Spark is the Future of Analytics

At the 2016 Spark Summit, Gartner Research Director Nick Heudecker asked: Is Spark the Future of Data Analysis?  It’s an interesting question, and it requires a little parsing. Nobody believes that Spark alone is the future of data analysis, even its most ardent proponents. A better way to frame the question: Does Spark have a role in the future of analytics? What is that role?

Unfortunately, Heudecker didn’t address the question but spent the hour throwing shade at Spark.

Spark is overhyped! He declared. His evidence? This:

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One might question an analysis that equates real things like optimization with fake things like “Citizen Data Science.” Gartner’s Hype Cycle by itself proves nothing; it’s a conceptual salad, with neither empirical foundation nor predictive power.

If you want to argue that Spark is overhyped, produce some false or misleading claims by project principals, or documented cases where the software failed to work as claimed. It’s possible that such cases exist. Personally, I don’t know of any, and neither does Nick Heudecker, or he would have included them in his presentation.

Instead, he cited a Gartner survey showing that organizations don’t use Spark and Flink as much as they use other tools for data analysis. From my notes, here are the percentages:

  • EDW: 57%
  • Cloud: 44%
  • Hadoop: 42%
  • Stat Packages: 32%
  • Spark or Flink: 9%
  • Graph Databases: 8%

That 42% figure for Hadoop is interesting. In 2015, Gartner concern-trolled the tech community, trumpeting the finding that “only” 26% of respondents in a survey said they were “deploying, piloting or experimenting with Hadoop.” So — either Hadoop adoption grew from 26% to 42% in a year, or Gartner doesn’t know how to do surveys.

In any event, it’s irrelevant; statistical packages have been available for 40 years, EDWs for 25, Spark for 3. The current rate of adoption for a project in its youth tells you very little about its future. It’s like arguing that a toddler is cognitively challenged because she can’t do integral calculus without checking the Wolfram app on her iPad.

Heudecker closed his presentation with the pronouncement that he had no idea whether or not Spark is the future of data analysis, and bolted the venue faster than a jackrabbit on Ecstasy. Which begs the question: why pay big bucks for analysts who have no opinion about one of the most active projects in the Big Data ecosystem?

Here are eight reasons why Spark has a central role in the future of analytics.

(1) Nearly everyone who uses Hadoop will use Spark.

If you believe that 42% of enterprises use Hadoop, you must believe that 41.9% will use Spark. Every Hadoop distribution includes Spark. Hive and Pig run on Spark. Hadoop early adopters will gradually replace existing MapReduce applications and build most new applications in Spark. Late adopters may never use MapReduce.

The only holdouts for MapReduce will be those who want their analysis the way they want their barbecue: low and slow.

Of course, Hadoop adoption isn’t static. Forrester’s Mike Gualtieri argues that 100% of enterprises will use Hadoop within a few years.

(2) Lots of people who don’t use Hadoop will use Spark.

For Hadoop users, Spark is a fast replacement for MapReduce. But that’s not all it is. Spark is also a general-purpose data processing environment for advanced analytics. Hadoop has baggage that data science teams don’t need, so it’s no surprise to see that most Spark users aren’t using it with Hadoop. One of the key advantages of Spark is that users aren’t tied to a particular storage back end, but can choose from many different options. That’s essential in real-world data science.

(3) For scalable open source data science, Spark is the only game in town.

If you want to argue that Spark has no future, you’re going to have to name an alternative. I’ll give you a minute to think of something.

Time’s up.

You could try to approximate Spark’s capabilities with a collection of other projects: for example, you could use Presto for SQL, H2O for machine learning, Storm for streaming, and Giraph for graph analysis. Good luck pulling those together. H2O.ai was one of the first vendors to build an interface to Spark because even if you want to use H2O for machine learning, you’re still going to use Spark for data wrangling.

“What about Flink?” you ask. Well, what about it? Flink may have a future, too, if anyone ever supports it other than ten guys in a loft on the Tempelhofer Ufer. Flink’s event-based runtime seems well-suited for “pure” streaming applications, but that’s low-value bottom-of-the-stack stuff. Flink’s ML library is still pretty limited, and improving it doesn’t appear to be a high priority for the Flink team.

(4) Data scientists who work exclusively with “small data” still need Spark.

Data scientists satisfy most business requests for insight with small datasets that can fit into memory on a single machine. Even if you measure your largest dataset in gigabytes, however, there are two ways you need Spark: to create your analysis dataset and to parallelize operations.

Your analysis dataset may be small, but it comes from a larger pool of enterprise data. Unless you have servants to pull data for you, at some point you’re going to have to get your hands dirty and deal with data at enterprise scale. If you are lucky, your organization has nice clean data in a well-organized data warehouse that has everything anyone will ever need in a single source of truth.

