IBM and Spark (Updated)

Updated March 8, 2016.  After publishing this post, I met with several IBM executives at Spark Summit East, who confirmed the accuracy of the original post and provided additional detail, which I’ve included in this version.  Updates are in bold red italics.

IBM also provided the low-resolution image.

IBM has a good story to tell — one out of ten contributors to Spark 1.6 were IBM employees.  But IBM does not tell its story effectively.  Nobody cares that IBM invented the punch card and the floppy disk.  Nobody cares that IBM is so big it can’t tell a straight product story.  Bigness is IBM’s problem.

On June 15, 2015, IBM announced a major commitment to Spark.  As we approach Spark Summit East, I thought it would be fun to check back and see how IBM’s accomplishments compare with the goals stated back in June.

Before we start, I’d like to note that any contribution to Spark moves the project forward, and is a good thing.  Also, simply by endorsing Spark, IBM has changed the conversation.  In early 2015, some analysts and journalists claimed that Spark was overhyped and “not enterprise ready.”  We haven’t heard a peep from this crowd since IBM’s announcement.  For that alone, IBM should get some kind of prize.  🙂

In its announcement, IBM detailed six initiatives:

  • IBM will build Spark into the core of the company’s analytics and commerce platforms.
  • IBM’s Watson Health Cloud will leverage Spark as a key underpinning for its insight platform, helping to deliver faster time to value for medical providers and researchers as they access new analytics around population health data.
  • IBM will open source its breakthrough IBM SystemML machine learning technology and collaborate with Databricks to advance Spark’s machine learning capabilities.
  • IBM will offer Spark as a Cloud service on IBM Bluemix to make it possible for app developers to quickly load data, model it, and derive the predictive artifact to use in their app.
  • IBM will commit more than 3,500 researchers and developers to work on Spark-related projects at more than a dozen labs worldwide, and open a Spark Technology Center in San Francisco for the Data Science and Developer community to foster design-led innovation in intelligent applications.
  • IBM will educate more than 1 million data scientists and data engineers on Spark through extensive partnerships with AMPLab, DataCamp, MetiStream, Galvanize and Big Data University MOOC.

Let’s see where things stand.

Spark in IBM Analytics and Commerce Platforms

IBM has an expansive definition of “analytics”, reporting $17.9 billion in business analytics revenue in 2015.  IDC, which tracks the market, credits IBM with $4.5 billion in business analytics software revenue in 2014.  The remaining $13.4 billion, it seems is services and fluff, neither of which count when the discussion is “platforms.”

Of that $4.5 billion, the big dogs are DataStage InfoSphere, DB2, Netezza PureData System for Analytics, Cognos IBM Business Analytics and SPSS IBM Predictive Analytics — so this is where we should look when IBM says it is building Spark into its products.

Currently, Cloudant is the only IBM data source with a published Spark connector.  Want to access DB2 with Spark?  It’s a science project.  Of course, you can always use the JDBC connector if you’re patient, but the standard is a parallel high-speed connector, like SAS has offered for years.  An IBM insider tells me that there is a project underway to build a Spark connector for Netezza, which will be a good thing when it’s available.

Update: IBM has subsequently added the one-way single-threaded Netezza connector to Spark Packages.  It’s also available on Git and the IBM Developer site.

I would emphasize that a one-way single-threaded connector is useful once, when you decommission your Netezza box and move the data elsewhere.  Netezza developed a native multi-threaded connector for SAS in a matter of months, so it’s not clear why it takes IBM so long to deliver something comparable for Spark.  

 

Last October in Budapest, an IBM VP — one of 137 IBM Veeps of “Big Data”  — claimed that DataStage InfoSphere supports Spark now.  Searching documentation for InfoSphere 11.3, the most current version, produces this:

Screen Shot 2016-02-13 at 2.48.21 PM

IBM appears to have discovered a new kind of product management where you build features into a product, then omit them from the documentation.

That was the approach taken for IBM Analytics Server Release 2.1, which was packaged up and shoved out the door so fast the documentation folks forgot to mention the Spark pushdown.  That said, Release 2.1.0.1 is an improvement; all functions that Analytic Server can push down to MapReduce now push down to Spark, and IBM supports the product on Cloudera and MapR as well as Hortonworks and BigInsights.

It’s not clear, though, why IBM thinks that licensing Analytics Server as a separate product is a smart move.  Most of the value in analytics is at the top of the stack (e.g. SPSS or Cognos,) where users can see results.  Spark pushdown is rapidly becoming table stakes for analytics software; the smarter move for IBM is to bundle Analytics Server for free into SPSS, Cognos and BigInsights, to build value in those products.

It’s also curious that IBM simultaneously continues to peddle Analytics Server while donating SystemML to open source.  Why not push down to Spark through SystemML?

