Big data – part 3

I have renamed this series to “Big data” because it’s no longer just about Hadoop (see Hadoop – part 1 & Hadoop – part 2 posts).

To try to partition this space just a bit, there is unstructured data analysis and structured data analysis. Hadoop is used to analyze un-structured data (although Hadoop is used to parse and structure the data).

On the other hand, for structured data there are a number of other options currently available. Namely:

• EMC Greenplum – a relational database that is available in a software only as well as now as a hardware appliance. Greenplum supports both row or column oriented data structuring and has support for policy based data placement across multiple storage tiers. There is a packaged solution that consists of Greenplum software and a Hadoop distribution running on a GreenPlum appliance.
• HP Vertica – a column oriented, relational database that is available currently in a software only distribution. Vertica supports aggressive data compression and provides high throughput query performance. They were early supporters of Hadoop integration providing Hadoop MapReduce and Pig API connectors to provide Hadoop access to data in Vertica databases and job scheduling integration.
• IBM Netezza – a relational database system that is based on proprietary hardware analysis engine configured in a blade system. Netezza is the second oldest solution on this list (see Teradata for the oldest). Since the acquisition by IBM, Netezza now provides their highest performing solution on IBM blade hardware but all of their systems depend on purpose built, FPGA chips designed to perform high speed queries across relational data. Netezza has a number of partners and/or homegrown solutions that provide specialized analysis for specific verticals such as retail, telcom, finserv, and others. Also, Netezza provides tight integration with various Oracle functionality but there doesn’t appear to be much direct integration with Hadoop on thier website.
• ParAccel – a column based, relational database that is available in a software only solution. ParAccel offers a number of storage deployment options including an all in-memory database, DAS database or SSD database. In addition, ParAccel offers a Blended Scan approach providing a two tier database structure with DAS and SAN storage. There appears to be some integration with Hadoop indicating that data stored in HDFS and structured by MapReduce can be loaded and analyzed by ParAccel.
• Teradata – a relational databases that is based on a proprietary purpose built appliance hardware. Teradata recently came out with an all SSD, solution which provides very high performance for database queries. The company was started in 1979 and has been very successful in retail, telcom and finserv verticals and offer a number of special purpose applications supporting data analysis for these and other verticals. There appears to be some integration with Hadoop but it’s not prominent on their website.

Probably missing a few other solutions but these appear to be the main ones at the moment.

In any case both Hadoop and most of it’s software-only, structured competition are based on a massively parrallelized/share nothing set of linux servers. The two hardware based solutions listed above (Teradata and Netezza) also operate in a massive parallel processing mode to load and analyze data. Such solutions provide scale-out performance at a reasonable cost to support very large databases (PB of data).

Now that EMC owns Greenplum and HP owns Vertica, we are likely to see more appliance based packaging options for both of these offerings. EMC has taken the lead here and have already announced Greenplum specific appliance packages.

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One lingering question about these solutions is why don’t customers use current traditional database systems (Oracle, DB2, Postgres, MySQL) to do this analysis. The answer seems to lie in the fact that these traditional solutions are not massively parallelized. Thus, doing this analysis on TB or PB of data would take a too long. Moreover, the cost to support data analysis with traditional database solutions over PB of data would be prohibitive. For these reasons and the fact that compute power has become so cheap nowadays, structured data analytics for large databases has migrated to these special purpose, massively parallelized solutions.

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