Recent research from The Data Warehousing Institute (TDWI) indicates an increasing adoption of columnar databases as the platform of choice for data warehousing and analytic applications due to their ability to deliver fast query response and efficiently compress and provide access to large amounts of detail data.

According to Philip Russom, senior manager, TDWI Research, a common complaint from BI professionals is that their data warehouse platform consists of a traditional DBMS and SMP hardware, both designed for transaction processing, not data warehousing. “Users are spending a significant amount of time and energy tweaking transactional platforms to get adequate performance for the type of queries and data volumes common in today’s data warehousing environments,” he said.

For this reason and others, he said, many BI professionals are seeking alternatives to the usual DBMS/SMP platform. “That search has led many of them to explore columnar databases, especially when they can be deployed in an MPP (Massively Parallel Processing) architecture,” Russom said in a recent TDWI webinar event entitled “Columnar Databases – Designed for Data Warehouse High Performance.”

In the TDWI webinar, Merkle’s Chief Technology Officer Christian Wright detailed how Merkle, one of the nation’s largest and fastest growing database marketing agencies, has reduced processing times by approximately 200 percent in the first phase of its next-generation consumer data integration platform implementation by using the column-based ParAccel Analytic Database. Merkle is using the ParAccel Analytic Database as part of an upgraded consumer data integration platform for its clients’ business to consumer database marketing systems. The platform is expected to grow to tens of terabytes over multiple implementation phases.

Wright agreed that columnar databases are “well suited” for computing environments such as Merkle’s massive consumer repositories. “Columnar works extremely well with redundant data, provides compelling data compression capabilities and helps reduce I/O demand further due to its selective data access,” Wright said. “In addition, the fact that the ParAccel Analytic Database runs in an MPP architecture ensures that this platform, which is the foundation of our business, will scale at a nearly linear rate, allowing for predictable system growth.”

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