So far as Spark is concerned, IBM is leaving SPSS Statistics users out in the cold unless they want to add Modeler and Analytics Server to the stack.  For these customers, Alteryx and RapidMiner look attractive.

A search for Spark in Cognos documentation yields a big fat zero, which explains why Gartner just tossed IBM from the Leaders quadrant in BI.Screen Shot 2016-02-13 at 3.11.35 PM

Update: at my request, an IBM executive shared a list of products that IBM says it has rebuilt around Spark to date.  I’m publishing it verbatim for reference, but note that the list includes:

  1. Double and triple counting (“Watson Content Analytics integrates with Dataworks which integrates with Spark as a Service”).
  2. Products that do not seem to exist (Spark connectors to DB2 and Informix appear in IBM documentation as generic JDBC connections).
  3. Aspirational products (Spark on Z/OS).
  4. Projects that are not products (Watson Discovery Advisor ran a POC with Spark).
  5. Capabilities that require little or no contribution from IBM (Spark runs under Platform Symphony EGO YARN Service). 

Other than that, it’s a good list.

  • IBM BigInsights  ( Version 4.0 included Spark 1.3.1, version 4.1 includes Spark 1.4.1 – GA’ed August 25th
  • EHAAS (BigInsights on Cloud)  – Includes Spark version 1.3.1 – GA’ed June  2015
  • Analytic for Hadoop – Includes Spark version 1.3.1 – Beta . Will be replaced by Pay-go and include Spark 1.4.1
  • Spark-as-a Service  – Beta in July . GA – Oct. Currently uses Spark version 1.3.1. Will move to Spark 1.4.1
  • Dataworks (Only Cloud) – Beta  – Integrated with Spark Service. Uses Spark Version 1.3.1 
  • SPSS Analytic Server and SPSS Modeler – SPSS Modeler will support Spark version 1.4.1. GA planned for end of Q3, 2015
  • Cloudant  – Cloudant includes Spark Connector. This product is already GA.
  • Omni Channel Pricing – Part of the IBM Commerce Brand. Integrating with Spark 1.4.1. Expected to GA early 2016
  • Dynamic Pricing –  Part of the IBM Commerce Brand. Integrating with Spark 1.4.1. Expected to GA early 2016
  • Mark Down Optimization –   Part of the IBM Commerce Brand. Integrating with Spark 1.4.1. Expected to GA early 2016
  • Nimbus ETL – Part of the IBM Commerce Brand. Integrating with Spark 1.4.1. Expected to GA early 2016
  • Journey Analytics – Part of the IBM Commerce Brand. Integrating with Spark 1.4.1. Expected to GA end of 2015
  • IBM Twitter CDE  On Cloud – Internal only
  • IBM Insights for Twitter Service – On Cloud , Externally Available
  • Internet of Things Real time Analytics on Cloud.  Integrating with Spark 1.3.1  – Open Beta
  • Platform Symphony EGO Service – Integrated with Spark Service for Resource Scheduling and Management. Can also be used with Spark bundled with IBM Open Platform.
  • DB2 Spark Connector
  • Netezza Spark Connector
  • Watson Content Analytics  -> Integrated with Dataworks which is integrating with Spark-as-a-Service
  • Watson Content Services – > Planning to use Spark for Data Ingestion and Enrichment. 
  • Spark on Zlinux  -> Spark enabled on zLinux
  • Spark on ZOS -> Will be available by end of year
  • No response
  • GPFS -GPFS – Spark, as part of BI, runs out of the box on GPFS. In our GPFS Ambari RPM package we changed the Spark service dependency from HDFS to GPFS and added the GPFS Hadoop connector jar to the classpath
  • Informix Connector
  • Watson Discovery Advisor  –  This is a product within Watson Health.  In a small POC that this team has done. they observed that using Scala and Spark , they can reduce their lines of code from 1000s to few hundred lines.” Looking to integrate Spark in early 2016.
  • Cognos – Team indicated that they will be able to submit SPARK SQL queries for getting the results for the data. They would connect to Spark using the JDBC/ODBC driver and then be able to execute Spark SQL queries to generate results for the report . This is planned for 2016.
  • Streams –   Spark MLLib Toolkit for InfoSphere Streams 
  • Watson Research team – Developed a Geospatial RDD on Spark.

Spark in IBM Watson Health Cloud

IBM insiders tell me that Watson relies heavily on Spark.  Okay.  Watson is a completely opaque product, so it’s impossible to verify whether IBM powers Watson with Spark or an army of trained crickets.

SystemML to Open Source

Though initially skeptical about SystemML, as I learn more about this software I’m more excited about its potential.  Rather than simply building an interface to the native Spark machine learning library (MLlib), I understand that IBM has completely rebuilt the algorithms.  That’s a good thing — some of the folks I know who have tried to use MLlib aren’t impressed.  Without getting into the details of the issues, suffice to say that it’s good for Spark to have multiple initiatives building functional libraries on top of the Spark core.

