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.

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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 8, 2016)

So, Apple acquires Turi for $200 million. Hopefully, Apple did not pay for brand equity.

Bridget Botelho argues that businesses must either disrupt or be disrupted, and outlines the role of machine learning. Someone should write a book about that.

Conference Announcements

— Flink Forward announces the schedule for its second annual event, to be held September 12-14 in Berlin.

— Databricks announces the agenda for Spark Summit Europe 2016 in Brussels (October 25-27)

Apple Buys GraphLab Dato Turi

Geekwire breaks the story, reporting a purchase price of $200 million. According to TechCrunch, Turi notified customers that its products would no longer be available. Apple adds Turi to the portfolio of machine learning startups it has acquired in the past year, including Emotient, Perceptio, and VocalIQ. More reporting here.

GraphLab started in 2009 as an open source project led by Carlos Guestrin of Carnegie Mellon. (According to OpenHub Guestrin never contributed any code.) In May 2013, Guestrin raised $6.75M to start an eponymous venture to provide commercial support for GraphLab. In October 2014, GraphLab announced the availability of GraphLab Create, a commercially licensed software product. Contributions to the open source project actually ended in 2013; while the code remains on GitHub, the project is dead.

GraphLab changed its name to Dato in January 2015. They should have googled the name; at the time, the top links in a search included Dato Foland, a gay porn star, and Datto Inc, a data backup and recovery company in Connecticut. The latter proved problematic; Datto sued, forcing Dato to rebrand as Turi earlier this month.

Turi’s open source SFrame project remains for those who think introducing another file system into the mix is a smart thing to do.

Teradata: 9 Straight Quarters of Declining Product Revenue

For the second quarter of 2016, declining data warehouse giant Teradata reports an 11% decline in product revenue compared to Q2 2015. (Product revenue includes revenue from licensing software and hardware — boxes with the Teradata brand.) Maintenance revenue increased slightly, which means that customers aren’t pulling the plug on Teradata databases as fast as they did last year. Consulting revenue declined by 1%, which casts doubt on TDC’s stated strategy to become a services powerhouse.

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Count me as skeptical about the merits of that plan. Teradata’s consulting revenue remains highly correlated with product revenue; in other words, if Teradata can’t sell its boxes, it’s not going to sell billable hours for consultants to implement those boxes. Teradata is not a credible competitor in the market for consulting-led solutions; companies like Oracle, IBM and SAS have a twenty-year head start.

Since Teradata performed better than “expectations”, Wall Street rewarded the stock with a bounce above $30.  It’s a dead-cat bounce. As the Wall Street Journal notes, companies routinely game analyst expectations. TDC currently trades at 32 times trailing earnings, well above its peers; moreover, its peers are growing rather than declining.

Explainers

— Kaarthik Sivashanmugam explains how to develop Apache Spark applications in .NET with Mobius.

— On the Cloudera Engineering blog, Devadutta Ghat et. al. explain the latest performance improvements in Impala 2.6.

— Parsey McParseface now has 40 cousins. On the Google Research Blog, Chris Alberti et. al. explain.

— Ujjwal Ratan explains how to use Amazon Machine Learning to predict patient readmission.

Perspectives

— Curt Monash offers his assessment of Spark. Highlights:

  • Spark replaces MapReduce, in particular for data transformation.
  • Spark is becoming the default platform for machine learning.
  • Spark SQL is OK as an adjunct for other analysis.
  • Spark Streaming is doing well, but there are challengers. (See below).
  • Databricks’ managed service for Spark has more than 200 subscribers.

— Serdar Yegulalp deploys the tired old “pure streaming versus microbatch” argument to claim that Apache Apex, Heron, Apache Flink and Onyx are “contenders” versus Spark. Someone should show him this graph:

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— In Datanami, Alex Woodie profiles Flink.

— Vance McCarthy touts MapR’s Spyglass Initiative for analytics on the MapR Converged Data Platform.

— Trevor Jones describes Microsoft Azure’s big data tools.

— Sam Dean champions Sparkling Water, H2O’s interface to Spark.

Commercial Announcements

— Dataiku announces the release of Data Science Studio 3.1, with five machine learning back ends and a visual coding interface (which it labels “code-free”).  Dave Ramel reports.

