Don’t wait to set your data strategy as Netezza goes end of support
For almost 20 years, Netezza technology has been at the forefront of advanced analytics, even as field of analytics has grown. New database architectures, vast increases in processing power, new tools and far more sources of data have combined to transform analytics.
In this landscape of change, Netezza technology has evolved as well. After originating as a purely transactional database system, Netezza built the industry’s first true “data warehouse appliance,” rapidly increasing users’ ability to use different kinds of data while focusing on core strengths of simplicity, speed and low cost of ownership. Netezza added support for major programming languages, including Hadoop MapReduce, Java, C++ and Python, so data scientists could efficiently work in languages they already knew. With these developments, Netezza grew from simply supporting data reports to becoming an analytics engine for the entire range of business data.
Now, the trend of Netezza innovation continues, raising new opportunities for accessibility and analytic insight while preserving business continuity. Support for Netezza TwinFin and Striper models will end as early as June 2019, potentially leaving business-critical data in unsupported environments. Yet there’s no need for long-time Netezza customers to take those risks. The next stage in Netezza’s evolution has already arrived.
The IBM Integrated Analytics System (IAS) takes the best of Netezza and adds compelling features and capabilities, including:
- Up to 5 times better performance than the Netezza models it’s replacing
- Built-in data science tools, including Watson Studio (formerly DSX)
- Support for popular programming languages such as Python and R
- Machine learning capabilities, using embedded Spark
- Support for widespread data federation via a Common SQL Engine
- Native cloud connectivity
These features work together to provide an integrated analytic platform. Thanks to the Common SQL Engine and cloud connectivity, data can be easily federated, accessed and analyzed via Hadoop, cloud and the IAS appliance itself. Bob Wilkinson, vice president at Fourth Millennium Technologies, calls the IAS “the best, if not the only choice for analytics today”.
A new generation of analytics
More than any single new feature, the IAS supports a widespread upgrade in how organizations can approach their analytical workloads. With built-in Watson Studio, data scientists have a single platform for drawing on any type of data, stored anywhere. Within this IAS sandbox, they can easily access data, build models and use machine-learning capabilities to refine those models. With built-in Spark, data scientists can access data and perform high-performance analytic calculations within IAS rather than moving data to an external Spark cluster.
According to Vitaly Tsivin, executive vice president and head of business intelligence for AMC Networks, AMC is using IBM analytics to “understand viewing patterns across traditional and digital channels, make smarter scheduling and marketing decisions, and win new viewers and advertisers”.
Thanks to the integrated hybrid data management model from IBM, in which the same code runs in the cloud and appliance solutions, AMC has “an incredible ability to move large amounts of data between [our] cloud solution [and] appliance solutions” without major training requirements or a steep learning curve, Tsivin says.
Machine learning has helped AMC keep up with a rapidly changing television market and understand how viewers’ preferences are changing. The models learn continuously rather than being updated quarterly by specialized staff. AMC can adjust programming and target ads to respond to changing viewing patterns as they happen, not after the fact. This is flexible, powerful, adaptable analytics, made possible by a flexible, powerful, adaptable platform.
How to get started with the IAS
To upgrade to the next generation of Netezza, the new guide, “Charting your path to the IBM Integrated Analytics System”, is the best place to start. It explains how the IAS offers a combination of high-performance hardware and a database query engine designed for massively parallel processing, data warehousing and analytics. The guide also breaks down the steps needed for a smooth transition and how IBM experts can support businesses along the way.
Keeping up with advances in analytics can be daunting, but the right data platform and the right forward-looking data management philosophy are essential to success.
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