IBM’s Fred Reiss, chief architect at IBM’s Spark Technology Center, is scheduled to present about SystemML at Spark Summit East next week.

Spark in Bluemix

IBM introduced Spark-as-as-Service in Bluemix as beta in July, 2015 and general availability in October.

The service includes Spark, Jupyter Notebooks and SWIFT object storage.  It’s a bit lost in the jumble of services available in Bluemix, but the Catalogue has a handy search tool.

As of this writing, Bluemix offers Spark 1.4.  Although two dot releases behind, that is competitive with Qubole Data Service.  Databricks is still the best bet if you want the most up to date release.

Update: An IBM executive tells me that Bluemix now uses Spark 1.6.  In the meantime, however, IBM has removed the Spark release version from its Bluemix documentation.  

IBM People to Spark Projects

3,500 researchers and developers.  Wow!  That’s a lot of butts in seats.  Let’s break that down into four categories:

(1) IBM people who actively contribute to Spark.

(2) IBM developers building interfaces from IBM products to Spark.

(3) IBM developers building IBM products on top of Spark.

(4) IBM consultants building custom applications on top of Spark.

Note that of the four categories, only (1) actually moves the Spark project forward.  Of course, anyone who uses Spark has the potential to contribute feedback, but ultimately someone has to cut code.  While IBM tracks the Spark JIRAs to which it contributes, IBM executives could not answer a simple question: of the 248 people who contributed to Spark Release 1.6, how many work for IBM?

I suspect that most of those 3,500 researchers and developers are in categories (3) and (4).

Satheesh Bandaram from the IBM Spark Technology Center replies: 26 people from STC contributed to Spark 1.6, with about 80 code commits.

Additional IBM response: Since June 2015. whenIBM announced Spark Technology Center (STC), engineers in STC have actively contributed to Spark releases:  v1.4.x, v1.5.x, v1.6.0, as well as releases v1.6.1 and v.2.0 (in progress.)  

As of today March 2, IBM STC has contributed to over 237 JIRAs and counting.  About 50% are answers to major JIRAs reported in Apache Spark.  

What’s in those 237 contributions…..
·        103 out of 237 (43%) are deliverables in Spark SQL area
·        56 of them (23%) are in MLlib module
·        37 (16%) are in PySpark module  

These top 3 areas of focus from IBM STC made up 82% of the total contributions as of today.   The rest are in the documentation, Spark Core and Streaming etc. modules.

You can track progress onthis live dashboard on github http://jiras.spark.tc/

Specific to Spark 1.6, IBM team members have over 80 commits – Majority of them from STC.  A total of 28 team members contributed to the release (25 of them from STC). Each contributing engineer is a credited contributor in the release notes of Spark 1.6.

For SparkSQL, we contributed:
·        enhancements and fixes in the new DataSet API
·        DataFrame API, Data type
·        UDF and SQL standard compliance, such as adding EXPLAIN and PrintSchema capability, and support coalesce and re-partition etc.
·         We have added support for column datatype of CHAR
·         Fixed the type extractor failures for complex data types
·         Fixed DataFrames bugs in saving long column partitioned parquet file, and handling of various nullability bugs and optimization issues
·        Fixed the limitation in Order by clause to comply with standard.  
·        Contributed to a number of UDF code fixes in completion of Stddev support.  

For Machine Learning, the STC team met with key influencers and stakeholders in the Spark community to jointly work on items on the roadmap Most of the roadmap items discussed went into 1.6.  The implementation of LU Decomposition algorithm is slated for the upcoming release.

In addition to helping implement the roadmap, here are some notable contributions:
·        We greatly improved the Pyspark distributed matrix algebra by enriching the matrix operations and fixing bugs.
·        Enhanced the Word2Vec algorithm.
·        We added optimized first through fourth order summary statistics for DataFrames (technically in SparkSQL, but related to machine learning).
·        We greatly enhanced Pyspark API by adding interfaces to Scala Machine learning tools.
·        We made a performance enhancement to the Linear Data Generator which is critical for unit testing in Spark ML.

The team also addressed major regressions on DataFrame API, enhanced support for Scala 2.11, made enhancements to the Spark History Server, and added JDBC Dialect for Apache Derby.

In addition to the JIRA activities, IBM STC also added the JDBC dialect support for DB2 and made Spark Connector for Netezza v0.1.1 available to public through Spark Packages and a developer blog on IBM external site. 

Spark Training

Like the Million Man March, “training a million people” sounds like one of those PR-driven claims that nobody expects to take seriously, especially since it’s not time-boxed.