Ha ha! Just kidding. Single sources of truth don’t exist, except in the wildest fantasies of data warehouse vendors. In reality, you’re going to muck around with many different sources and integrate your analysis data on the fly. Spark excels at that.

For best results, machine learning projects require hundreds of experiments to identify the best algorithm and optimal parameters. If you run those tests serially, it will take forever; distribute them across a Spark cluster, and you can radically reduce the time needed to find that optimal model.

(5) The Spark team isn’t resting on its laurels.

Over time, Spark has evolved from a research project for scalable machine learning to a general purpose data processing framework. Driven by user feedback, Spark has added SQL and streaming capabilities, introduced Python and R APIs, re-engineered the machine learning libraries, and many other enhancements.

Here are some projects under way to improve Spark:

— Project Tungsten, an ongoing effort to optimize CPU and memory utilization.

— A stable serialization format (possibly Apache Arrow) for external code integration.

— Integration with deep learning frameworks, including TensorFlow and Intel’s new BigDL library.

— A cost-based optimizer for Spark SQL.

— Improved interfaces to data sources.

— Continuing improvements to the Python and R APIs.

Performance improvement is an ongoing mission; for selected operations, Spark 2.0 runs 10X faster than Spark 1.6.

(6) More cool stuff is on the way.

Berkeley’s AMPLab, the source of Spark, Mesos, and Tachyon/Alluxio, is now RISELab. There are four projects under way at RISELab that will extend Spark capabilities:

Clipper is a prediction serving system that brokers between machine learning frameworks and end-user applications. The first Alpha release, planned for mid-April 2017, will serve scikit-learn, Spark ML and Spark MLLib models, and arbitrary Python functions.

Drizzle, an execution engine for Apache Spark, uses group scheduling to reduce latency in streaming and iterative operations. Lead developer Shivaram Venkataraman has filed a design document to implement this approach in Spark.

Opaque is a package for Spark SQL that uses Intel SGX trusted hardware to deliver strong security for DataFrames. The project seeks to enable analytics on sensitive data in an untrusted cloud, with data encryption and access pattern hiding.

Ray is a distributed execution engine for Spark designed for reinforcement learning.

Three Apache projects in the Incubator build on Spark:

— Apache Hivemall is a scalable machine learning library implemented as a collection of Hive UDFs designed to run on Hive, Pig or Spark SQL with MapReduce, Tez or Spark.

— Apache PredictionIO is a machine learning server built on top of an open source stack, including Spark, HBase, Spray, and Elasticsearch.

— Apache SystemML is a library of machine learning algorithms that run on Spark and MapReduce, originally developed by IBM Research.

MIT’s CSAIL lab is working on ModelDB, a system to manage machine learning models. ModelDB extracts and stores model artifacts and metadata, and makes this data available for easy querying and visualization. The current release supports Spark ML and scikit-learn.

(7) Commercial vendors are building on top of Spark.

The future of analytics is a hybrid stack, with open source at the bottom and commercial software for business users at the top. Here is a small sample of vendors who are building easy-to-use interfaces atop Spark.

Alpine Data provides a collaboration environment for data science and machine learning that runs on Spark (and other platforms.)

AtScale, an OLAP on Big Data solution, leverages Spark SQL and other SQL engines, including Hive, Impala, and Presto.

Dataiku markets Data Science Studio, a drag-and-drop data science workflow tool with connectors for many different storage platforms, scikit-learn, Spark ML and XGboost.

StreamAnalytix, a drag-and-drop platform for real-time analytics, supports Spark SQL and Spark Streaming, Apache Storm, and many different data sources and sinks.

Zoomdata, an early adopter of Spark, offers an agile visualization tool that works with Spark Streaming and many other platforms.

All of the leading agile BI tools, including Tableau, Qlik, and PowerBI, support Spark. Even stodgy old Oracle’s Big Data Discovery tool runs on Spark in Oracle Cloud.

(8) All of the leading commercial advanced analytics platforms use Spark.

All of them, including SAS, a company that embraces open source the way Sylvester the Cat embraces a skunk. SAS supports Spark in SAS Data Loader for Hadoop, one of SAS’ five different Hadoop architectures. (If you don’t like SAS architecture, wait six months for another.)

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Magic Quadrant for Advanced Analytics Platforms, 2016

— IBM embraces Spark like Romeo embraced Juliet, hopefully with a better ending. IBM contributes heavily to the Spark project and has rebuilt many of its software products and cloud services to use Spark.

— KNIME’s Spark Executor enables users of the KNIME Analytics Platform to create and execute Spark applications. Through a combination of visual programming and scripting, users can leverage Spark to access data sources, blend data, train predictive models, score new data, and embed Spark applications in a KNIME workflow.

— RapidMiner’s Radoop module supports visual programming across SparkR, PySpark, Pig, and HiveQL, and machine learning with SparkML and H2O.

