition, Singular Trading databases, Intra-query parallelism, etc. These allow enterprises to run components separately and still reintegrate and restore highly desirable features in database.

Intra-query parallelism is very beneficial in Decision Support System (DSS) applications, which often have complex, long-running queries. "As DSS have become more widely used, database vendors have been increasing their support for intra-query parallelism. Parallel processing is necessary to provide timely results from complex, decision support database queries needed by managers in data intensive organizations," added Mehta.

"Parallel technology ensures more space, more speed, and better query performance as per the support systems requirements. It involves more volume of data stored in large databases," added Shan.

It helps in getting the database performance as fast as possible so that it can face various situations. In databases, parallel technology provides higher performance in storing the data, and performance in retrieve in the data.


Related Links:

BO To Support HP Neoview Data Warehouse Platform
IBM Unveils Dynamic Data Warehouse