#aster data

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Rob Klopp:

But there are three products, the Greenplum database (GPDB), HAWQ, and Aster Data, that will be squeezed more quickly as they are positioned either in between the EDW and Hadoop… or directly over Hadoop. In this post I’ll explain what I suspect Pivotal and Teradata are trying to do… why I believe their strategy will not work for long… and why readers of this blog should be careful moving forward.

This is a very interesting analysis of the enterprise data warehouse market. There’s also a nice visualization of this prediction:

Here’s an alternative though. As showed in the picture above, the expansion of in-memory databases’ depends heavily on the evolution of the price of memory. It’s hard to argument against price predictions or Moore’s law. But accidents even if rare are still possible. Any significant change in the trend of memory costs, or other hardware market conditions (e.g. an unpredicted decrease of the price for SSDs), could give Teradata and Pivotal the extra time/conditions to break into advanced hybrid storage solutions that would offer slightly less fast but also less expensive products than their competitors’ in-memory databases.

Original title and link: Aster Data, HAWQ, GPDB and the First Hadoop Squeeze (NoSQL database©myNoSQL)

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I think this is the first extensive Big Data report I’m reading that includes enough relevant and quite exhaustive data about the majority of players in the Big Data market, plus some captivating forecasts.

As of early 2012, the Big Data market stands at just over $5 billion based on related software, hardware, and services revenue. Increased interest in and awareness of the power of Big Data and related analytic capabilities to gain competitive advantage and to improve operational efficiencies, coupled with developments in the technologies and services that make Big Data a practical reality, will result in a super-charged CAGR of 58% between now and 2017.

While there are many stories behind these numbers and many things to think about, here is what I’ve jotted down while studying the report:

it’s no surprise that “megavendors” (IBM, HP, etc.) account for the largest part of today’s Big Data market revenue

still, the revenue ratio of pure-players vs megavendors feels quite unbalanced: $311mil out of $5.1bil

the pure-player category includes: Vertica, Aster Data, Splunk, Greenplum, 1010data, Cloudera, Think Big Analytics, MapR, Digital Reasoning, Datameer, Hortonworks, DataStax, HPCC Systems, Karmasphere

there are a couple of names that position themselves in the Big Data market that do not show up in anywhere (e.g. 10gen, Couchbase)

this could lead to the conclusion that the companies that include hardware in their offer benefit of larger revenues

I’m wondering though what is the margin in the hardware market segment. While not having any data at hand, I think I’ve read reports about HP and Dell not doing so well due exactly to lower margins

see bullet point further down about revenue by hardware, software, and services

this could explain why so many companies are trying their hand at appliances

by looking at the various numbers you can see that those selling appliances usually have a large corporation behind supporting the production costs for hadware and probably the cost of the sales force

in the Big Data revenue by vendor you can find quite a few well-known names from the consulting segment

the revenue by type pie lists services as accounting for 44%, hardware for 31%, and software for 13% which might give an idea of what makes up the megavendors’ sales packages

most of the NoSQL database companies and Hadoop companies are mostly in the software and services segment

Great job done by the Wikibon team.

Original title and link: Big Data Market Analysis: Vendors Revenue and Forecasts (NoSQL database©myNoSQL)

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Aster Data SQL-MapReduce Technology Patent

From a Teradata PR announcement:

SQL-MapReduce® is a framework which enables fast, investigative analysis of complex information by data scientists and business analysts. It enables procedural expressions in software languages (such as Java, C#, Python, C++, and R) to be parallelized across a group of linked computers (compute cluster) and then activated for use (invoked) with standard SQL.  

The closest open source solution I can think of is Pig , created and open sourced by Yahoo! (PDF).

Original title and link: Aster Data SQL-MapReduce Technology Patent (NoSQL database©myNoSQL)