Fuzzy Logix

The Year in Machine Learning (Part Four)

This is the fourth installment in a four-part review of 2016 in machine learning and deep learning.

— Part One covered Top Trends in the field, including concerns about bias, interpretability, deep learning’s explosive growth, the democratization of supercomputing, and the emergence of cloud machine learning platforms.

— Part Two surveyed significant developments in Open Source machine learning projects, such as R, Python, Spark, Flink, H2O, TensorFlow, and others.

— Part Three reviewed the machine learning and deep learning initiatives of Big Tech Brands, industry leaders with significant budgets for software development and marketing.

In Part Four, I profile eleven startups in the machine learning and deep learning space. A search for “machine learning” in Crunchbase yields 2,264 companies. This includes companies, such as MemSQL, who offer absolutely no machine learning capability but hype it anyway because Marketing; it also includes application software and service providers, such as Zebra Medical Imaging, who build machine learning into the services they provide.

All of the companies profiled in this post provide machine learning tools as software or services for data scientists or for business users. Within that broad definition, the firms are highly diverse:

— Continuum Analytics, Databricks, and H2O.ai drive open source projects (Anaconda, Apache Spark, and H2O, respectively) and deliver commercial support.

— Alpine Data, Dataiku, and Domino Data Lab offer commercially licensed collaboration tools for data science teams. All three run on top of an open source platform.

— KNIME and RapidMiner originated in Europe, where they have large user communities. Both combine a business user interface with the ability to work with Big Data platforms.

— Fuzzy Logix and Skytree provide specialized capabilities primarily for data scientists.

— DataRobot delivers a fully automated workflow for predictive analytics that appeals to data scientists and business users. It runs on an open source platform.

Four companies deserve an “honorable mention” but I haven’t profiled them in depth:

— Two startups, BigML and SkyMind, are still in seed funding stage. I don’t profile them below, but they are worth watching. BigML is a cloud-based machine learning service; SkyMind drives the DL4J open source project for deep learning.

— Two additional companies aren’t startups because they’ve been in business for more than thirty years. Salford Systems developed the original software for CART and Random Forests; the company has added more techniques to its suite over time and has a loyal following. Statistica, recently jettisoned by Dell, delivers a statistical package with broad capabilities; the company consistently performs well in user satisfaction surveys.

I’d like to take a moment to thank those who contributed tips and ideas for this series, including Sri Ambati, Betty Candel, Leslie Miller, Bob Muenchen, Thomas Ott, Peter Prettenhofer, Jesus Puente, Dan Putler, David Smith, and Oliver Vagner.

Alpine Data

In 2016, the company formerly known as Alpine Data Labs changed its name and CEO. Alpine dropped the “Labs” from its brand — I guess they didn’t want to be confused with companies that test stool samples — so now it’s just Alpine Data. And, ex-CEO Joe Otto is now an “Advisor,” replaced by Dan Udoutch, a “seasoned executive” with 30+ years of experience in business and zero years of experience in machine learning or advanced analytics. The company also dropped its CFO and head of Sales during the year, presumably because the investors were extremely happy with Alpine’s business results.

Originally built to run in Greenplum database, the company ported some of its algorithms to MapReduce in early 2013. Riding a wave of Hadoop buzz, Alpine closed on a venture round in November 2013, just in time for everyone to realize that MapReduce sucks for machine learning. The company quickly turned to Spark — Databricks certified Alpine on Spark in 2014 — and has gradually ported its analytics operators to the new framework.

It seems that rebuilding on Spark has been a bit of a slog because Alpine hasn’t raised a fresh round of capital since 2013. As a general rule, startups that make their numbers get fresh rounds every 12-24 months; companies that don’t get fresh funding likely aren’t making their numbers. Investors aren’t stupid and, like the dog that did not bark, a venture capital round that does not happen says a lot about a company’s prospects.

In product news, the company announced Chorus 6, a major release, in May, and Chorus 6.1 in September. Enhancements in the new releases include:

— Integration with Jupyter notebooks.

— Additional machine learning operators.

— Spark auto-tuning. Chorus pushes processing to Spark, and Alpine has developed an optimizer to tune the generated Spark code.

— PFA support for model export. This is excellent, a cutting edge feature.

— Runtime performance improvements.

— Tweaks to the user experience.

Lawrence Spracklen, Alpine’s VP of Engineering, will speak about Spark auto-tuning at the Spark Summit East in Boston.

Prospective users and customers should look for evidence that Alpine is a viable company, such as a new funding round, or audited financials that show positive cash flow.