Anyway, the details:

  • AMPLab offers occasional training in the complete BDAS stack under the AMPCamp format.  IBM funds AMPLab, but it does not appear that AMPLab is doing anything now that it wasn’t already doing last June.
  • DataCamp does not offer Spark training.
  • MetiStream offers public and private Spark training with a defined curriculum and service offering.  The training program is certified by Databricks.
  • Galvanize does not offer Spark training.
  • Big Data University offers a two-part MOOC in Spark fundamentals.

The Big Data University courses are free, and four hours apiece, so a million enrollees is plausible, eventually at least.  Interestingly, MetiStream developed the second of the two BDU courses.  So the press release should read “MetiStream and IBM, but mostly MetiStream, will train a million….”

Big Analytics Roundup (November 2, 2015)

Spark Summit Europe, Oracle Open World and IBM Insights all met last week, as did Cloudera’s Wrangle conference for data scientists.

But in the really important news, KC beats the Mets to take the Series.

Top news from the Spark Summit is Typesafe’s announcement of Spark support, plus some insight into what’s coming in Spark 1.6.  I will publish a separate roundup for the Spark Summit next week  when presentations are available.

Nine stories this week:

(1) Typesafe Announces Spark Support

Typesafe, the commercial venture behind Scala and Akka, announces commercial support for Apache Spark.   Planned service offerings include an offer of one day business hour response to questions for projects in development.  For production, SLAs range from 4 hour turnaround during business hours up to 24/7 with one hour turnaround.

(2) More Funding for Alteryx

The New York Times reports that Alteryx has landed an $85 million “C” round, led by Iconiq Capital.  That makes a total of $163 million in four rounds for the company.

(3) Oracle Adds Spark to Cloud

At Oracle Open World, Oracle announces Oracle Cloud Platform for Big Data, a PaaS offering;  Dave Ramel covers the story.   Key new bits include automated ingestion, preparation, repair, enrichment and governance, all built in Spark; and a DBaaS offering with Hadoop, Spark and NoSQL data services.

(4) IBM Adds Spark Support to Analytics Server

Full story here.  Great news for those who want to use the high-end version of the second most popular data mining workbench with the third and fourth most popular Hadoop distributions.

(5) Ned Explains Zeppelin

Ned’s Blog provides a nice Zeppelin walk-through, noting the UI’s rich list of language interpreters, which currently includesL HiveQL, Spark, Flink, Postgres, HAWQ, Tajo, AngularJS, Cassandra, Ignite, Phoenix, Geode, Kylin and Lens.

(6) IIT and ANL Deliver BSP with ZHT

Researchers from the Illinois Institute of Technology, Argonne Labs and Hortonworks report that they have implemented a graph processing system based on Bulk Synchronous Processing on ZHT, a distributed key-value store.   Nicole Hemsoth reports.   The new engine, called Pregelix, when benchmarked against Giraph, GraphLab, GraphX and Hama, outshines them all.

(7) Wrangle 2015 Meets in SFO

Cloudera’s Justin Kestelyn summarizes the event, which hosted data science teams from the likes of Uber, Facebook and Airbnb.  Tony Baer offers the trite perspective that data science is about people.

(8) MapR Offers Free Spark Training

MapR announces availability of its first free Apache Spark course as part of its Hadoop On-Demand Training program.  No word on quality, but it’s hard to beat the price.

(9) Cloudera Pushes HUE for Spark

On the Cloudera Engineering blog, Justin Kestelyn explains how to use HUE’s notebook app with SQL and Spark.

IBM Adds Spark Support to Analytics Server

With its customary PR blitz, IBM announces that it has added Spark integration to several products, including SPSS.   IBM gets a small pat on the head for adding Spark support to its Analytics Server software, under the premise that something is better than nothing.

There is a very narrow pool of SPSS users who will benefit from this enhancement.  Spark integration is only available to the subset of SPSS users who license SPSS Modeler; most SPSS users work with SPSS Statistics.  Users must also license SPSS Analytics Server, a product that only runs on Hortonworks HDP or IBM BigInsights.

So, if you’re using the high-end version of the second most popular commercial analytic server, and you’re willing to pay extra to integrate with the third and fourth ranked Hadoop distributions, you’re in luck today.

Analytics Server is a software middle layer installed on Hortonworks or BigInsights; it selectively supports SPSS Modeler operations in Hadoop.  Previous versions ran through MapReduce only;  IBM claims that the latest version runs through Spark when available, although the product documentation is surprisingly quiet on the subject.  There is no reference to Spark in IBM’s Release NotesInstallation Guide or User’s Guide.  Spark is mentioned deep in the Administrator Guide, under Troubleshooting; so the good news is that if the product fails, IBM has some tips — one of which should be “Install Spark.”