— Statistica, which is no longer part of Dell, offers Spark integration in its Expert and Enterprise editions.

— Microsoft supports Spark in AzureHD, and it has rebuilt Microsoft R Server’s Hadoop integration to leverage Spark as well as MapReduce. VentureBeat reports that Databricks will offer its managed service for Spark on Microsoft Azure later this year.

— SAP, another early adopter of Spark, supports Vora, a connector to SAP HANA.

You get the idea. Spark is deeply embedded in the ecosystem, and it’s foolish to argue that it doesn’t play a central role in the future of analytics.

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.

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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.

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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.

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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.

dataiku

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.

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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.

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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

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.

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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.

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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.

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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).

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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 (August 15, 2016)

In the second quarter of 2015, Hortonworks lost $1.38 for every dollar of revenue. In the second quarter of 2016, HDP lost $1.46 for every dollar of revenue. So I guess they aren’t making it up on volume.

On the Databricks blog, Jules Damji summarizes Spark news from the past two weeks.

AWS Launches Kinesis Analytics

Amazon Web Services announces the availability of Amazon Kinesis Analytics, an SQL interface to streaming data. AWS’ Ryan Nienhuis explains how to use it in the first of a two-part series.

The biggest threat to Spark Streaming doesn’t come from the likes of Flink, Storm, Samza or Apex. It comes from popular message brokers like Apache Kafka and AWS Kinesis, who can and will add analytics to move up the value chain.

Intel Freaks Out

Intel announces an agreement to acquire Nervana Systems, a 28-month-old startup working on hardware and software solutions for deep learning. Re/code reports a price tag of $408 million. The customary tech media unicorn story storm ensues. (h/t Oliver Vagner)

Intel says it plans to use Nervana’s software to improve the Math Kernel Library and market the Nervana Engine alongside the Xeon Phi processor. Nervana neon is YADLF — Yet Another Deep Learning Framework — that ranked twelfth in usage among deep learning frameworks in KDnuggets’ recent poll. According to Nervana, neon benchmarks well against Caffe; but then, so does CNTK.

Do special-purpose chips for deep learning have legs? Obviously, Intel thinks so. The headline on that recent Wired story about Google’s deep learning chip — Time for Intel to Freak Out — looks prescient. That said, the history of computing isn’t kind to special-purpose hardware; does anyone remember Thinking Machines? If Intel has any smarts at all, it will take steps to ensure that its engine works with the deep learning frameworks people actually want to use, like TensorFlow, Theano, and Caffe.

Cloud Computing Drivers

Tony Safoian describes five trends driving the growth of cloud computing: better security, machine learning and big data, containerization, mobile and IoT. Cloud security hasn’t actually improved — your data was always safer in the cloud than it was on premises. What has changed is the perception of security, and the growing sense that IT sentiments against cloud have little to do with security and a lot to do with rent-seeking and turf.

On the other points, Safoian misses the big picture — due to the costs of data movement, the cloud is best suited to machine learning and big data when data sources are also in the cloud. As organizations host an increasing number of operational applications in the cloud, it makes sense to manage and analyze the data there as well.

Machine Learning for Social Good

Microsoft offers a platform to predict scores in weather-interrupted cricket matches.

Shameless Commerce

In a podcast, Ben Lorica interviews John Akred on the use of agile techniques in data science. Hey, someone should write a book about that.

Speaking of books, I plan to publish snippets from my new book, Disruptive Analytics, every Wednesday over the next couple of months.

DA Cover

Explainers

— Uber’s Vinoth Chandar explains why you rarely need sub-second latency for streaming analytics.

— Microsoft’s David Smith explains how to tune Apache Spark for faster analysis with Microsoft R Server.

— Databricks’ Jules Damji explains how to use SparkSession with Spark 2.0.

— On the Cloudera Engineering Blog, Devadutta Ghat et. al. explain analytics and BI on S3 with Apache Impala. Short version: you’re going to need more nodes.

— In the first of a three-part series, IBM’s Elias Abou Haydar explains how to score health data with Apache Spark.

— Basho’s Pavel Hardak explains how to use the Riak Connector for Apache Spark.

— On YouTube, Alluxio founder and CEO Haoyuan Li explains Alluxio.

— Pat Ferrel explains the roadmap for Mahout. According to OpenHUB, Mahout shows a slight uptick in developer activity, from zero to two active contributors.

— Cisco’s Saravanan Subramanian explains the features of streaming frameworks, including Spark, Flink, Storm, Samza, and Kafka Streams. A pretty good article overall, except that he omits Apache Apex, a top-level Apache project.

— Frances Perry explains what the Apache Beam has accomplished in the first six months of incubation.

Perspectives

— Curt Monash opines about Databricks and Spark. He notes that some people are unhappy that Databricks hasn’t open sourced 100% of its code, which is just plain silly.

