Teradata: All content tagged as Teradata in NoSQL databases and polyglot persistence
Earlier today I’ve posted about Teradata’s take on the evolution of databases. As expected, everything is safe and under control. Now this report from Larry Dignan for ZDNet about Teradata Q4 earnings call presents Teradata’s perspective about Hadoop:
Teradata’s fourth quarter earnings were solid, but analysts peppered management with questions about Hadoop as data warehouse revenue worries persist.
Teradata CEO Mike Koehler and CFO Steve Scheppmann talked Hadoop throughout the company’s conference call. Was Hadoop taking Teradata’s business away? What’s the revenue hit? Can Teradata co-exist?
Once again everything is safe with a bright future. Until it isn’t anymore and Hadoop eats the enterprise data warehouse space. In Teradata’s defense, they’ve been one of the first companies that has looked seriously at Hadoop and came up with a coherent positioning.
Original title and link: Hadoop and Teradata’s business ( ©myNoSQL)
Simply put, Hadoop becomes the staging area for “raw data streams” while the EDW stores data from “operational systems”. Hadoop then analyzes the raw data and shares the results with the EDW. […] The paper then positions Hadoop as an active archive. I like this idea very much. Hadoop can store archived data that is only accessed once a month or once a quarter or less often.. and that data can be processed directly by Hadoop programs or shared with the EDW data using facilities such as Teradata’s SQL-H, or Greenplum’s External Hadoop tables (not by HAWQ, though… see here), or by other federation engines connected to HANA, SQL Server, Oracle, etc.
It’s an interesting positioning of Hadoop. And it’s very similar to the approach Linux has taken when penetrating the walls of enterprises. Then it slowly replaced pretty much everything.
In the early days—we are still in those days, the EDW vendors could still believe this story: Hadoop is complicated and meant for batch processing and it lacks the tools and refinements built over years in EDW.
But the story is starting to change. Fast. Hadoop is becoming more of a platform (YARN), it gets support for (almost) real-time querying (Impala, Project Stinger, HAWQ, just to name a few), and Hadoop leaders are signing partnerships with challengers and incumbents of the big data market at a rate that I don’t think I’ve seen before.
In the end, guess who will become the pillar of the big data platforms: the solution storing all the data or those tools being able to process, indeed very fast and with much control, limited amounts of that data?
✚ The Cloudera-Teradata paper titled “Hadoop and the Data Warehouse: When to Use Which” can be found here.
Original title and link: Hadoop and the EDW ( ©myNoSQL)
In the series of Big Data for C-Suites, here’s a video from Teradata:
Notice how this one focuses on two dimensions only: keywords and Teradata. For now Hortonworks’s Big Data and Hadoop for C-Suites resonates better with me.
Original title and link: Big Data for C-Suites: Teradata and Big Data the Best Decision Possible ( ©myNoSQL)
The Cloudera deal from September 2010 provided a pipe from a Hadoop cluster into the Teradata data warehouses, while the Hortonworks partnership announced today is providing a pipe between Hadoop and Aster Data appliances.
Hortonworks and Teradata will do joint marketing and development, and are exploring ways to better integrate their respective software. This will specifically be done on Data Platform 1.0 from Hortonworks and Aster Database 5.0 from Teradata. Future engineering work could include running the HortonWorks and Aster Data programs on the same physical clusters, side-by-side, although this is not the way customers tend to do it today, according to Argyros.
Original title and link: More Details About the Teradata and Hortonworks Partnership ( ©myNoSQL)