Continuum Analytics

Continuum Analytics develops and supports Anaconda, an open source Python distribution for data science. The core Anaconda bundle includes Navigator, a desktop GUI that manages applications, packages, environments and channels; 150 Python packages that are widely used in data science; and performance optimizations. Continuum also offers commercially licensed extensions to Anaconda for scalability, high performance and ease of use.

Anaconda 2.5, announced in February, introduced performance optimization with the Intel® Math Kernel Library. Beginning with this release, Continuum bundled Anaconda with Microsoft R Open, an enhanced free R distribution.

In 2016, Continuum introduced two major additions to the Anaconda platform:

— Anaconda Enterprise Notebooks, an enhanced version of Jupyter notebooks

— Anaconda Mosaic, a tool for cataloging heterogeneous data

The company also announced partnerships with Cloudera, Intel, and IBM. In September, Continuum disclosed $4 million in equity financing. The company was surprisingly quiet about the round — there was no press release — possibly because it was undersubscribed.

Continuum’s AnacondaCon 2017 conference meets in Austin February 7-9.

Databricks

Databricks leads the development of Apache Spark (profiled in Part Two of this review) and offers a cloud-based managed service built on Spark. The company also offers training, certification, and organizes the Spark Summits.

The team that originally developed Spark founded Databricks in 2013. Company employees continue to play a key role in Apache Spark, holding a plurality of the seats on the Project Management Committee and contributing more new code to the project than any other company.

In 2016, Databricks added a dashboarding tool and a RESTful interface for job and cluster management to its core managed service. The company made major enhancements to the Databricks security framework, completed SOC 2 Type 1 certification for enterprise security, announced HIPAA compliance and availability in Amazon Web Services’ GovCloud for sensitive data and regulated workloads.

Databricks also launched a free Community edition; a five-part series of free MOOCs; completed its annual survey of the Spark user community, and organized three Spark Summits.

In December, Databricks announced a $60 million “C” round of venture capital. New Enterprise Associates led the round; Andreessen Horowitz participated.

Dataiku

Dataiku develops and markets Data Science Studio (DSS), a workflow and collaboration environment for machine learning and advanced analytics. Users interact with the software through a drag-and-drop interface; DSS pushes processing down to Hadoop and Spark. The product includes connectors to a wide variety of file systems, SQL platforms, cloud data stores and NoSQL databases.

In 2016, Dataiku delivered Releases 3.0 and 3.1. Major new capabilities include H2O integration (through Sparkling Water); additional data sources (IBM Netezza, SAP HANA, Google BigQuery, and Microsoft Azure Data Warehouse); added support for Spark MLLib algorithms; performance improvements, and many other enhancements.

In October, Dataiku closed on a $14 million “A” round of venture capital. FirstMark Capital led the financing, with participation from Serena Capital.

DataRobot

DataRobot, a Boston-based startup founded by insurance industry veterans, offers an automated machine learning platform that combines built-in expertise with a test-and-learn approach. Leveraging an open source back end, the company’s eponymous software searches through combinations of algorithms, pre-processing steps, features, transformations and tuning parameters to identify the best model for a particular problem.

The company has a team of Kaggle-winning data scientists and leverages this expertise to identify new machine learning algorithms, feature engineering techniques, and optimization methods. In 2016, DataRobot added several new capabilities to its product, including support for Hadoop deployment, deep learning with TensorFlow, reason codes that explain prediction, feature impact analysis, and additional capabilities for model deployment.

DataRobot also announced major alliances with Alteryx and Cloudera. Cloudera awarded the company its top-level certification: the software integrates with Spark, YARN, Cloudera Service Descriptors, and Cloudera Parcels.

Earlier in the year, DataRobot closed on $33 million in Series B financing. New Enterprise Associates led the round; Accomplice, Intel Capital, IA Ventures, Recruit Strategic Partners, and New York Life also participated.

Domino Data Lab

Domino Data Lab offers the Domino Data Science Platform (DDSP) a scalable collaboration environment that runs on-premises, in virtual private clouds or hosted on Domino’s AWS infrastructure.

DDSP provides data scientists with a shared environment for managing projects, scalable computing with a variety of open source and commercially licensed software, job scheduling and tracking, and publication through Shiny and Flask. Domino supports rollbacks, revision history, version control, and reproducibility.

In November, Domino announced that it closed a $10.5 million “A” round led by Sequoia Capital. Bloomberg Beta, In-Q-Tel, and Zetta Venture Partners also participated.