— IBM’s Vijay Bommireddipalli touts IBM’s contributions to Spark 2.0.

— Mellanox’ Gillad Shainer touts the performance advantage of EDR InfiniBand versus Intel Omni-Path. Mellanox sells InfiniBand host bus adapters and network switches.(h/t Bob Muenchen)

— Kan Nishida runs a cluster analysis on R packages in Google BigQuery and produces something incomprehensible.

— Pivotal’s Jagdish Mirani argues that network-attached storage (NAS) may be a good alternative to direct-attached storage (DAS). Coincidentally, Pivotal’s parent company EMC sells NAS devices.

Open Source News

— Apache Flink announces two releases. Release 1.1.0 includes new connectors, the Table API for SQL operations, enhancements to the DataStream API, a Scala API for Complex Event Processing and a new metrics system. Release 1.1.1 fixes a dependency issue.

— Apache Kafka announces Release 0.10.0.1, with bug fixes.

— Apache Samza releases Samza 0.10.1 with new features, performance improvements, and bug fixes.

— Apache Storm delivers version 1.0.2, with bug fixes.

Commercial Announcements

— AWS releases EMR 5.0, with Spark 2.0, Hive 2.1 and Tez as the default execution engine for Hive and Pig. EMR is the first Hadoop distribution to support Spark 2.0.

— Fractal Analytics partners with KNIME.

— MapR announces a $50 million venture round led by the Australian Government Future Fund.

Big Analytics Roundup (April 11, 2016)

Top story of the week is NVIDIA’s new DGX-1 deep learning chip; scroll down for more on that.

We have three roundups from Strata + Hadoop World, Rashomon style:

  • Alex Woodie reports six takeaways: Kafka, Spark, Hadoop, Cloud, machine learning, mainframes.
  • Jessica Davis recalls four things: comedian Paula Poundstone, MapR, public data sets, AI.
  • Nik Rouda recaps five things: Spark, machine learning, data warehousing, user interfaces, cloud.

— H2O.ai CTO and co-founder Cliff Click departs H2O, joins Neurensic, a firm that specializes in compliance analytics. Neurensic has a team of surname-eschewing executives that is surprisingly large considering it has no visible funding.

— Machine learning startup Context Relevant announces the appointment of Joseph Polverari as CEO, replacing board member Chris Kelley, who replaced founder Stephen Purpura in July, 2015, a month after the latter wrote a meditation on failure. Kelley’s major accomplishment: firing people. Appears that Context Relevant isn’t the next unicorn.

— One of the 76 IBM executives with the title of “CTO” touts cognitive computing. My take:

Screen Shot 2016-04-10 at 7.52.54 AM

— Forrester publishes its 2016 “Wave” for Big Data Streaming Analytics. You can go here and buy it for $2,495, get a free copy here, or just look at the picture below.

Screen Shot 2016-04-10 at 3.52.54 PM

— Spiderbook’s Aman Naimat examines data gleaned by trolling through billions of publicly available documents, identifies 2,680 companies that are using Hadoop at any level of maturity, and another 3,500 that are just learning. That’s out of a total universe of 500,000 companies worldwide. I’m thinking that trolling through billions of public documents may understate the actual incidence of Hadoop usage.

— Crowdflower, a data enrichment platform, surveys data scientists and publishes the results. The report does not disclose how data scientists were identified and sampled, which is key to interpreting surveys like this. Respondents report that they spend a lot of time mucking around with data, which won’t surprise anyone, since Crowdflower sells a service that helps data scientists spend less time mucking with data.

NVIDIA Unveils Deep Learning Chip

— NVIDIA announces June availability for the DGX-1, a deep learning supercomputer on a chip. The DGX-1 includes eight Tesla P100 GPUs, each of which is 12X faster than NVIDIA’s previous benchmark. For $129K you get the throughput of 250 CPU-based servers.

— NVIDIA also reveals a Deep Learning SDK with Deep Learning primitives, math libraries, tools for multi-GPU communication, a CUDA toolkit and DIGITS, a model training system. The system works with popular Deep Learning frameworks like Caffe, CNTK, TensorFlow and Theano.

— Selected media reports:

— MIT Technology Review interviews NVIDIA CEO Jen-Hsun Huang.

Explainers

— Ian Pointer explains Structured Streaming, coming up in Spark 2.0.

— Till Rohrmann introduces Complex Event Processing (CEP) with Flink.

— Maxime Beauchemin explains Caravel, Airbnb’s data exploration platform.

— LinkedIn’s Akshay Rai explains Dr. Elephant, a newly open-sourced self-service performance tuning package for Hadoop and Spark.

— In a guest post on the Cloudera Engineering Blog, engineers from Wargaming.net explain how they built their real-time recommendation engine with Spark, Kafka, HBase and Drools.