Analytics Server 2.1 partially supports most Modeler record and field operations.  Out of Modeler’s 37 data mining nodes, Analytic Server fully supports 8, partially supports 5 and does not support 24.  Among the missing:

  • Logistic Regression
  • k-Means
  • Support Vector Machines
  • PCA
  • Feature Selection
  • Anomaly Detection

Everyone understands that software engineering takes time, but IBM’s priorities are muddled. Logistic regression, k-means, SVM and PCA are all available today in Spark’s open source library; I suspect that IBM figures they can’t justify additional license fees if they point to algorithms that anyone can use for free  (*).  Clustering, PCA, feature selection and anomaly detection are precisely the kind of analyses users want to run on all of the data, not a sample extracted back to a server.

(*) IBM is mistaken on that point, of course.  There are a lot of business users who want the power of Spark but don’t want to mess with a programming API.  These users would happily pay for a nice business user front end like SPSS Modeler, and they won’t care what happens in the back end.

Assuming that this product actually works — not guaranteed, given the sloppy and incomplete documentation — it is better than the previous version of Analytics Server, but that is a low bar.  Spark or no, IBM is way behind SAS in this space; I’m not a great believer in SAS’ proprietary approach to distributed in-memory analytics, but compared to IBM’s offering SAS wins on depth of features and breadth of platform support.  There are no published benchmarks, but I suspect that SAS wins on performance as well.

Also, SAS knows how to write documentation, which seems to be a problem for IBM.

To its credit, IBM’s Analytic Server offers more Spark capability than current offerings by Alpine, Alteryx and RapidMiner; but H2O and Skytree offer richer and better engines for serious machine learning.

As for the majority of SPSS users, wouldn’t it be great if SPSS could just connect to a Spark DataFrame?  Or if Spark could ingest SPSS datasets?

Software for High Performance Advanced Analytics

Strata+Hadoop World week is a good opportunity to update the list of platforms for high-performance advanced analytics.  Vendors are hustling this week to announce their latest enhancements; I’ll post updates as needed.

First some definition.  The scope of this analysis includes software with the following properties:

  • Support for supervised and unsupervised machine learning
  • Support for distributed processing
  • Open platform or multi-vendor platform support
  • Availability of commercial support

There are three main “architectures” for high-performance advanced analytics available today:

  • Integration with an MPP database through table functions
  • Push-down integration with Hadoop
  • Native distributed computing, freestanding or co-located with Hadoop

I’ve written previously about the importance of distributed computing for high-performance predictive analytics, why it’s difficult to deliver and potentially disruptive to the analytics ecosystem.

This analysis excludes software that runs exclusively in a single vendor’s data platform (such as Netezza Analytics, Oracle Advanced Analytics or Teradata Aster‘s built-in analytic functions.)  While each of these vendors seeks to use advanced analytics to differentiate its data warehousing products, most enterprises are unwilling to invest in an analytics architecture that promotes vendor lock-in.  In my opinion, IBM, Oracle and Teradata should consider open sourcing their machine learning libraries, since they’re effectively giving them away anyway.

This analysis also excludes open source libraries “in the wild” (such as Vowpal Wabbit) that lack significant commercial support.

Open Source Software

H2O 

Distributor: H2O.ai (formerly 0xdata)

H20 is an open source distributed in-memory computing platform designed for deployment in Hadoop or free-standing clusters. Current functionality (Release 2.8.4.4) includes Cox Proportional Hazards modeling, Deep Learning, generalized linear models, gradient boosted classification and regression, k-Means clustering, Naive Bayes classifier, principal components analysis, and Random Forests. The software also includes tooling for data transformation, model assessment and scoring.   Users interact with the software through a web interface, a REST API or the h2o package in R.  H2O runs on Spark through the Sparkling Water interface, which includes a new Python API.

H2O.ai provides commercial support for the open source software.  There is a rapidly growing user community for H2O, and H2O.ai cites public reference customers such as Cisco, eBay, Paypal and Nielsen.

MADLib 

Distributor: Pivotal Software

MADLib is an open source machine learning library with a SQL interface that runs in Pivotal Greenplum Database 4.2 or PostgreSQL 9.2+ (as of Release 1.7).  While primarily a captive project of Pivotal Software — most of the top contributors are Pivotal or EMC employees — the support for PostgreSQL qualifies it for this list.    MADLib includes rich analytic functionality, including ten different regression methods, linear systems, matrix factorization, tree-based methods, association rules, clustering, topic modeling, text analysis, time series analysis and dimensionality reduction techniques.

