Teradata: All in the Family of Appliances and Big Data

Teradata recently held its Partners User Group meeting (Twitter hashtag #TDPUG11) in San Diego. Analysts were briefed previously on some of the announcements, which I covered in an earlier post.

With the benefit of hindsight, I realize that Teradata is at the center of a trend in the information management market toward broader availability and acceptance of a range of data appliances. Teradata has been in what’s now called the appliance business for decades, but  only in the last decade have other vendors, beginning with Netezza in 2000, recognized an opportunity to compete in the appliance segment. Reflecting on the Teradata Partners event, including the announcements and the customers I met, it occurred to me that Teradata has unique breath of products, which it calls a family of appliances, an appropriate label because they share a common set of software (with the exception of the products recently acquired with Aster Data). I’m sure IBM would like to position its appliances as similarly broad, but its “family” of appliances is more like cousins than siblings. They are independent product offerings with different software, and each addresses a different portion of the market.

In this context of a family of products, the announcements surrounding the Teradata Partners meeting can be divided into two groups: enhancements to individual appliances and new features related to the whole family. Several announcements covered features of the upcoming Teradata 14, including newcolumnar capabilities, extended analytic capabilities and Teradata Unity to tie together various parts of the Teradata product family. Unity provides query routing and database synchronization among different Teradata instances in an organization and also distinguishes the Teradata offerings as a family. With Unity you can query all of them as a single instance. Behind the scenes it routes the query to the appropriate instance based on which system has the data and the availability of that system. Unity also maintains consistency among individual database instances, and all or portions of a database can be synchronized across different machines. Unity does not require Teradata 14 and is available now in the Americas. The workload management capabilities of Teradata Active System Management (TASM) are separate from Unity, but I hope to see integration of these two sets of capabilities in the future.

In conjunction with Partners, Teradata announced the 2690 Data Warehouse appliance, scheduled to be available in the first quarter of 2012. The 2690 replaces the 2650 in the product line. It adds new compression capabilities and offloads compression and decompression tasks to a co-processor. With the enhanced compression the 2690 can hold more data and consume less power while managing the same workload as its predecessor. Also right next to the 2690 on the exhibit floor was NetApp showing its Hadoop applianceannounced earlier this year and the integration of it to the Teradata family of big data appliances.

Another announcement says that early next year the Aster product line will be enhanced with version 5.0, the introduction of a MapReduce appliance and an adaptor for moving data between Teradata and Aster systems. The 5.0 product includes more prebuilt analytic functions based on Aster’s SQL-MapReduce capabilities and enhances workload management for balancing SQL and MapReduce tasks. In addition, the Unity framework will be extended to incorporate the MapReduce appliance, integrating the Aster products into the analytical ecosystem and making them less of an orphan in the Teradata family.

The Aster MapReduce appliance will compete with other recently announced MapReduce appliances from EMC Greenplum and Oracle. However, the Aster version is based on its own, patented SQL-MapReduce implementation rather than the open source Apache Hadoop project. SQL-MapReduce has the advantage of making MapReduce more accessible to SQL programmers and SQL-based applications. However, it is not supported by the larger community surrounding Hadoop MapReduce. It remains to be seen whether this strategy will be successful on a large scale or SQL-MapReduce will turn out to be the black sheep of the Teradata family. Nevertheless, Teradata has an impressive set of appliances for different purposes that earn the designation of family.

Regards,

David Menninger – VP & Research Director

Aster Data is Now Part of the New Teradata

There has been a spate of acquisitions in the data warehousing and business analytics market in recent months. In May 2010 SAP announced an agreement to acquire Sybase, primarily for its mobility technology and had already been advancing its efforts with SAP HANA and BI. In July 2010 EMC agreed to acquire data warehouse appliance vendor Greenplum. In September 2010 IBM countered by acquiring Netezza, a competitor of Greenplum. In February 2011 HP announced after giving up on its original focus with HP Neoview and now has acquired analytics vendor Vertica that had been advancing its efforts efficiently. Even Microsoft shipped in 2010 its new release of SQL Server database and appliance efforts. Now, less than one month later, Teradata has announced its intent to acquire Aster Data for analytics and data management. Teradata bought an 11% stake in Aster Data in September, so its purchase of the rest of the company shouldn’t come as a complete surprise. My colleague had raised the question if Aster Data could be the new Teradata but now is part of them.