— Katrin Leinweber et. al. explain how to analyze an assay of bacteria-induced biofilm formation the freshwater diatom Achnanthidium minutissimum with KNIME. In case you’re wondering, Achnanthidium minutissimum is a kind of algae.

Perspectives

— On LinkedIn, George Hill of The Cyclist nicely critiques the 2011 McKinsey Big Data report, offering a point by point assessment.

— Mauricio Prinzlau of Cloudwards.net opines, without data, that the five languages paving the future of machine learning are MATLAB/Octave, R, Python, “Java-family/C-family” and Extreme Learning Machines (ELM). What was that last one again? Personally, I’ve never seen anyone lump Java and C into a single category, but whatever.

— In InfoWorld, “internationally recognized industry expert and thought leader” David Linthicum ventures into the machine learning discussion by arguing that it’s mostly BS.

— John Dunn demonstrates his ignorance of fraud by asking if machine learning can help banks detect it. As if they haven’t been doing that for years. Also, the “hard decline” he describes at the beginning of the article is rare; most false positives produce “soft declines,”, where the merchant is asked to request identification or speak with the call center.

— In IBT, Ian Allison wonders if financial analysts will lose their jobs to intelligent trading machines. If he watched Billions, he would know that financial analysts spend their time procuring inside information.

— Timo Elliott argues that BI is dead. I have to wonder if it was ever alive.

— Confluent CTO Neha Narkhede opines on stream processing. She’s in favor of it.

— Brandon Butler interviews AWS’ Matt Wood, who chats about competing with Google and Microsoft.

— On Forbes, Robert Hof interviews Cloudera CEO Tom Reilly.

Open Source Announcements

— Qubole releases SQL optimizer Quark to open source.

— Flink releases version 1.0.1, a maintenance release.

— Apache Lens, a “unified analytics interface,” releases version 2.5.0 to beta.

— Airbnb open sources Caravel, a data exploration package.

— Apache Tajo announces Release 0.11.2, which should please its user.

— LinkedIn releases Dr. Elephant to open source.

Commercial Announcements

— Databricks announces the agenda for Spark Summit 2016 in SFO.

— Cloudera announces Cloudera Enterprise 5.7. New analytic bits include Hive-on-Spark GA, support for the HBase-Spark module, support for Spark 1.6 and support for Impala 2.5.

— MapR announces availability of Apache Drill 1.6 as the unified SQL layer for the MapR Converged Data Platform.

Gartner’s 2016 MQ for Advanced Analytics Platforms

This is a revised and expanded version of a story that first appeared in the weekly roundup for February 15.

Gartner publishes its 2016 Magic Quadrant for Advanced Analytics Platforms.   You can get a free copy here from RapidMiner (registration required.)  The report is a muddle that mixes up products in different categories that don’t compete with one another, includes marginal players, excludes important startups and ignores open source analytics.

Other than that, it’s a fine report.

The advanced analytics category is much more complex than it used to be.  In the contemporary marketplace, there are at least six different categories of software for advanced analytics that are widely used in enterprises:

  • Analytic Programming Languages (e.g. R, SAS Programming Language)
  • Analytic Productivity Tools (e.g. RStudio, SAS Enterprise Guide)
  • Analytic Workbenches (e.g. Alteryx, IBM Watson Analytics, SAS JMP)
  • Expert Workbenches (e.g. IBM SPSS Modeler, SAS Enterprise Miner)
  • In-Database Machine Learning Engines (e.g. DBLytix, Oracle Data Mining)
  • Distributed Machine Learning Engines (e.g. Apache Spark MLlib, H2O)

Gartner appears to have a narrow notion of what an advanced analytics platform should be, and it ignores widely used software that does not fit that mold.  Among those evaluated by Gartner but excluded from the analysis: BigML, Business-Insight, Dataiku, Dato, H2O.ai, MathWorks, Oracle, Rapid Insight, Salford Systems, Skytree and TIBCO.

Gartner also ignores open source analytics, including only those vendors with at least $4 million in annual software license revenue.  That criterion excludes vendors with a commercial open source business model, like H2O.ai.  Gartner uses a similar criterion to exclude Hortonworks from its MQ for data warehousing, while including Cloudera and MapR.

Changes from last year’s report are relatively small.  Some detailed comments:

— Accenture makes the analysis this year, according to Gartner, because it acquired Milan-based i4C Analytics, a tiny little privately held company based in Milan, Italy.  Accenture rebranded the software assets as the Accenture Analytics Applications Platform, which Accenture positions as a platform for custom solutions.  This is not at all surprising, since Accenture is a consulting firm and not a software vendor, but it’s interesting to note that Accenture reports no revenue at all from software licensing;  hence, it can’t possibly satisfy Gartner’s inclusion criteria for the MQ.  The distinction between software and services is increasingly muddy, but if Gartner includes one services provider on the analytics MQ it should include them all.