— John Snow Labs announces it will deliver curated data in Parquet format.

— Lexalytics announces the availability of its Semantria text analytics software on Azure.

Big Analytics Roundup (April 25, 2016)

Mesosphere wins the internet this week with its announcement that it has open sourced DC/OS, its datacenter virtualization project built around Apache Mesos. While not an “analytics” project per se, DC/OS has the potential to transform how organizations provision and deploy their analytics platforms.

In a nutshell, Apache Mesos distributes workloads across physical IT resources. DC/OS adds a container orchestration platform; installation, management and monitoring tools; and improvements to networking, security, load balancing, security and other areas. For more details about DC/OS and why it matters, read this white paper by Benjamin Hindman and Edward Hsu of Mesosphere.

Mesosphere has assembled an alliance of 61 launch partners, including tech vendors, systems integrators and potential users. Big brands include Accenture, Capgemini, Cisco, EMC, HPE, Microsoft, MapR, Microsoft and Verizon. Notable startups include Alluxio, Canonical, Confluent, Lightbend and MemSQL.

Analysts chime in:

  • Gavin Clarke thinks Google forced Mesosphere’s hand by open sourcing Kubernetes.
  • Mike Wheatley, notes that many of the components were already open source.
  • On TechCrunch, Frederic Lardinois reports and comments.
  • In Computerworld, John Ribeiro reports.
  • Janakiram MSV wonders if DC/OS will emerge as an alternative to Kubernetes.
  • Sam Dean surveys the project and interviews Ben Hindman.
  • George Leopold notes the scope of the DC/OS ecosystem.
  • Joao Lima reports.

DC/OS ships with more than 30 open source packages ready to install as DC/OS services. Notable among them: Cassandra, Elasticsearch, Kafka, MemSQL, Spark, Storm and Zeppelin.

Explainers

— Andrie de Vries explains how he scraped CRAN to trace the growth in R packages.

— On the Cloudera Engineering blog, David Alves explains how to use Impala and Kudu for analytic workloads.

— Michael Hunger and William Lyon explain how they analyzed the Panama Papers with Neo4j.

— On the Microsoft Azure blog, Liam Cavanagh explains how to optimize document search in Azure.

— Adrian Colyer of the morning paper summarizes five papers on word vectors, reviews Global Vectors for Word Representation, delivers an overview of Deep Learning and covers ImageNet classification with deep convolutional neural networks.

— Mario Inchiosa and Roni Burd explain how Microsoft R Server delivers an R interface to Spark in HDInsight.

Perspectives

— In MIT Technology review, Tom Simonite interviews Google’s Jeff Dean, contributor to Spanner, Translate, BigTable, MapReduce, Google Brain. LevelDB and TensorFlow. They discuss the future of machine learning.

— David Weldon went to Strata and interviewed some people:

  • Ali Hodroj of GigaSpaces, a cloud enabling company. Hodroj is bullish on cloud.
  • H2O.ai’s Arno Candel, who is surprised that so many people are talking about Spark.
  • Nikita Ivanov of GridGain, who says that people are excited about in-memory computing.
  • DataArtisans’ Kostas Tzoumas, who thinks that more people would use Flink if they were better educated.

— Alex Woodie touts Apache Beam, the open source implementation of Google’s Cloud Dataflow, which aspires to unify everything.

— James Nunns surveys ten Big Analytics startups: Confluent, H2O.ai, AtScale, Interana, Tamr, Wavefront, BlueTalon, Cazena, DataTorrent and Databricks.

— In Silicon Angle, Wikibon’s Paul Gillin interviews Wikibon’s George Gilbert, who is bullish on Spark.

— John Leonard ruminates on Hadoop, noting the proliferation of cute animal logos, and the challenges of the open source business model.

— Sam Dean notices that there are quite a few new open source tools for machine learning.

— Jack Vaughan summarizes the educational challenges posed by machine learning.

Commercial Announcements

— Dataiku announces availability of Data Science Studio on Microsoft Azure.

— GridGain announces availability of a support package for Apache Ignite that includes its Professional Edition — essentially the same as Apache Ignite, with more frequent maintenance releases and some LGPL libraries.