Fuzzy Logix markets DB Lytix, a library of more than eight hundred functions for machine learning and advanced analytics. Functions run as database table functions in relational databases (Informix, MySQL, Netezza, ParAccel, SQL Server, Sybase IQ, Teradata Aster and Teradata Database) and in Hadoop through Hive.

Users invoke DB Lytix functions from SQL, R, through BI tools or from custom web interfaces. Functions support a broad range of machine learning capabilities, including feature engineering, model training with a rich mix of supported algorithms, plus simulation and Monte Carlo analysis. All functions support native in-database scoring. The software is highly extensible, and Fuzzy Logix offers a team of well-qualified consultants and developers for custom applications.

In April, the company announced the availability of DB Lytix on Teradata Aster Analytics, a development that excited all three of the people who think Aster has legs.

H2O.ai

H2O.ai develops and supports H2O, the open source machine learning project I profiled in Part Two of this review. As I noted in Part Two, H2O.ai updated Sparkling Water, its Spark integration for Spark 2.0; released Steam, a model deployment framework, to production, and previewed Deep Water, an interface to GPU-accelerated back ends for deep learning.

In 2016, H2O.ai added 3,200 enterprise organizations and over 43,000 users to its roster, bringing its open source community to over 8,000 enterprises and nearly 70,000 users worldwide. In the annual KDnuggets poll of data scientists, reported usage tripled. New customers include Kaiser Permanente, Progressive, Comcast, HCA, McKesson, Macy’s, and eBay.

KNIME

KNIME.com AG, a commercial enterprise based in Zurich, Switzerland, distributes the KNIME Analytics Platform under a GPL license with an exception permitting third parties to use the API for proprietary extensions. The KNIME Analytics Platform features a graphical user interface with a workflow metaphor. Users build pipelines of tasks with drag-and-drop tools and run them interactively or in batch.

KNIME offers commercially licensed extensions for scalability, integration with data platforms, collaboration, and productivity. The company provides technical support for the extension software.

During the year, KNIME delivered two dot releases and three maintenance releases. The new features added to the open source edition in Releases 3.2 and 3.3 include Workflow Coach, a recommender based on community usage statistics; streaming execution; feature selection; ensembles of trees and gradient boosted trees; deep learning with DL4J, and many other enhancements. In June, KNIME launched the KNIME Cloud Analytics Platform on Microsoft Azure.

KNIME held its first Summit in the United States in September and announced the availability of an online training course available through O’Reilly Media.

RapidMiner

RapidMiner, Inc. of Cambridge, Massachusetts, develops and supports RapidMiner, an easy-to-use package for business analysis, predictive analytics, and optimization. The company launched in 2006 (under the corporate name of Rapid-I) to drive development, support, and distribution for the RapidMiner software project. The company moved its headquarters to the United States in 2013.

The desktop version of the software, branded as RapidMiner Studio, is available in free and commercially licensed editions. RapidMiner also offers a commercially licensed Server edition, and Radoop, an extension that pushes processing down to Hive, Pig, Spark, and H2O.

RapidMiner introduced Release 7.x in 2016 with an updated user interface. Other enhancements in Releases 7.0 through 7.3 include a new data import facility, Tableau integration, parallel cross-validation, and H2O integration (featuring deep learning, gradient boosted trees and generalized linear models).

The company also introduced a feature called Single Process Pushdown. This capability enables RapidMiner users to supplement native Spark and H2O algorithms with RapidMiner pipelines for execution in Hadoop. RapidMiner supports Spark 2.0 as of Release 7.3.

In January 2016, RapidMiner closed a $16 million equity round led by Nokia Growth Partners. Ascent Venture Partners, Earlybird Venture Capital, Longworth Venture Partners, and OpenOcean also participated.

Skytree

Skytree Inc. develops and markets an eponymous commercially licensed software package for machine learning. Its founders launched the venture in 2012 to monetize an academic machine learning project (Georgia Tech’s FastLab).

The company landed an $18 million venture capital round in 2013 and hasn’t secured any new funding since then. (Read my comments under Alpine Data to see what that indicates.) Moreover, the underlying set of algorithms does not seem to have changed much since then, though Skytree has added and dropped several different add-ons and wrappers.

Users interact with the software through the Skytree Command Line Interface (CLI), Java and Python APIs or a browser-based GUI. Output includes explanations of the model in plain English. Skytree has a grid search feature for parameterization, which it trademarks as AutoModel, labels as “ground-breaking” and is attempting to patent. Analysts who don’t know anything about grid search think this is amazing.

