Teradata Updates Aster Platform, Unified Data Architecture To Make Big Data a ‘Snap’
Teradata’s latest updates to its analytics discovery platform aims to open up data analytics to more mainstream users. Teradata’s Aster Discovery Platform 6 sports an innovative “snap together” technology to empower non-technical users to generate business insights. IDN speaks with Teradata’s Senior Director Manan Goel.
by Vance McCarthy
"We provide users a ‘unified data architecture’ that makes it much easier and faster to get insights from all sorts of data."
Teradata’s latest updates to its analytics discovery platform aims to open up data analytics to more mainstream users. Teradata’s Aster Discovery Platform 6 sports an innovative “snap together” technology to empower non-technical users to generate business insights.
Teradata’s SNAP Framework does what the name implies, lets users “snap” multiple analytic engines and file stores together to get customizable, deep dives into data. With an emphasis on simplicity, business analysts can simply submit a single query in SQL to seamlessly activate multiple analytic engines across petabytes of data from new and existing files stores, Manan Goel, Teradata’s Senior Director of Product Marketing, told IDN.
“For our customers, their data sources are evolving,” Goel said. “Earlier, organizations would mostly use transactional data they collected in the ERP or CRM systems. But today, they have clickstream data, machine data and data from weblogs and other unstructured sources they can innovate with and tap into.”
“To help customers get the full picture, we feel it’s necessary to provide users a ‘unified data architecture’ that makes it much easier and faster for all types of users – not just data specialists or data scientists – to get these insights from all sorts of data,” Goel added.
“Having UDA provides Teradata customers an integrated data architecture, which, in turn, will improve performance and remove the need to work with multiple complex tools,” Goel said. UDA plays a key role in helping deliver three-workload specific solutions, Goel added, including using Hadoop pre-processing of data; uniting structured and unstructured data to begin exploratory analytics; and ability to support on-going SLA for operations.
On that point, Teradata’s latest edition of the SNAP Framework powering the Aster Discovery Platform lets users easily select from a portfolio of analytic engines and submit a single data discovery SQL query. SNAP seamlessly and simultaneously integrates the engines and data stores, and then executes and optimizes the query cross-analytic engines and data stores.
The result: Teradata’s Aster Discovery Platform 6 lets customers more easily bring together their data from diverse data sets. And not only can they bring all that multi-structured data together, they can analyze it with multiple methods of analytics, Goel added. In specific, Teradata’s updated platform offers a pre-built analytics library of more than 80 SQL-MapReduce functions.
Under the Covers of Tearadata Aster Discovery Platform 6
Under the covers, Teradata’s updated query executer and optimizer work together for high performance processing at scale.
Teradata Aster Discovery Platform 6 adds a new graph engine. Teradata SQL-GR can work right alongside the company’s already available MapReduce and SQL engines. Together, these engines can rapidly ingest and analyze large amounts of raw multi-structured data from the database and the new storage system built especially for data discovery.
The Teradata SQL-GR engine is unique because it is scalable, processes big data in parallel, and is not limited by system memory. Its analytic capability can extend to millions of nodes or processing units. The Teradata SQL-GR enables native processing of large-scale analytic graph queries and pre-built graph functions and can be used for customer churn, product affinity, fraud detection, and recommendation engines. This capability helps companies use graph analysis to look for high-traffic connections among select users to derive insights, such as when a telecom firm might look for clues to fraudulent calling activity, Goel added.
Teradata’s Aster Integrated Optimizer, another innovation in the latest Aster version, is able to understand the discovery query’s request and automatically run the query in ways that ensure high performance and data processing at scale. The new Teradata Aster Integrated Executor can orchestrate all types of queries by enabling communication among analytic engines without user intervention.
Teradata’s latest Aster update also leverages powerful machine learning and ensembles from hundreds of algorithms, Goel added. This capability lets users quickly derive analytics to understand complex or real-time problems that can require access to multiple data sets.
The new Teradata Aster File Store works with multi-structured data, in addition to traditional row or column format. It can quickly take in and store petabytes of raw, multi-structured data, provide storage management, and then make it readily available for analysis. The Teradata Aster File Store is compatible with Apache Hadoop and the Hadoop Distributed File System.
To efficiently handle large amounts of data, Teradata’s SNAP Framework also supports the Aster Common Storage System and Services. This provides a set of common services that can efficiently write data to the disk. It also supports data replication, and can provide data snapshots. This entire capability works under unified security model, offers high availability, reliability, and manageability – and all without the need to physically store data, Goel added.
Beyond benefits to end-user customers, Teradata’s UDA also sets the stage for a wave of new and deeper Teardata technology partnerships. “We believe in using UDA to let customers easily modernize their data warehousing [solutions] and derive more value with best-of-breed solutions,” Goel said. Teradata is already working with IBM, SAP, MicroStrategies, Tableau and partnerships with Oracle and Tibco are also in the works, he added.
- Integration is The Next Step to ROI on Big Data Analytics
- Survey: Evan Data Finds App Developers Welcome the Surge in AI, Deep Learning Tools
- MapR Looks To Accelerate Analytics Apps; Adds Event-Driven Microservices to Converged Data Platform
- Talend’s Big Data Sandbox Assembles Key Ingredients and ‘Recipes’ To Explore Ideas and Deliver Success
- SAS Enters Era of ‘Open Analytics’ with Viya Platform’s Focus on Cloud, Open Programming and Machine Learning