Mahout

Distributor: Apache Software Foundation

Mahout is an eclectic machine learning project incepted in 2011 and currently included in major Hadoop distributions, though it seems to be something of an embarrassment to the community.  The development cadence on Mahout is very slow, as key contributors appear to have abandoned the project three years ago.   Currently (Release 0.9), the project includes twenty algorithms; five of these (including logistic regression and multilayer perceptron) run on a single node only, while the rest run through MapReduce.  To its credit, the Mahout team has cleaned up the software, deprecating unsupported functionality and mandating that all future development will run in Spark.  For Release 1.0, the team has announced plans to deliver several existing algorithms in Spark and H2O, and also to deliver something for Flink (for what that’s worth).  Several commercial vendors, including Predixion Software and RapidMiner leverage Mahout tooling in the back end for their analytic packages, though most are scrambling to rebuild on Spark.

Spark

Distributor: Apache Software Foundation

Spark is currently the platform of choice for open source high-performance advanced analytics.  Spark is a distributed in-memory computing framework with libraries for SQL, machine learning, graph analytics and streaming analytics; currently (Release 1.2) it supports Scala, Python and Java APIs, and the project plans to add an R interface in Release 1.3.  Spark runs either as a free-standing cluster, in AWS EC2, on Apache Mesos or in Hadoop under YARN.

The machine learning library (MLLib) currently (1.2) includes basic statistics, techniques for classification and regression (linear models, Naive Bayes, decision trees, ensembles of trees), alternating least squares for collaborative filtering, k-means clustering, singular value decomposition and principal components analysis for dimension reduction, tools for feature extraction and transformation, plus two optimization primitives for developers.  Thanks to large and growing contributor community, Spark MLLib’s functionality is expanding faster than any other open source or commercial software listed in this article.

For more detail about Spark, see my Apache Spark Page.

Commercial Software

Alpine Chorus

Vendor: Alpine Data Labs

Alpine targets a business user persona with a visual workflow-oriented interface and push-down integration with analytics that run in Hadoop or relational databases.  Although Alpine claims support for all major Hadoop distributions and several MPP databases, in practice most customers seem to use Alpine with Pivotal Greenplum database.  (Alpine and Greenplum have common roots in the EMC ecosystem).   Usability is the product’s key selling point, and the analytic feature set is relatively modest; however, Chorus’ collaboration and data cataloguing capabilities are unique.  Alpine’s customer list is growing; the list does not include a recent win (together with Pivotal) at a large global retailer.

dbLytix

Vendor: Fuzzy Logix

dbLytix is a library of more than eight hundred functions for advanced analytics; analytics run as database table functions and are currently supported in Informix, MySQL, Netezza, ParAccel, SQL Server, Sybase IQ, Teradata Aster and Teradata Database.  Embedded in SQL, analytics may be invoked from a range of application, including custom web interfaces, Microsoft Excel, popular BI tools, SAS or SPSS.   The software is highly extensible, and Fuzzy Logix offers a team of well-qualified consultants and developers for custom applications.

For those seeking the absolute cutting edge in advanced analytics, Fuzzy’s Tanay Zx Series offers more than five hundred analytic functions designed to run on GPU chips.  Tanay is available either as a software library or as an analytic appliance.

IBM SPSS Analytic Server

Vendor: IBM

Analytic Server serves as a Hadoop back end for IBM SPSS Modeler, a mature analytic workbench targeted to business users (licensed separately).  The product, which runs on Apache Hadoop, Cloudera CDH, Hortonworks HDP and IBM BigInsights, enables push-down MapReduce for a limited number of Modeler nodes.  Analytic Server supports most SPSS Modeler data preparation nodes, scoring for twenty-four different modeling methods, and model-building operations for linear models, neural networks and decision trees.  The cadence of enhancements for this product is very slow; first released in May 2013, IBM has released a single maintenance release since then.

RapidMiner Radoop

Vendor: RapidMiner

(Updated for Release 2.2)

RapidMiner targets a business user persona with a “code-free” user interface and deep selection of analytic features.  Last June, the company acquired Radoop, a three-year-old business partner based in Budapest.  Radoop brings to RapidMiner the ability to push down analytic processing into Hadoop using a mix of MapReduce, Mahout, Hive, Pig and Spark operations.

RapidMiner Radoop 2.2 supports more than fifty operators for data transformation, plus the ability to implement custom HiveQL and Pig scripts.  For machine learning, RapidMiner supports k-means, fuzzy k-means and canopy clustering, PCA, correlation and covariance matrices, Naive Bayes classifier and two Spark MLLib algorithms (logistic regression and decision trees); Radoop also supports Hadoop scoring capabilities for any model created in RapidMiner.

Support for Hadoop distributions is excellent, including Cloudera CDH, Hortonworks HDP, Apache Hadoop, MapR, Amazon EMR and Datastax Enterprise.  As of Release 2.2, Radoop supports Kerberos authentication.