— MemSQL announces closing on a $36 million “C” round. All existing investors participated, plus two new investors.

Big Analytics Roundup (April 4, 2016)

Strata + Hadoop World sparks a number of commercial announcements: AtScale has a new release, Microsoft previews R Server on HDInsight, and IBM puts Spark on a mainframe, FWIW. We also have a nice harvest of explainers and perspectives.

Slides from Strata available here.

The folks at Domino Data ask: Is XGBoost 10X faster than H2O? We’ll never know the answer, since they took down the post. I’m guessing the answer is “no.”

Screen Shot 2016-04-04 at 10.47.32 AM

Databricks offers a collection of popular blog posts on Apache Spark as an eBook.

Explainers

On the Google Cloud Big Data Blog, Eric Anderson and Marian Dvorsky compare autoscaling in Dataflow/Beam to Spark and Hadoop. (h/t William Vambenepe)

Miles Yucht and Reynold Xin explain DeepSpark, a convolutional neural network that automates software development processes, such as writing test cases, fixing bugs and so forth.

Databricks’ Jules Damji explains how to process JSON data with Spark Datasets and DataFrames.

On the Airbnb engineering blog, Ricardo Bion explains how to scale data science with R.

Eduardo Ariño De La Rubia explains how The Climate Corporation created a high-throughput data science machine.

DataArtisans’ Kostas Tzoumas explains Flink internals, and how Flink counts elements in streams.

On the Insight Data Engineering blog, Daniel Blazevski explains Flink quadtrees.

H2O.ai’s Erin LeDell explains scalable ensemble learning with H2O. Also at Strata, Arno Candel explains why Deep Learning is eating your lunch.

On the Dataiku blog, someone named Margot explains automated model deployment with Data Science Studio.

On the DataTorrent blog, David Yan explains latency calculations in Apache Apex.

Christopher Crosbie explains SparkR on EMR, on the AWS Big Data blog.

Perspectives

Jack Vaughan notes the prominence of streaming analytics at Strata, quotes some old guy who thinks streaming is a thing.

On the Cloudera Vision Blog, Dan Sturman describes Cloudera’s response to what he characterizes as a software quality challenge.

Cloud vendor Altiscale’s Raymie Stata asks which is best for Spark and Hadoop: cloud or on-premises. Spoiler: he thinks you should choose cloud.

On LinkedIn, consultant Rick van der Lans touts Apache Drill.

Wikibon releases forecasts of Spark adoption and the Big Data market. You can either pay Wikibon for a subscription, or read George Leopold’s summary here or Mike Wheatley’s summary here.

Alex Woodie recaps Doug Cutting’s keynoter at Strata+Hadoop.

On the tech blog for Berlin-based online retailer Zalando, Javier Lopez and Mihail Vieru recap a recently completed Flink versus Spark bakeoff. They like Flink’s low latency which, as a fashion retailer, they totally think they need. The bottom line, though, seems to be that DataArtisans is just a few stops away on the U-Bahn, so they chose Flink.

Brandon Butler summarizes the Microsoft and Google challenges to Amazon in the cloud.

InfoWorld’s Martin Heller reviews Databricks’ Spark service, likes it.

In TechCrunch, Josh Klahr lists seven things to watch for at Strata + Hadoop World, which is still worth reading even though the show came and went.

Talend CMO Ashley Stirrup suggests you sharpen your customer reflexes with Apache Spark. If you want to improve your actual reflexes, read this.

Open Source Announcements

ASF announces Apache NiFi 0.6.0, with Kerberos authentication for its REST API and support for Amazon Kinesis, AWS Lambda, Splunk, and Apache Cassandra. (h/t Hadoop Weekly)

Commercial Announcements

OLAP-on-Hadoop vendor AtScale announces release 4.0. Key new bits: fine-grained security that links every query to an end user and an intelligent query optimizer that pushes down either as SQL or as MDX depending on end user tool. AtScale has also added to its platform integration, now supports  Business Objects, Cognos, Excel, Jaspersoft, Qlik, MicroStrategy, PowerBI, Spotfire, and Tableau on CDH, HDP, HDInsights and MapR with Hive/Tez, Impala and Spark SQL and an impressive list of data storage formats. Mike Wheatley reports.

