Teradata’s Big Data and Analytics Strategy Unveiled

Unlike other recent conferences that seem to focus almost exclusively on cloud computing, this week’s Teradata Partners Conference emphasized big data and analytics. The vision that Teradata lays out is one in which new technologies such as Apache Hadoop live side by side with more traditional enterprise data warehouses (EDW) and companies have the flexibility to define their own approaches to BI tools. This approach, at least in the near and medium terms, makes a lot of sense, and is backed by our own research into big data, which shows relational databases are still the predominant tool for delivering big data analytics and solutions to the enterprise. Companies have spent a lot of money on their current infrastructures, and not many have the stomach for a rip-and-replace strategy. Nor do most organizations have the tools and the skillsets yet to take full advantage of all of the newer approaches coming into the market around big data analytics.

Now part of Teradata’s big data and analytics strategy is its integration of Aster that my colleague assessed, which the company acquired about a year ago, into the Teradata portfolio. Aster offers some big advantages for accessing big data through commonly used and understood query approaches such as SQL. In fact, the SQL-H approach that Aster pioneered allows ANSI-standard SQL exploration of big data, and so far Aster is the only product on the market with such capabilities. SQL-H allows users to employ familiar SQL approaches to the MapReduce framework and take advantage of the massively parallel processing nature of Hadoop. It does this by leveraging HCatalog, which abstracts a metadata layer from Hadoop and provides hooks for the Aster query engine. The fact that this approach uses standard SQL makes it fit in well with existing BI tools and processes.

The Teradata approach relies heavily on Hadoop, the fastest-growing open source ecosystem around big data, but one that is still in its early stages; most organizations have not even put it through a proof of concept phase. Its most mature use case, and the one where organizations seem to be deriving the most value, is one in which Hadoop acts as a supercharged landing strip and refinery for different types of data. Hadoop is very good at capturing and storing data, and at applying low-level math on an extremely large scale in a batch process. The functional tasks it performs, such as filtering, sorting, counting and averaging, are valuable in deriving order out of the chaos of unstructured data. Once analysts apply some basic structure to the data, they can look at it in more advanced ways with more complex algorithms. These algorithms can be used in a more purpose-built analytical platform such as Teradata Aster. Both approaches provide analysts with an exploratory sandbox environment within which they can build algorithms and test hypotheses iteratively.

Teradata promises enterprise-class customer support for the Big Analytics Appliance. The company provides three levels of customer support across the entire system, including Hadoop. Any calls escalated to the highest level, level four, are routed to various centers of expertise, including platform engineering, Aster engineering and Hortonworks.Last week Teradata announced the Teradata Aster Big Analytics Appliance. In a close developmental partnership with HortonWorks, the Big Analytics Appliance aligns with Hadoop via Hortonworks’ HDP 1.1 and integrates hardware with software. The system provides more than 50 prebuilt MapReduce functions that are accessible in SQL. Because it uses standardized SQL, the system can leverage off-the-shelf BI tools and current ETL deployments. Another key feature is Teradata Viewpoint, which provides server management and monitoring for Teradata’s EDW platform and Teradata Aster. Support for Hadoop is expected early 2013. The system connects the Big Analytics Appliance to the Teradata EDW platform via 40Gbps InfiniBand, SQL-H, TD-Aster and TD-Hadoop connectors.

This enterprise assurance factor is a key part of the Teradata’s analytics strategy and will be a key determinant of Teradata’s future success. In discussions with customers at the conference, I got a positive feeling about Teradata’s ongoing commitment to tight integration within its systems, its approach to professional services and its general approach to customer support and satisfaction. Enterprise assurance is an intangible driver of purchasing behavior, but it can be especially strong in changing times.  One challenge will be for Teradata to maintain this value as it grows. It will be incumbent upon its professional services divisions to attract and retain a high level of talent, and structure its service delivery models in such a way that any growing pains are seamless from a customer perspective.

Teradata’s Big Analytics Appliance and big data analytics strategy provide a compelling story that addresses the analytics skills and staffing gap that our big data research and promises a high level of customer assurance.

Regards,

Tony Cosentino

VP & Research Director