Alpine Data Labs declines a lot in “Ability to Deliver,” which makes sense since they appear to be running out of money (*).  Gartner characterizes Alpine as “running analytic workflows natively within Hadoop”, which is only partly true.  Alpine was originally developed to run on MPP databases with table functions (such as Greenplum and Netezza), and has ported some of its functions to Hadoop.  The company has a history with Greenplum Pivotal and EMC Dell, and most existing customers use the product with Greenplum Database, Pivotal Hadoop, Hawq and MADlib, which is great if you use all of those but otherwise not.  Gartner rightly notes that “the depth of choice of algorithms may be limited for some users,” which is spot on — anyone not using Alpine with Hawq and MADlib.

(*) Of course, things aren’t always what they appear to be.  Joe Otto, Alpine CEO, contacted me to say that Alpine has a year’s worth of expenses in the bank, and hasn’t done any new venture rounds since 2013 “because they haven’t needed to do so.”  Joe had no explanation for Alpine’s significantly lower rating on both dimensions in Gartner’s MQ, attributing the change to “bias”.  He’s right in pointing out that Gartner’s analysis defies logic.

Alteryx declines a little, which is surprising since its new release is strong and the company just scored a pile of venture cash.  Gartner notes that Alteryx’ scores are up for customer satisfaction and delivering business value, which suggests that whoever it is at Gartner that decides where to position the dots on the MQ does not read the survey results.  Gartner dings Alteryx for not having native visualization capabilities like Tableau, Qlik or PowerBI, a ridiculous observation when you consider that not one of the other vendors covered in this report offers visualization capabilities like Tableau, Qlik or PowerBI.

Angoss improves a lot, moving from Niche to Challenger, largely on the basis of its WPL-based SAS integration and better customer satisfaction.  Data prep was a gap for Angoss, so the WPL partnership is a positive move.

— Dell: Arguing that Dell has “executed on an ambitious roadmap during the past year”, Gartner moves Dell into the Leaders quadrant.   That “execution” is largely invisible to everyone else, as the product seems to have changed little since Dell acquired Statistica, and I don’t think too many people are excited that the product interfaces with Boomi.  Customer satisfaction has declined and pricing is a mess, but Gartner is all giggly about Boomi, Kitenga and Toad.  Gartner rightly cautions that software isn’t one of Dell’s core strengths, and the recent EMC acquisition “raises questions” about the future of software at Dell.  Which raises questions about why Gartner thinks Dell qualifies as a Leader in the category.

FICO fades for no apparent reason.  I’m guessing they didn’t renew their subscription.

IBM stays at about the same position in the MQ.  Gartner rightly notes the “market confusion” about IBM’s analytics products, and dismisses yikyak about cognitive computing.  Recently, I spent 30 minutes with one of the 443 IBM vice presidents responsible for analytics — supposedly, he’s in charge of “all analytics” at IBM — and I’m still as confused as Gartner, and the market.

— KNIME was a Leader last year and remains a Leader, moving up a little.  Gartner notes that many customers choose KNIME for its cost-benefit ratio, which is unsurprising since the software is free.  Once again, Gartner complains that KNIME isn’t as good as Tableau and Qlik for visualization.

Lavastorm makes it to the MQ this year, for some reason.  Lavastorm is an ETL and data blending tool that does not claim to offer the native predictive analytics that Gartner says are necessary for inclusion in the MQ.

Megaputer, a text mining vendor, makes it to the MQ for the second year running despite being so marginal that they lack a record in Crunchbase.  Gartner notes that “Megaputer scores low on viability and visibility and there is a lack of awareness of the company outside of text analytics in the advanced analytics market.”  Just going out on a limb, here, Mr. Gartner, but maybe that’s your cue to drop them from the MQ, or cover them under text mining.

Microsoft gets Gartner’s highest scores on Completeness of Vision on the strength of Azure Machine Learning (AML) and Cortana Analytics Suite.  Some customers aren’t thrilled that AML is only available in the cloud, presumably because they want hackers to steal their data from an on-premises system, where most data breaches happen.  Microsoft’s hybrid on-premises cloud should render those arguments moot.  Existing customers who use SQL Server Analytic Services are less than thrilled with that product.

Predixion Software improves on “Completeness of Vision” because it can “deploy anywhere” according to Gartner.  Wut?  Anywhere you can run Windows.

Prognoz returns to the MQ for another year and, like Megaputer, continues to inspire WTF? reactions from folks familiar with this category.  Primarily a BI tool with some time-series and analytics functionality included, Prognoz appears to lack the native predictive analytics capabilities that Gartner says are minimally required. 

RapidMiner moves up on both dimensions.  Gartner recognizes the company’s “Wisdom of Crowds” feature and the recent Series C funding, but neglects to note RapidMiner’s excellent Hadoop and Spark integration.