Data integration startup Tamr announces “compatibility” with Spark. The press release does not specify whether that means connectivity, push-down integration or something else. Tamr is not certified by Databricks, and has not published anything on Spark Packages.

Pouring new wine into old bottles, IBM delivers Spark on a mainframe, as promised last July.  IBM touts this as a way to perform analysis of your data “in place”, which is great if all of your data is stuck on a mainframe.

IBM partners with Lightbend, the company formerly known as Typesafe, to deliver Scala training through the Big Data University.

Altiscale announces partnership with Tableau, will add visualization to its managed service for Big Data.

Databricks announces availability of APIs to automate Spark infrastructure. On the Databricks blog, Dave Wang explains.

Microsoft announces preview of R Server for HDInsight and an update to Apache Spark for Azure HDInsight. R Server for HDInsight is a rebranded version of Revolution Analytics’ ScaleR acquired last year. R Server is a distributed machine learning platform with push-down integration to MapReduce and Spark and an R API.

Flink promoter DataArtisans announces a 5.5 million Euro Series A financing round led by Intel Capital.

Dataiku announces a new release of Data Science Studio. The press release touts some new features, but I’ll refrain from commenting until the company posts release notes.

Big Analytics Roundup (March 21, 2016)

Minimal hard news this week, but some interesting survey results, analysis, articles, explainers and perspectives.

— On his personal blog, Will Kurt describes Bayesian reasoning in the Twilight Zone. I tried to learn Bayesian reasoning a few years ago, but it conflicted with my prior beliefs.

— Stack Overflow shares results from its 2016 Developer Survey. (h/t Thomas Ott) Key bits:

  • Most popular technologies for math and data: Python and SQL.
  • Top paying technologies: Spark and Scala.
  • Top paying tech for data scientists: Scala, Spark and Hadoop.
  • Top tech stack for data scientists: Python + R + SQL.
  • Top development environments for data scientists: (1) Vim; (2) Notepad++; (3) RStudio; (4) IPython/Jupyter.
  • Job priorities for data scientists: (1) Salary; (2) Building something that’s innovative.
  • Biggest challenge at work (all respondents): Unrealistic expectations.
  • Purchasing power of developers in South Africa: 25,713 Big Macs per year.

— MIT Technology Review summarizes a comparative analysis of the tweeps for Hillary Clinton and Donald Trump. Study authors use facial recognition to classify followers into demographic categories, with surprising findings.

— Daniel Chalef of Domino Data analyzes data from Google Trends and StackOverflow, discovers that people search for open source data science tools more than they do for commercial data science tools. For a more comprehensive look at this question, see Bob Muenchin’s blog on the popularity of analytics software. Search interest is one data point, Bob’s work with job postings offers a better picture of the actual state of the market.

— On his Databaseline blog, Ian Hellström corrals information on Apache streaming projects, including Apex, Beam, Flink, Flume, Ignite, NiFi, Samza, Spark Streaming and Storm/Trident.

Explainers

— On the Confluent blog, Jay Kreps explains Kafka Streams. Given Kafka’s dominance in the streaming data space, I suspect that we will see Confluent move upstream — no pun intended — to streaming analytics.

— This week from the morning paper:

  • Adrian Colyer explains MacroBase, an open source software project for anomaly detection in streaming data.
  • … explains social engineering attacks and potential defenses.
  • explains distributed TensorFlow with MPI. Distributed versions improve (runtime) performance, but scaleability is sublinear; with 32 nodes, performance is a little less than 12X faster than a single node.

— MapR’s Tugduall Grall explains what Spark is, what it does, and what sets it apart.

— In SlideShare, Joe Chow explains random grid search for hyperparameter optimization in H2O.

— On the Databricks blog, Denny Lee et. al. explain how to use the new GraphFrames package. They include a notebook and demonstration of GraphFrames with the airline on-time performance dataset.

— MSFT’s Jeff Stokes explains how to scale stream analytics jobs with Azure Machine Learning functions.

— On the MapR blog, Carol McDonald explains how to get started using GraphX with Scala.

Perspectives

— Jack Vaughan interviews some old guy who thinks Spark is a thing.