Revolution R Enterprise

Vendor: Revolution Analytics

Revolution R Enterprise bundles a number of components, including Revolution R, an enhanced and commercially supported R distribution, a Windows IDE, integration tools and ScaleR, a suite of distributed algorithms for predictive analytics with an R interface.  A little over a year ago, Revolution released its version 7.0, which enables ScaleR to integrate with Hadoop using push-down MapReduce.   The mix of techniques currently supported in Hadoop includes tools for data transformation, descriptive statistics, linear and logistic regression, generalized linear models, decision trees, ensemble models and k-means clustering.   Revolution Analytics supports ScaleR in Cloudera, Hortonworks and MapR; Teradata Database; and in free-standing clusters running on IBM Platform LSF or Windows Server HPC.  Microsoft recently announced that it will acquire Revolution Analytics; this will provide the company with additional resources to develop and enhance the platform.

SAS High Performance Analytics

Vendor: SAS

SAS High Performance Analytics (HPA) is a distributed in-memory analytics engine that runs in Teradata, Greenplum or Oracle appliances, on commodity hardware or co-located in Hadoop (Apache, Cloudera or Hortonworks).  In Hadoop, HPA can be deployed either in a symmetric configuration (SAS instance on each DataNode) or in an asymmetric configuration (SAS deployed on dedicated “Analysis” nodes within the Hadoop cluster.)  While an asymmetric architecture seems less than ideal (due to the need for data movement and shuffling), it reduces the need to upgrade the hardware on every node and reduces SAS software licensing costs.

Functionally, there are five different bundles, for statistics, data mining, text mining, econometrics and optimization; each of these is separately licensed.  End users leverage the algorithms from SAS Enterprise Miner, which is also separately licensed.  Analytic functionality is rich compared to available high-performance alternatives, but existing SAS users will be surprised to see that many techniques available in SAS/STAT are unavailable in HPA.

SAS first introduced HPA in December, 2011 with great fanfare.  To date the product lacks a single public reference customer; this could mean that SAS’ Marketing organization is asleep at the switch, or it could mean that customer success stories with the product are few and far between.  As always with SAS, cost is an issue with prospective customers; other issues cited by customers who have evaluated the product include HPA’s inability to run existing programs developed in Legacy SAS, and concerns about the proprietary architecture. Interestingly, SAS no longer talks up this product in venues like Strata, pointing prospective customers to SAS In-Memory Statistics for Hadoop (see below) instead.

SAS In-Memory Statistics for Hadoop

Vendor: SAS

SAS In-Memory Statistics for Hadoop (IMSH) is an analytics application that runs on SAS’ “other” distributed in-memory architecture (SAS LASR Server).  Why does SAS have two in-memory architectures?  Good luck getting SAS to explain that in a coherent manner.  The best explanation, so far as I can tell, is a “mud-on-the-wall” approach to new product development.

Functionally, IMSH Release 2.5 supports data prep with SAS DS2 (an object-oriented language), descriptive statistics, classification and regression trees (C4.5), forecasting, general and generalized linear models, logistic regression, a Random Forests lookalike, clustering, association rule mining, text mining and a recommendation system.   Users interact with the product through SAS Studio, a web-based IDE introduced in SAS 9.4.

Overall, IMSH is a better value than HPA.  SAS prices this software based on the number of cores in the servers upon which it is deployed; while I can’t disclose the list price per core, it’s fair to say that any configuration beyond a sandbox will rapidly approach seven figures for the first year fee.

Skytree

Product: Skytree Infinity

Skytree began life as an academic machine learning project (FastLab, at Georgia Tech); the developers shopped the distributed machine learning core to a number of vendors and, finding no buyers, launched as a commercial software vendor in January 2013.  Recently rebranded from Skytree Server to Skytree Infinity, the product now includes modules for data marshaling and preparation that run on Spark.  Distributed algorithms can run as a free-standing cluster or co-located in Hadoop under YARN.  The product has a programming interface; the vendor claims ability to run from R, Weka, C++ and Python.   Neither Skytree’s modest list of algorithms nor its short list of public reference customers has changed in the past two years.

Distributed Analytics: A Primer

Can we leverage distributed computing for machine learning and predictive analytics? The question keeps surfacing in different contexts, so I thought I’d take a few minutes to write an overview of the topic.

The question is important for four reasons:

  • Source data for analytics frequently resides in distributed data platforms, such as MPP appliances or Hadoop;
  • In many cases, the volume of data needed for analysis is too large to fit into memory on a single machine;
  • Growing computational volume and complexity requires more throughput than we can achieve with single-threaded processing;
  • Vendors make misleading claims about distributed analytics in the platforms they promote.

First, a quick definition of terms.  We use the term parallel computing to mean the general practice of dividing a task into smaller units and performing them in parallel; multi-threaded processing means the ability of a software program to run multiple threads (where resources are available); and distributed computing means the ability to spread processing across multiple physical or virtual machines.