SAP stays at pretty much the same place in the MQ.  Gartner notes that SAP has the lowest scores in customer satisfaction, analytic support and sales relationship, which is about what you would expect when an ankle-biter like KXEN gets swallowed by a behemoth like SAP, where analytics go to die.

SAS declines slightly in Ability to Deliver.  Gartner notes that SAS’ licensing model, high costs and lack of transparency are a concern.  Gartner also notes that while SAS has a loyal customer base whose members refer to it as the “gold standard” in advanced analytics, SAS also has the highest percentage of customers who have experienced challenges or issues with the software.

Forrester “Wave” for Predictive Analytics

Last week, Forrester published its 2015 “Wave” report for Big Data Predictive Analytics Solutions.  You can pay $2,495 and buy it directly from Forrester (here), or you can get the same report for free from SAS (here).

The report is inaptly named, as it commingles software that scales to Big Data (such as Alpine Chorus) with software that does not scale (such as Dell Statistica.)  Nor does Big Data capability appear to impact the ratings; otherwise Alpine and Oracle would have scored higher than they did, and SAP would have scored lower.  IBM SPSS alone does not scale without Netezza or BigInsights; SAS only scales if you add one of its distributed in-memory back ends.  These products aren’t listed among the evaluated software components.

Also, Forrester seriously needs to hire an editor.  Alteryx does not currently offer software branded as “Alteryx Analytics”, nor does SAS currently offer a bundle called the “SAS Analytics Suite.”

Forrester previously published this wave in 2013; key changes since then:

  • Among the Leaders, IBM edged past SAS for the top rating.
  • SAP’s rating did not change but its brand presence improved considerably, which demonstrates the uselessness of brand presence as a measure of value.
  • Oracle showed up at the beauty show this time, and improved its position slightly.
  • Statistica’s rating did not change, but its brand presence improved due to the acquisition by Dell.  (See SAP, above).  Shockingly, the addition of “Toad Data Point” to the Dell/Statistica solution did not move the needle.
  • Angoss improved its ratings and brand strength slightly.
  • TIBCO and Salford switched their analyst relations budgets from Forrester to Gartner and are gone from this report.
  • KXEN and Revolution Analytics are also gone due to acquisitions.  Interestingly, the addition of KXEN to SAP had no impact on SAP’s ratings, thus demonstrating that two plus zero is still two.
  • RapidMiner, Alteryx, FICO, Alpine, KNIME and Predixion are all new to the report.

Gartner issued its “Magic Quadrant” back in February; the comparisons are interesting:

  • KNIME is a “leader” in Gartner’s view, while Forrester considers the product to be decidedly mediocre.  Seems to me that Forrester has it about right.
  • Oracle did not participate in the Gartner MQ.
  • RapidMiner, a “leader” in the Gartner MQ, scores very well on Forrester’s “Current Offering” axis, but less well on “Strategy.”   This strikes me as a good way for Forrester to sell strategy consulting.
  • Microsoft and Alpine landed in Gartner’s Visionary quadrant but scored relatively low in Forrester’s assessment.  Both vendors have appealing strategies, and need to roll up their sleeves to deliver.
  • Predixion trails the pack in both reports.  Reminds me of high school gym class.

Forrester’s methodology places more weight on the currently available software, while Gartner places more emphasis on the vendor’s “vision.”  Vision is certainly important to consider when selecting a software vendor, but leadership tends to be self-sustaining; today’s category leaders are likely to be tomorrow’s category leaders, except when markets are disrupted — in which case analysts are rarely able to pick winners.

Big Analytics Roundup (March 9, 2015)

Here’s a roundup of interesting Big Analytics news and analysis from the past week.  Featured this week: Hortonworks, Alpine, Spark and H2O.

Hortonworks

  • Matt Asay, writing in InfoWorld, deconstructs Hortonworks’ earnings fiasco, and with it the “100% open source” business model.

Alpine Data Labs

  • VentureBeat reports a story that Alpine Data Labs claims 10X growth in user count and billings year over year.
  • MarketWired reports the same story.
  • ITBusinessNet too.

There is no supporting press release from Alpine Data Labs.   The VentureBeat story includes the nugget that Alpine currently has “more than 60” customers; an insider tells me that the number is closer to 75, roughly twice as many as last year.  Alpine has changed its selling model, hiring its own sales force instead of selling through EMC and Pivotal.  This also means that Alpine has changed its messaging from “we run on Greenplum and PostgresSQL, but mostly on Greenplum” to “we run on anything.”  This is an aspiration, to be sure, but a good one.

Alpine has also changed its pricing model from a perpetual server-based model to a user-based subscription model.

Separately, Ventana Research publishes a positive review of Alpine Chorus 5.0.