— In Forbes, Gil Press reviews the Forrester TechRadar Big Data report and opines about the top ten technologies. InformationWeek’s Jessica Davis reviews the same report and draws different conclusions. The great thing about punditry is you can say anything you like.

— Gabriela Motroc engages the tiresome old “Spark versus Hadoop” theme.

— Alex Woodie opines that Hadoop must evolve toward greater simplicity. While his complaint has merit, the problem with his argument is that organisms do not “evolve” to simplicity; simplicity itself is a product of design.  Pure Hadoop is simple: MapReduce and HDFS.  Hadoop has evolved to something more complex because it had to do so; every additional piece added to the ecosystem is a response to unmet needs.

— H2O.ai’s Ken Sanford, who previously worked for SAS, argues that the best data scientists run R and Python.  He’s right. Money talks: according to O’Reilly’s 2015 Data Science Salary Survey, the median salary for data scientists who use SAS is less than the median salary for data scientists who use R and Python.

— On Medium, PredictionIO’s Thomas Stone celebrates ten years of open source machine learning.

— Jessica Davis profiles nine big data and analytics startups she thinks you should watch: Confluent, H2O.ai, AtScale, Algorithmia, BedrockData, Wavefront, RJMetrics, BlueTalon, and Cazena.

— In TechCrunch, Hightail’s Mike Trigg opines that Silicon Valley’s unicorn problem will solve itself. I doubt that’s true; you can’t simultaneously argue that VCs are irrational on the upside (e.g. Groupon) but rational on the downside. If VCs are too dumb to spot companies with no sustainable competitive advantage, they are also too dumb to spot “well-run, profitable companies with proven business models and healthy balance sheets.”

— On Quora, Dato’s Carlos Guestrin opines about what’s next in machine learning.

— In Martech Advisor, Ankush Gupta Mar interviews Altiscale’s VP of Marketing, Barbara Lewis. Interesting bits about Altiscale’s Spark-as-Service offering.

— David Weldon asks if you are asking all the wrong questions about Apache Spark. He interviews Sean Suchter of Pepperdata.

— Srini Penchikala interviews the authors of Spark in Action, an upcoming book from Manning.

Teradata Watch

— Teradata CEO Mike Koehler continues to demonstrate confidence in the company’s growth prospects by selling another 350,000 shares.

— Zacks downgrades TDC to hold. On Wall Street, “hold” is code for “dump it.”

Open Source Announcements

— Three announcements from Apache projects:

  • Apex announces release 3.3.1 of the Malhar library, a maintenance release.
  • Drill announces release 1.6.0, which includes a few new features and many bug fixes. Release notes here.
  • Phoenix announces release 4.7, with ACID transaction support, better statistics, improved performance and 150+ bug fixes.

Commercial Announcements

— SAP announces general availability for SAP HANA Vora, a tool that enables HANA users to query data in Hadoop and other distributed storage platforms through Spark. In CIO, Thor Olavsrud reports.

— Dataiku announces that it has hired two new Veeps to drive expansion in North America.

— Reltio announces GA of Reltio Cloud 2016.1, with early access to Reltio Insights. Reltio offers a master data management platform-as-a-service; Reltio Insights adds Spark to the mix.

— BlueData announces that it has joined the Dell Technology Partnership Program. BlueData offers a datacenter virtualization capability that enables enterprises to build an on-premises cloud. BlueData Veep Greg Kirchoff opines about the partnership. Spoiler: he likes it.

Big Analytics Roundup (February 29, 2016)

Happy Leap Day.  Tachyon’s rebranding as Alluxio, release of CaffeOnSpark and GA for Google Cloud Dataproc lead the hard news this week.  The Alluxio announcement has inspired big thinkers to share big thoughts.  And, we have a nice crop of explainers.  Scroll down to the bottom for another SQL on Hadoop benchmark.

Explainers

— In SearchDataManagement, Jack Vaughn explains Spark 2.0.

— In Datanami, Alex Woodie explains Structured Streaming in Spark 2.0.

— MapR’s Jim Scott explains Spark accumulators.   Jim also explains Spark Streaming.

— DataArtisans’ Fabian Hueske introduces Flink.

— In SlideShare, Julian Hyde explains streaming SQL.

— Wes McKinney explains why pandas users should be excited about Apache Arrow.

