IBM DB2 for z/OS and IBM System z are already the platforms of choice for decision support environments worldwide—and DB2 has been adding warehousing and analytics-specific functionality with each new release. Today, it is possible to build an end-to-end enterprise warehousing solution in System z that includes data extraction, transformation, load, query, and reporting functionalities.
Analytics and warehousing queries are complex, highly resource-intensive endeavors that require organizations to process massive amounts of data. Big data is a reality today, and data volumes will only continue to grow. In addition, the dynamics of global competition mean that companies must leverage the information in their systems quickly to help them make smart, informed strategy decisions.
In many situations, however, the speed at which analysts can understand and to react to changes in dynamic business environments is limited by the IT infrastructure’s ability to process complex queries against large volumes of data. In an ideal world, business analytics would be unconstrained by infrastructure limitations. Organizations could leverage near-instantaneous analytics and run reports when they need them, even in real time. This is where query accelerators come into the picture.
Query accelerators—transparent tools designed to boost database speed and performance—can provide dramatic improvements in response time and reduce CPU utilization by offloading eligible queries to specifically designed hardware. They can help deliver faster, more reactive business insight by executing analytics when they are required, and at hyper-speed.
The IBM DB2 Analytics Accelerator can dramatically improve performance and reduce the cost of analytics in DB2 for z/OS environments. Available as an add-on appliance built on IBM Netezza technology, the DB2 Analytics Acclerator is designed for easy, rapid deployment. The appliance requires very little configuration, and accelerator performance information is integrated with the traditional DB2 instrumentation facility.
Transparency is a key concept here. In practice, you “plug performance” to the mainframe and instruct DB2 to consider the query accelerator as a new access path for eligible queries on eligible objects. There is no need to change existing queries to work with DB2 Analytics Accelerator (the DB2 engine evaluates whether acceleration in the appliance is a good idea). Not every query is eligible; acceleration is only for dynamic SQL that complies with restrictions commonly found in analytic queries. Users must make data available in the appliance before it is eligible to be accelerated.
For candidate queries, the results can be astonishing: queries often run significantly faster than they ever have before. Response times for well-tuned queries running on traditional hardware can shrink from hours to seconds.
Queries that previously ran too slowly to be useful can often be completed in minutes. And as response times approach what you might expect from an online transaction processing environment, real-time analytics can become an everyday reality.
The secret of this speed resides in the highly specialized hardware and software that is tuned for serving analytics queries. The DB2 Analytics Accelerator appliance exploits massive parallel processing on dedicated CPUs, disks, and memory in a highly and linearly scalable architecture. The business value of a query accelerator resides in its close and transparent integration with DB2 and System z; you get hyper-speed analytics in the highly reliable, secure and stable mainframe environment that you already know and love. DB2 Analytics Accelerator can not only help to reduce total cost of ownership, but also change how analytics are executed on DB2 and System z.
IBM Redbook: Get a holistic view of modernization from the database to the user interface
IBM Redbook: Apply IBM Watson Content Analytics and gain business insight
IBM Redbook: DB2 SQL compatibility features for enterprise database migration
Embeddable reporting is among advanced services now available
Big data in a minute: The little details of big data