The principal benefit of parallel computing is speed and scalability; if it takes a worker one hour to make one hundred widgets, one hundred workers can make ten thousand widgets in an hour (ceteris paribus, as economists like to say).  Multi-threaded processing is better than single-threaded processing, but shared memory and machine architecture impose a constraint on potential speedup and scalability.  In principle, distributed computing can scale out without limit.

The ability to parallelize a task is inherent in the definition of the task itself.  Some tasks are easy to parallelize, because computations performed by each worker are independent of all other workers, and the desired result set is a simple combination of the results from each worker; we call these tasks embarrassingly parallel.   A SQL Select query is embarrassingly parallel; so is model scoring; so are many of the tasks in a text mining process, such as word filtering and stemming.

A second class of tasks requires a little more effort to parallelize.  For these tasks, computations performed by each worker are independent of all other workers, and the desired result set is a linear combination of the results from each worker.  For example, we can parallelize computation of the mean of a distributed database by computing the mean and row count independently for each worker, then compute the grand mean as the weighted mean of the worker means.  We call these tasks linear parallel.

There is a third class of tasks, which is harder to parallelize because the data must be organized in a meaningful way.  We call a task data parallel if computations performed by each worker are independent of all other workers so long as each worker has a “meaningful” chunk of the data.  For example, suppose that we want to build independent time series forecasts for each of three hundred retail stores, and our model includes no cross-effects among stores; if we can organize the data so that each worker has all of the data for one and only one store, the problem will be embarrassingly parallel and we can distribute computing to as many as three hundred workers.

While data parallel problems may seem to be a natural application for processing inside an MPP database or Hadoop, there are two constraints to consider.  For a task to be data parallel, the data must be organized in chunks that align with the business problem.  Data stored in distributed databases rarely meets this requirement, so the data must be shuffled and reorganized prior to analytic processing, a process that adds latency.  The second constraint is that the optimal number of workers depends on the problem; in the retail forecasting problem cited above, the optimal number of workers is three hundred.  This rarely aligns with the number of nodes in a distributed database or Hadoop cluster.

There is no generally agreed label for tasks that are the opposite of embarrassingly parallel; for convenience, I use the term orthogonal to describe a task that cannot be parallelized at all.  In analytics, case-based reasoning is the best example of this, as the method works by examining individual cases in a sequence.  Most machine learning and predictive analytics algorithms fall into a middle ground of complex parallelism; it is possible to divide the data into “chunks” for processing by distributed workers, but workers must communicate with one another, multiple iterations may be required and the desired result is a complex combination of results from individual workers.

Software for complex machine learning tasks must be expressly designed and coded to support distributed processing.  While it is physically possible to install open source R or Python in a distributed environment (such as Hadoop), machine learning packages for these languages run locally on each node in the cluster.  For example, if you install open source R on each node in a twenty-four node Hadoop cluster and try to run logistic regression you will end up with twenty-four logistic regression models developed separately for each node.  You may be able to use those results in some way, but you will have to program the combination yourself.

Legacy commercial tools for advanced analytics provide only limited support for parallel and distributed processing.  SAS has more than 300 procedures in its legacy Base and STAT software packages; only a handful of these support multi-threaded (SMP) operations on a single machine;  nine PROCs can support distributed processing (but only if the customer licenses an additional product, SAS High-Performance Statistics).  IBM SPSS Modeler Server supports multi-threaded processing but not distributed processing; the same is true for Statistica.

The table below shows currently available distributed platforms for predictive analytics; the table is complete as of this writing (to the best of my knowledge).

Distributed Analytics Software, May 2014

Several observations about the contents of this table:

(1) There is currently no software for distributed analytics that runs on all distributed platforms.

(2) SAS can deploy its proprietary framework on a number of different platforms, but it is co-located and does not run inside MPP databases.  Although SAS claims to support HPA in Hadoop, it seems to have some difficulty executing on this claim, and is unable to describe even generic customer success stories.

(3) Some products, such as Netezza and Oracle, aren’t portable at all.

(4) In theory, MADLib should run in any SQL environment, but Pivotal database appears to be the primary platform.

To summarize key points:

— The ability to parallelize a task is inherent in the definition of the task itself.

— Most “learning” tasks in advanced analytics tasks are not embarrassingly parallel.

— Running a piece of software on a distributed platform is not the same as running it in distributed mode.  Unless the software is expressly written to support distributed processing, it will run locally, and the user will have to figure out how to combine the results from distributed workers.

Vendors who claim that their distributed data platform can perform advanced analytics with open source R or Python packages without extra programming are confusing predictive model “learning” with simpler tasks, such as scoring or SQL queries.