— On her blog, Paige Roberts explains Project Tungsten, complete with pictures.

— Someone from Dremio explains Drillix, which is what you get when you combine Apache Phoenix and Apache Drill. (h/t Hadoop Weekly).

Perspectives

— In TheNextPlatform, Timothy Prickett Morgan argues that Tachyon Caching (Alluxio) is bigger than Spark

— In SiliconAngle, Maria Deutscher opines that Alluxio (née Tachyon) could replace HDFS for Spark users.

— In The New Stack, Susan Hall speculates that Apache Arrow’s columnar data layer could accelerate Spark and Hadoop.  She means Hadoop in a general way, e.g. the Hadoop ecosystem.

— On the Dataiku blog, “Caroline” interviews John Kelly, Managing Director of Berkeley Research Group and asks him questions about data science.  Left unanswered: is it “Data-ikoo” or “Day-tie-koo?”

— Alpine Data Labs’ Steven Hillion ruminates on success.  He’d be better off ruminating on “how to raise your next round of venture capital.”

— Max Slater-Robins opines that Microsoft is inventing the future, which is even better than winning the internet.

— In ZDNet, Andrew Brust wonders if Databricks is vying for a full analytics stack, citing the new Dashboard feature as cause for wonder.  He’s just trolling.

— In Search Cloud Applications, Joel Shore opines that streaming analytics is replacing complex event processing, which makes sense.   He further opines that Flink will displace Spark for streaming, which doesn’t make sense.   Shore interviews IBM’s Nagui Halim about streaming here.

Open Source Announcements

— Alluxio (née Tachyon) announces Release 1.0.0.  Alluxio is open source software distributed through Git under an Apache license, but is not an Apache project.  Yet.  Release 1.0 includes frameworks for MapReduce, Spark, Flink and Zeppelin.  Daniel Gutierrez reports.

— Yahoo releases CaffeOnSpark, a distributed deep learning package.  Caffe is one of the better-known deep learning packages, with a track record in image recognition.  Software is available on Git.  For more information, see the Wiki.  Alex Handy reports; Charlie Osborne reports.

— RapidMiner China announces availability of an extension for deep learning engine DL4J.  The extension is open source, and works with the open source version of RapidMiner.  DL4J sponsor Skymind collaborated.

Commercial Announcements

–Tachyon Nexus, the commercial venture founded to support Tachyon, the memory-centric virtual distributed storage system, announces that it has rebranded as Alluxio.

— Google announces general availability for its Cloud Dataproc managed service for Spark and Hadoop.

Funding Announcements

Health analytics vendor Health Catalyst lands a $70M Series E round.

AtScale Benchmarks SQL-on-Hadoop Engines

On the AtScale blog, Trystan Leftwich summarizes results from a benchmark test of Hive on Tez (1.2/0.7), Cloudera Apache Impala (2.3) and Spark SQL (1.6).  The AtScale team tested Impala and Spark with Parquet and Hive on Tez with ORC.  For test cases, the team used TPC-H data arranged in a star schema, and ran 13 queries in each SQL engine multiple times, averaging the results.

While Hortonworks recommends ORC with Hive/Tez, there are published cases where users achieved good results with Hive/Tez on Parquet.  Since the storage format has a big impact on SQL performance, I would have tested Hive/Tez on Parquet as well.  AtScale did not respond to queries on this point.

Key findings:

  • All three engines performed about the same on single-table queries, and on queries joining three small tables.
  • Spark and Impala ran faster than Hive on queries joining three large tables.
  • Spark ran faster than Impala on queries joining four or more tables.

The team ran the same tests with AtScale’s commercial caching technology, with significant performance improvements for all three engines.

In concurrency testing, Impala performed much better than Hive or Spark.

Details of the test available in a white paper here (registration required).

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.

Big Analytics Roundup (October 5, 2015)

Announcements timed to coincide with Strata NYC 2015 drive the news this week.  The single most interesting item for Big Analytics is the O’Reilly 2015 Data Science Survey, which warrants a post of its own.  Two key points:

  • Data scientists still use SQL, Excel, Python and R. (Doh!)
  • Data scientists who spend time in meetings and presenting results of analysis earn more than the grunts who muck around with data.

