Big Analytics Roundup (March 23, 2015)

This week, Spark Summit East produced a deluge of news and analysis on Apache Spark and Databricks.  Also in the news: a couple of ventures landed funding, SAP released software and SAS soft-launched something new for SAS Visual Analytics.

Analytic Startups

Venture Capital Dispatch on WSJ.D reports that Andreeson Horowitz has invested $7.5 million in AMPLab spinout Tachyon Nexus.  Tachyon Nexus supports the eponymous Tachyon project, a memory-centric storage layer that runs underneath Apache Spark or independently.

Social media mining venture Dataminr pulls $130 million in “D” round financing, demonstrating that the real money in analytics is in applications, not algorithms.

Apache Flink

On the Flink project blog, Fabian Hueske posts an excellent article that describes how joins work in Flink.

Apache Spark

ADTMag rehashes the tired debate about whether Spark and Hadoop are “friends” or “foes”.  Sounds like teens whispering in the hallways of Silicon Valley High.  Spark works with HDFS, and it works with other datastores; it all depends on your use case.  If that means a little less buzz for Hadoop purists, get over it.

To that point, Matt Kalan explains how to use Spark with MongoDB on the Databricks blog.

A paper published by a team at Berkeley summarizes results from Spark benchmark testing, draws surprising conclusions.

In other commentary about Spark:

  • TechCrunch reports on the growth of Spark.
  • TechRepublic wonders if anything can dim Spark.
  • InfoWorld lists five reasons to use Spark for Big Data.

In VentureBeat, Sharmila Mulligan relates how ClearStory Data’s big bet on Spark paid off without explaining the nature of the payoff.  ClearStory has a nice product, but it seems a bit too early for a victory lap.

On the Spark blog, Justin Kestelyn describes exactly-once Spark Streaming with Apache Kafka, a new feature in Spark 1.3.

Databricks

Doug Henschen chides Ion Stoica for plugging Databricks Cloud at Spark Summit East, hinting darkly that some Big Data vendors are threatened by Spark and trying to plant FUD about it.  Vendors planting FUD about competitors that threaten them: who knew that people did such things?  It’s not clear what revenue model Henschen thinks Databricks should pursue; as Hortonworks’ numbers show, “contributing to open source” alone is not a viable business model.  If those Big Data vendors are unhappy that Databricks Cloud competes with what they offer, there is nothing to stop them from embracing Spark and standing up their own cloud service.

In other news:

  • On the Databricks blog, the folks from Uncharted Software describe PanTera, cool visualization software that runs in Databricks Cloud.
  • Rob Marvin of SD Times rounds up new product announcements from Spark Summit East.
  • In PCWorld, Joab Jackson touts the benefits of Databricks Cloud.
  • ConsumerElectronicsNet recaps Databricks’ announcement of the Jobs feature for Databricks Cloud, plus other news from Spark Summit East.
  • On ZDNet, Toby Wolpe reviews the new Jobs feature for production workloads in Databricks Cloud.
  • On the Databricks blog, Abi Mehta announces that Tresata’s TEAK application for AML will be implemented on Databricks Cloud.  Media coverage here, here and here.

Geospatial

MemSQL announced geospatial capabilities for its distributed in-memory NewSQL database.

J. Andrew Rogers asks why geospatial databases are hard to build, then answers his own question.

RapidMiner

Butler Analytics publishes a favorable review of RapidMiner.

SAP

SAP released a new on-premises version of Lumira Edge for visualization, adding to the list of software that is not as good as Tableau.  SAP also released Predictive Analytics 2.0, a product that marries the toylike SAP Predictive Analytics with KXEN InfiniteInsight, a product acquired in 2013.  According to SAP, Predictive Analytics 2.0 is a “single, unified analytics product” with two work environments, which sounds like SAP has bundled two different code bases into a marketing bundle with a common datastore.  Going for a “three-fer”, SAP also adds Lumira Edge to the bundle.

SAS

American Banker reports that SAS has “launched” SAS Transaction Monitoring Optimization for AML scenario testing; in this case, “launch”, means marketing collateral is available.  The product is said to run on top of SAS Visual Analytics, which itself runs on top of SAS LASR Server, SAS’ “other” distributed in-memory platform.

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4 comments

  • Love your analysis – And the tinge of sarcasm! Great work Tom.

  • >> ADTMag rehashes the tired debate about whether Spark and Hadoop are “friends” or “foes”
    Any one comparing Spark and Hadoop are missing the point. The Hadoop ecosystem is a collection of many things components (HDFS, MapReduce, etc), interfaces (Input/Output formats, Hive serdes), libraries (compression codecs). In my opinion Spark smartly works and reuses the best parts of Hadoop viz. Input/Output formats, Hive Serde (Serialization/Deserialization) Interface, the Hive Metastore, HDFS, compression codecs, Yarn. It also provides a single a much better platform to replace for the (now) not so great parts in the Hadoop ecosystem viz. Mapreduce, the Hive execution engine, pig, Mahout, etc.

    >> We don’t see that Spark has to win at the expense of MapReduce,” Qubole’s Thusoo told ADTMag.com. “For now, there are still distinct tradeoffs in terms of use cases, infrastructure cost and relative maturity.
    I definitely see MapReduce being history in the near future and soon followed by the Hive, Mahout and other specialized execution engines in their current form . I have stopped running MapReduce daemons on our production servers, everything runs on Spark.

  • >> ADTMag rehashes the tired debate about whether Spark and Hadoop are “friends” or “foes”
    Any one comparing Spark and Hadoop are missing the point. The Hadoop ecosystem is a collection of many different things
    1) components (HDFS, MapReduce, etc)
    2) interfaces (Input/Output formats, Hive serdes),
    3) libraries (compression codecs).

    Spark works well and reuses the best parts of Hadoop viz. Input/Output formats, Hive Serde (Serialization/Deserialization) interfaces, the Hive Metastore, HDFS, compression codecs, Yarn, etc.

    And Spark provides a single and much better platform to replace the (now) not so great parts in the Hadoop ecosystem viz. Mapreduce, the Hive execution engine, Mahout, etc.

    >> We don’t see that Spark has to win at the expense of MapReduce,” Qubole’s Thusoo told ADTMag.com. “For now, there are still distinct tradeoffs in terms of use cases, infrastructure cost and relative maturity.
    I have to disagree with Qubole’s Thusoo, I definitely see MapReduce being history in the near future and soon followed by the Hive, Mahout and other specialized execution engines in their current form . I have stopped running MapReduce daemons on our production servers, everything runs on Spark.

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