All these plays have implications for how enterprises manage and use their fast-growing stores of data. We’re living in an era of large-scale data, as I wrote recently. Founded in 1979, Teradata has been a dominant player in this market for years. Teradata was a pioneer in massively parallel processing (MPP) for database systems that I recently described, a concept behind much of today’s analytic database market, including all the recently acquired vendors mentioned above. When I worked at Oracle in the late 1990s, Teradata was the chief competitor when pursuing 1 terabyte (TB) data warehouse opportunities. Yes, managing a single terabyte was considered a significant challenge then that few vendors were ready to take on. Although the data volumes have grown, little else has changed since those years with Oracle now competing against more providers despite its recent promotions of its second generation Oracle Exadata appliance and it Oracle 11g Release 2 database at the 2010 Oracle OpenWorld.

Of course, that has all changed long since. Over the last few years Aster Data established itself as a player in the data warehousing market with an MPP relational database product. It also embraced the MapReduce parallel processing technology earlier than most other data warehouse vendors. MapReduce is a key concept of the increasingly significant Apache Hadoop project; it appears that Aster Data’s proprietary implementation of MapReduce was a significant factor in Teradata’s decision to acquire it. The tone of the joint Teradata/Aster Data briefing for analysts even suggests that Teradata is attempting an end-run around Hadoop. Aster Data has successfully developed a market niche of customers doing analysis of unstructured data and social networks. Both of these are activities one might use Hadoop to do. The company also had success in other segments, such as financial services, marketing services, retail and e-commerce.

Aster Data customers should benefit from the increased resources Teradata can invest in developing the product, and the rich heritage of Teradata in this space should enhance the infrastructure supporting the Aster Data product line. For example, Teradata’s workload management tools are among the best in the industry. However, even if the association with Teradata brings some of these capabilities to the Aster Data platform, it’s likely that the cost advantages of Aster Data over Teradata will decline over time. Integration into the Teradata organization and technology stack could detour Aster Data from its previous path of innovation. So customers may see a longer time between releases and maybe a less ambitious product roadmap.

Obviously Teradata has many more customers than Aster Data and is most concerned about them.  They, too, might see some negative impact on development schedules, but on the plus side they instantly receive a new source of technology that could be beneficial to them. The question, as yet unanswered until a roadmap is published, will be how quickly Teradata customers can take advantage of the innovations Aster Data has brought to market.

Teradata officials talked about Aster Data retaining some independence in operations, in the product line and perhaps in identity, but integration is the key to making the acquisition valuable to the Teradata customer base. One likely outcome favorable to current and future Teradata customers is more support for industry-standard server hardware, which was specifically mentioned as a benefit of the acquisition. Teradata customers may also benefit from the columnar database capabilities if those capabilities are ported to the main Teradata product line.

There were a couple of notable omissions from the discussion as the acquisition was announced. Both Teradata and Aster Data had partnerships with SAS and Cloudera, the Hadoop vendor. The SAS relationship provided advanced analytics including statistical analyses and data mining embedded within the database. Teradata may be looking to shift to using Aster Data’s embedded analytic capabilities. With respect to Hadoop, although both companies had publicly announced partnerships with Cloudera, neither had offered any deep integration with Hadoop. I expect such an arm’s-length relationship to continue, but I suspect the new combination will put more weight behind the embedded SQL-MR capability that Aster Data developed. Teradata may be large enough to attempt such an independent strategy, but I think it would be a mistake to alienate the entire Hadoop community that I have been researching, which in my opinion is a market not served by Teradata today.

Customers of other data warehousing companies should feel little immediate negative impact from this coupling. In fact, more competition over the analysis of unstructured data could spark those companies to enhance their product offerings. As I noted in beginning, the market for independent products has shrunk over the last six months, so the remaining vendors could see an increase in revenues as customers who were using Aster Data or others in preference to larger companies may start to consider other alternatives. However, at some point the musical chairs of the large-scale database acquisitions is going to stop, and when that does, if your vendor is still reliant on venture capital (VC) funding (i.e., does not yet have positive cash flow) you could have a problem. The VCs may decide to stop funding such a company, which could force them to scale back on their plans.

Probably some other shoes will drop before the market shift is over. Dell had a partnership with Aster Data that is now called into question. Dell therefore might seek an alternative partner or even acquire one of the other vendors to get into the game. Potential candidates include many we have been assessing including: 1010Data, Calpont, Kognitio and ParAccel. I didn’t include Infobright in this list because it doesn’t have an MPP offering, which seems to be table stakes now. This latest acquisition could also lead to another round of acquisitions based on Hadoop or NoSQL technologies. Or perhaps the game may take a step in the direction of complex event processing or predictive analytics. We’ll have to wait and see although at this rate we may not have to wait long!

Regards,

 David Menninger – VP & Research Director