In 2016, Skytree introduced a freemium edition, branded as Skytree Express. Hold out another six months and they’ll pay you to try it.

As is the case with Alpine Data, if you like Skytree’s technology wait for another funding round, or ask the company to provide evidence of positive cash flow.

Big Analytics Roundup (November 9, 2015)

My roundup of the Spark Summit Europe is here.

Two important events this week:

H2O World starts today and runs through Wednesday at the Computer History Museum in Mountain View CA. Yotam Levy summarizes here and here.
Open Data Science Conference meets November 14-15 at the Marriott Waterfront in SFO

Five backgrounders and explainers:

At HUG London, Apache’s Ufuk Celebi delivers a nice intro to Flink.
On the Databricks blog, Yesware’s Justin Mills explains how his team migrates Spark applications from concept through prototype through production.
On Slideshare, Alpine’s Holden Karau delivers an overview of Spark with Python.
Chloe Green wakes from a three year slumber and discovers Spark.
On the Cloudera Engineering blog, Madhu Ganta explains how to build a CEP app with Spark and Drools.

Third quarter financials drive the news:

(1) MapR: We Grew 160% in Q3

MapR posts its biggest quarter ever.

(2) HDP: We Grew 168% in Q3

HDP loses $1.33 on every dollar sold, tries to make it up on volume. Stock craters.

(3) Teradata: We Got A Box of Steak Knives in Q3

Teradata reports more disappointing sales as customers continue to defer investments in big box solutions for data warehousing. This is getting to be a habit with Teradata; the company missed revenue projections for 2014 as well as the first and second quarters of this year. Any company can run into headwinds, but a management team that consistently misses targets clearly does not understand its own business and needs to go.

Full report here.

(4) “B” Round for H2O.ai

Machine learning software developer H2O.ai announces a $20 million Series B round led by Paxion Capital Partners. H2O.ai leads development of H2O, an open source project for distributed in-memory machine learning. The company reports 25 new support customers this year.

(5) Fuzzy Logix Lands Funds

In-database analytics vendor Fuzzy Logix announces a $5 million “A” round from New Science Ventures. Fuzzy offers a library of analytic functions that run in a number of high-performance databases and in HiveQL.

(6) New Optimization Package for Spark

On the Databricks blog, Aaron Staple announces availability of Spark TFOCS, an optimization package based on the eponymous Matlab package. (TFOCS=Templates for First Order Conic Solvers.)

(7) WSO2 Delivers IoT App on Spark

IoT middleware vendor WSO2 announces Release 3.0 of its open source Data Analytics Server (DAS) platform. DAS collects data streams and applies batch, real-tim or interactive analytics; predictive analytics are in the roadmap. For streaming data sources, DAS supports java agents, javascript clients and 100+ connectors. The software runs on Spark and Lucene.

(8) Hortonworks: We Aren’t Irrelevant

On the Hortonworks blog, Vinay Shukla and Ram Sriharsha tout Hortonworks’ contributions to Spark, including ORC support, an Ambari stack definition for Spark, tighter integration between Hive and Spark, minor enhancements to ML and user-facing documentation. Looking at the roadmap, they discuss Magellan for geospatial and Zeppelin notebooks. (h/t Hadoop Weekly).

(9) Apache Drill Delivers Fast SQL-on-Laptop

On the MapR blog, Mitsutoshi Kiuchi offers a case study in how to run a silly benchmark.

Comparing the functionality of Drill and Spark SQL, Kiuchi argues that Drill “supports” NoSQL databases but Spark does not, relegating Spark’s packages to a footnote. “Support” is a loaded word with open source software; technically, nothing is supported unless you pay for it, in which case the scope of support is negotiated as part of the SLA. It’s also worth noting that MongoDB developed Spark’s interface to MongoDB (for example), which provides a certain amount of confidence.

Kiuchi does not consider other functional areas, such as security, YARN support, query fault tolerance, the user interface, metastore management and view support, where Drill comes up short.

In a previously published performance test of five SQL engines, Spark successfully ran nine out of eleven queries, while Drill ran eight out of ten. On the eight queries both engines ran, Drill was slightly faster on six. For this benchmark, Kiuchi runs three queries on his laptop with a tiny dataset.

As a general rule, one should ignore SQL-on-Hadoop benchmarks unless they run industry standard queries (e.g. TPC) with large datasets in a distributed configuration.

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.