Teradata Aster Delivers Big Data Insights in a SNAP
Today, Teradata unveils its next-generation analytic discovery platform, the Teradata Aster Discovery Platform 6. With it, company business analysts and super-users will be able to learn more from their massive volumes of information than ever before and they’ll be able to do so easily and in short order.
This breakthrough comes via Teradata’s new SNAP framework which lets users “snap” together multiple analytics engines (algorithms) and petabytes of data from new file stores based on tailored, domain-specific questions using a single SQL query.
SNAP works by allowing analysts to select from a portfolio of analytic engines and to submit a single data discovery SQL query. It then seamlessly and simultaneously integrates the engines and data stores, and executes and optimizes the query cross-analytic engines and data stores.
In other words, Teradata Aster Discovery Platform 6 handles the sometimes difficult,time-consuming andcumbersome work, for which the expertise of data scientists or highly-skilled analysts is often required, and frees them up to use their time and energies on higher value activities.
“There is nothing else like this (Teradata Aster Discovery Platform 6) out there,” says Chris Twogood, Teradata Aster’s product and services marketing manager.
The new platform leverages Teradata’s newly introduced graph engine -- Teradata SQL-GR. This is in addition to the current SQL MapReduce and SQL engines. The high performance engines are able to rapidly ingest and analyze large amounts of raw multi-structured data (unstructured, like from documents and e-mail; structured, like from business processes, and semi-structured) from the database and the new storage system built especially for data discovery -- Teradata Aster File Store™.
Twogood says that the Teradata Aster Discovery Platform 6 is the biggest architectural change to Aster since it was introduced.
Speeding Informed Decisions is End Goal
The beauty of the platform, provided it lives up to its promise, is that more users will be able to gain access to data-driven insights. And those insights will enable users to take action sooner rather than later, or not at all, because they’ll have a strong basis on which to make their decisions.
Take, for example, the hypothetical case of a cellular provider who is trying to reduce customer churn. With an analytics engine, the provider may be able to discover which customers, in general, are likely to switch carriers. MapReduce can be used to determine what kind of patterns and events lead to churn -- say three dropped calls in a single day.
Another kind of query might be able to determine who the people who experienced the dropped calls regularly talk to (because friends of people who change carriers often change carriers) and so on … Using this information, the carrier will know exactly which customers to extend special offers to, incentivizing them to stay, before they’ve even hinted that might go away.
While each of these kinds of queries had to be run separately in the past and answers came slowly even with highly-skilled individuals involved every step of the way, the Teradata Aster Discovery Platform can handle the data and deliver insights quickly with one simple query.
The value that the new platform can provide to Enterprises and organizations is quite clear. This is not Big Data Discovery voodoo.
Perhaps Big Data critics who ask questions like “Is Big Data An Economic Big Dud?” will soon have their answers, courtesy of Teradata.
Title image courtesy of Krivosheev Vitaly (Shutterstock)