New cloud storage and Hadoop managed service offering from Spring SNW

Strange Clouds by michaelroper (cc) (from Flickr)
Strange Clouds by michaelroper (cc) (from Flickr)

Last week I posted my thoughts on Spring SNW in Dallas, but there were two more items that keep coming back to me (aside from the tornados).  The first was a new startup called Symform in cloud storage and the other was an announcement from SunGard about their new Hadoop managed services offering.

Symform

Symform offers an interesting alternative on cloud storage that avoids the build up of large multi-site data centers and uses your desktop storage as a sort of crowd-sourced storage cloud, sort of bit-torrent cloud storage.

You may recall I discussed such a Peer-to-Peer cloud storage and computing services in a posting a couple of years ago.  It seems Symform has taken this task on, at least for storage.

A customer downloads (Windows or Mac) software which is installed and executes on your desktop.  The first thing you have to do after providing security credentials is to identify which directories will be moved to the cloud and the second is to tell whether you wish to contribute to Symform’s cloud storage and where this storage is located.  Symform maintains a cloud management data center which records all the metadata about your cloud resident data and everyone’s contributed storage space.

Symform cloud data is split up into 64MB blocks and encrypted (AES-256) using a randomly generated key (known only to Symform). Then this block is broken up into 64 fragments with 32 parity fragments (using erasure coding) added to the stream which is then written to 96 different locations.  With this arrangement, the system could potentially lose 31 fragments out of the 96 and still reconstitute your 64MB of data.  The metadata supporting all this activity sits in Symform’s data center.

Unclear to me what you have to provide as far as ongoing access to your contributed storage.  I would guess you would need to provide 7X24 access to this storage but the 32 parity fragments are there for possible network/power failures outside your control.

Cloud storage performance is an outcome of the many fragments that are disbursed throughout their storage cloud world. It’s similar to a bit torrent stream with all 96 locations participating in reconstituting your 64MB of data.  Of course, not all 96 locations have to be active just some > 64 fragment subset but it’s still cloud storage so data access latency is on the order of internet time (many seconds).  Nonetheless, once data transfer begins, throughput performance can be pretty high, which means your data should arrive shortly thereafter.

Pricing seemed comparable to other cloud storage services with a monthly base access fee and a storage amount fee over that.  But, you can receive significant discounts if you contribute storage and your first 200GB is free as long as you contribute 200GB of storage space to the Symform cloud.

Sungard’s new Apache Hadoop managed service

Hadoop Logo (from http://hadoop.apache.org website)
Hadoop Logo (from http://hadoop.apache.org website)

We are well aware of Sungard’s business continuity/disaster recovery (BC/DR) services, an IT mainstay for decades now. But sometime within the last decade or so Sungard has been expanding outside this space by moving into managed availability services.

Apparently this began when Sungard noticed the number of new web apps being deployed each year exceeded the number of client server apps. Then along came virtualization, which reduced the need for lots of server and storage hardware for BC/DR.

As evident of this trend, last year Sungard announced a new enterprise class computing cloud service.  But in last week’s announcement, Sungard has teamed up with EMC Greenplum to supply an enterprise ready Apache Hadoop managed service offering.

Recall, that EMC Greenplum is offering their own Apache Hadoop supported distribution, Greenplum HD.  Sungard is basing there service on this distribution. But there’s more.

In conjunction with Hadoop, Sungard adds Greenplum appliances.  With this configuration Sungard can load Hadoop processed and structured data into a Greenplum relational database for high performance data analytics.  Once there, any standard SQL analytics and queries can be used against to analyze the data.

With these services Sungard is attempting to provide a unified analytics service that spans all structured, semi-structured and unstructured data.

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Probably more to Spring SNW but given my limited time on the exhibition floor and time in vendor discussions these and my previously published post are what I seem of most interest to me.

Big data – part 3

Linkedin maps data visualization by luc legay (cc) (from Flickr)
Linkedin maps data visualization by luc legay (cc) (from Flickr)

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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