Apache Spark

  • Jonathan Buckley of Qubole argues that the three open source projects that transformed Hadoop are Hive, Spark and Presto.  It’s an odd choice.  Hive is certainly a key project and Spark is red hot; Presto, not so much.
  • Data prep engine vendor Paxata announces a new release that runs on Spark, releases benchmark report showing significant performance improvements.
  • Databricks announces selection of Databricks Cloud as preferred platform for B2B vendor Radius Intelligence, publishes case study.
  • Forbes profiles Databricks CEO Ion Stoica.
  • Ian Lumb offers eight reasons why Spark is hot.
  • Databricks published a slideshare about Spark DataFrames, which will be available in Spark 1.3 later this month.
  • From the Cloudera blog, an excellent post showing how to build an application for financial markets risk calculations in Spark.

H2O

  • In an interview with KDNuggets, Ted Dunning touts Mahout and H2O over Spark.
  • H2O.ai announces Cloudera certification for its Sparking Water interface to Spark.

General

CMSWire rehashes the Gartner Magic Quadrant without adding value.   The author notes breathlessly that “many KNIME enthusiasts are data miners”, and “on the downside, (RapidMiner’s) user base is mostly data scientists”; as if these points are news, and as if there is something extraordinary about data miners and data scientists using data mining and data science tools.

Gartner Advanced Analytics Magic Quadrant 2015

Gartner’s latest Magic Quadrant for Advanced Analytics is out; for reference, the 2014 report is here; analysis from Doug Henschen here.  Key changes from last year:

  • Revolution Analytics moves from Visionary to Niche
  • Alpine and Microsoft move from Niche to Visionary
  • Oracle, Actuate and Megaputer drop out of the analysis
Gartner 2015 Magic Quadrant, Advanced Analytics
Gartner 2015 Magic Quadrant, Advanced Analytics

Gartner changed its evaluation criteria this year to reflect only “native” (e.g. proprietary) functionality; as a result, Revolution Analytics dropped from Visionary to Niche.   Other vendors, it seems, complained to Gartner that the old criteria were “unfair” to those who don’t leverage open source functionality.  If Gartner applies this same reasoning to other categories, it will have to drop coverage of Hortonworks and evaluate Cloudera solely on the basis of Impala.  🙂

Interestingly, Gartner’s decision to ignore open source functionality did not impact its evaluation of open source vendors RapidMiner and KNIME.

Based on modest product enhancements from Version 4.0 to Version 5.0, Alpine jumped from Niche to Visionary.   Gartner’s inclusion criteria for the category mandate that “a vendor must offer advanced analytics functionality as a stand-alone product…”; this appears to exclude Alpine, which runs in Pivotal Greenplum database (*).  Gartner’s criteria are flexible, however, and I’m sure it’s purely coincidental that Gartner analyst Gareth Herschel flacks for Alpine.

(*) Yes, I know — Alpine supports other databases and Hadoop as well.   The number of Alpine customers who use it in anything other than Pivotal can meet in Starbucks at one of the little tables in the back.

Gartner notes that Alpine “still lacks depth of functionality. Several model techniques are either absent or not fully developed within its tool.”  Well, yes, that does seem important.   Alpine’s promotion to Visionary appears to rest on its Chorus collaboration capability (originally developed by Greenplum).  It seems, however, that customers don’t actually use Chorus very much; as Gartner notes, “adoption is currently slow and the effort to boost it may divert Alpine’s resources away from the core product.”

Microsoft’s reclassification from Niche to Visionary rests purely on the basis of Azure Machine Learning (AML), a product still in beta at the time of the evaluation.  Hardly anyone uses MSFT’s “other” offering for analytics (SQL Server Analytic Services, or SSAS), apparently for good reason:

  • “The 2014 edition of SSAS lacks breadth, depth and usability, in comparison with the Leaders’ offerings.”
  • “Microsoft received low scores from SSAS customers for its willingness to incorporate their feedback into future versions of the product.”
  • “SSAS is a low-performing product (with poor features, little data exploration and questionable usability.”

On paper, AML is an attractive product, though it maxes out at 10GB of data; however, it seems optimistic to rate Microsoft as “Visionary” purely on the basis of a beta product.  “Visionary” is a stretch in any case — analytic software that runs exclusively in the cloud is by definition a niche product, as it appeals only to a certain segment of the market.  AML’s most attractive capabilities are its ability to run Python and R — and, as we noted above — these no longer carry any weight with Gartner.

Dropping Actuate and Megaputer from the MQ simply recognizes the obvious.  It’s not clear why these vendors were included last year in the first place.

It appears that Oracle chose not to participate in the MQ this year.  Analytics that run in a single database platform are by definition niche products — you can’t use Oracle Advanced Analytics if you don’t have Oracle Database, and few customers will choose Oracle Database because it has Oracle Advanced Analytics.