Teradata's new storage and analytics offerings focus on speed and simplicity
Three tools and a service
Teradata has launched three products and a service designed to provide businesses with faster and simplified access to analytics.
One of the new tools, Connection Analytics, uses data to build relationships between people, products and purchase processes. Teradata's algorithms will take into account centrality measures, location analytics and machine learning to discover how every step of the purchase process is influenced by other actions.
"Part of the problem is content based analytics look at individual entities as discrete products," said Chris Twogood, VP of Product Marketing at Teradata. "What they don't look at is the interdepencies and relationships between entities. Some of this has to do with social network analysis. How does my purchase of a product influence others to buy or influence me to buy other kinds of products?"
In-memory and Data Fabric
Teradata also launched, Data Fabric, a tool that will tie together multiple solutions into one analytics ecosystem. Users will be able to access Teradata's database within the same ecosystem as Hadoop and Oracle.
Additionally, with Data Fabric, users can "do pushdown processing, [provide] easy administration and classify different workloads for performance," Twogood said. "Data Fabric moves data in a high-speed way as if it's a single system. It's an orchestrating engine so that you're not having to go to disparate systems."
Teradata has also launched in-memory optimization, including added pipelining and in-memory structures, that are built to remove latency from the storage and administration process. The tool leverages vectorization in order to reduce the amount of processes that occur between the memory and the CPU. The tool includes temperature management in order to help reduce running costs.
The data integration optimization service
Teradata has also created a consultancy that will help companies reduce the cost of data integrations. The service will help clients analyze offloading jobs in order to optimize speed and precision.
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