Dr. Jon A. Lind, InfoSphere Warehouse Product Manager, DB2 Warehousing Product Management,
Shamit Bagchi, DB2 & PureData Data Warehouse Product Marketing
Businesses are dealing with more and more data coming from a wide range of sources each and every day. At the same time, businesses are being challenged to react even faster to changing market conditions and customer demands. Being able to analyze this ever growing data volume and deliver actionable insight to the business is putting traditional warehousing solutions to the test. Combine this with a growing importance to put analytics in the hands of decision makers across the enterprise it becomes easy to understand why business intelligence and analytics are a key concern of leaders today
Over the last few years, IBM research and development has been investigating technologies within the warehousing landscape that would be able to help businesses address their data and analytics challenges. From exploring parallel processing execution across an exploding number of cores or how to exploit memory over an ever increasing memory demand, or defining methods to fully utilize the capabilities of modern processor, IBM continues to push research and development forward by the demands of business and economy. Within the database arena, IBM has been focused on advances in columnar storage, query optimization techniques, and compression technologies. In the analytics space, IBM has been researching to understand the unique characteristics and techniques utilized by analytical applications to query the data repository and then optimize for those query patterns.
The years of research have culminated into the latest DB2 Technology Preview This DB2 Technology Preview shows how a revolutionary combination of columnar data store, hardware and database optimization techniques will deliver a quantum leap forward in the abilities of DB2 as the backbone to supplying analytics across the enterprise. The focus is not just on the implementation of a column data store, but the implementation of a warehouse data management solution that optimizes all areas necessary to drive huge performance gains to business intelligence.
DB2 Technology Preview:
This DB2 Technology Preview is an exciting new accelerator for analytics. Featuring a column store for tables providing not only fantastic performance but storage savings due to the column store architecture and extreme compression algorithms. This new technology that has been developed by IBM and integrated directly into the DB2 engine. The DB2 Technology Preview introduces the use of column-organized tables as a direct part of the DB2 engine and DDL. This is not a bolt-on technology nor is it a separate analytic engine that sits outside of DB2. Much like when IBM added XML data as a first class object within the database along with all the storage and processing enhancements that came with XML, column organized tables are integrated into the storage and processing engine of DB2. In conjunction with this new type of data store, the DB2 technology preview will showcase improved performance, updated storage savings features and a new design paradigm to simplify the implementation and management of DB2. This allows the DB2 technology preview to deliver on these performance and storage innovations while also optimizing the use of main-memory, improving I/O efficiency and exploiting CPU instructions and characteristics to enhance the value derived from your database investments.
A key value of this technology is the storage savings. By design virtue, columnar storage is able to provide significant storage savings over current compression technologies. This provides savings on disk for the base tables and also provides savings for backups. Compression, however, is not the only storage benefit. The DB2 technology preview showscases how you no longer require auxiliary performance structures like indexes, materialized views/materialized query tables (MQTs), multi dimensional clustered tables (MDCs), etc, to gain superior performance.
Lower cost of operational analytics
• Reduces the number of objects that need to be deployed to achieve the required level of performance saving time and effort.
• Designed with simplicity in mind, for faster deployment and immediate execution of the technology.
• Reduces the cost and time of performance tuning for analytical workloads by removing the requirement of data structures such as indices.
• Does not require scale out technology to achieve required performance, leverages existing hardware or single server deployments to deliver the performance of larger clusters.
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