The lesson is clear: if you want to earn the big bucks, stop messing around with Zeppelin and learn PowerPoint.

There are a few interesting presentations from Strata embedded in this post, plus two that stand out:

  • Ron Kasabian of Intel and Michael Draugelis of Penn Medicine explain how they improve medical decision-making with predictive analytics.
  • Iulia Pasov and Calin-Andrei Burloiu show how they use data science to measure and prevent churn at Avira.

Paul Kent has the toughest job at SAS — promoting an initiative his boss thinks is hype.  In this sponsored presentation, Paul does a professional job presenting SAS’ Big Data story, which seems compelling.  However, the challenge for SAS in Big Data remains: name a reference customer.

Spark

Commentary

MapR’s Jim Scott takes another shot at the “will Spark replace Hadoop?” meme.  All together now:

  • Spark, like MapReduce, is a compute engine
  • Hadoop = MapReduce + HDFS + YARN + (an ecosystem of other bits)
  • Spark lacks a native file system, so it can never replace Hadoop
  • Coming in 2016:  Hadoop = Spark + HDFS + YARN + (an ecosystem…)
  • Also possible: Spark + Cassandra, Spark + MongoDB, Spark + Druid, Spark + (your database here)

On the Intersog blog, Jenny Richards gets it right by focusing on the differences between Spark and MapReduce.

Spark Maintenance Release

The Spark team announces Spark 1.5.1, a maintenance release with about 80 bug fixes.  On the Databricks blog, Reynold Xin explains Spark version numbers work.  Short version: top level numbers correspond to API compatibility, dot releases include features and enhancements, double-dot releases have bug fixes.

Spark Use Cases and Success Stories

At Strata NYC 2015, Databricks’ Reynold Xin describes  “sketching” with Spark (aka exploratory analysis and feature engineering).

Also at Strata, Edd Dumbill presents the business case for Spark, Kafka “and friends.”

On the MongoDB blog, Mat Keep interviews Thiago Cardoso, co-founder and CTO of Hekima, a social media analytics startup.  Hekima uses Spark, Hadoop and (you guessed it) MongoDB.

On SmartDataCollective, the ubiquitous Jim Scott describes what he calls use cases for Spark: exploratory analytics; machine learning; real-time dashboards and ETL.

At Big Data Dat LA 2015, ESRI’s Adam Mollenkops explains how to apply GeoSpatial Analytics with Spark. Video here.

Spark Integration

A slew of software vendors announce integration with Spark.

–Talend announces Release 6, which leverages Spark and Spark Streaming.  Stories here, herehere, here, here, here, and here.

Syncsort announces integration with Spark and Kafka.

–TIBCO announces Spotfire integration with Spark through the Spark SQL and SparkR APIs.  More here, here and here.

–Dataiku announces integration of its Data Science Studio (DSS) software with Spark.  DSS offers a commercially licensed visual workbench enabling the user to build pipelines integrating a number of data sources and formats.  Analytic functionality is modest.

–SnapLogic announces pending release of what it calls SnapLogic Elastic Integration Platform, which includes components branded as “Sparkplex” and Spark “Snaps”.  The former includes a code generator that translates user requests from the SnapLogic visual pipeline designer into Spark code.  The latter is SnapLogic branding for “prebuilt connectors”, of which there are many.  SnapLogic claims that Snaps are plug and play, so it’s a “snap” to convert your pipeline from MapReduce to Spark.  Stories here, here, here, here, and here.

Spark as a Service

In case you’re not happy with offerings from Databricks, Qubole, Amazon Web Services, Google, BlueData and MemSQL, Altiscale announces you-know-what.

SQL/OLAP

Apache Drill

Dremio, a startup led by MapR’s Drill gurus, lands a $10 million A round.

At the Hadoop Meeting NYC, the folks from Dremio present Drill use cases and a roadmap.

Polymath Abhishek Tiwari reflects on Drill.

On the MapR blog, Joseph Blue explains how to identify a data breach with Drill.

AtScale

adds support for Microsoft PowerBI to its BI on Hadoop story.

Presto

On ZDNet, Natalie Gagliordi touts Teradata’s embrace of Presto.  (Note to ZDNet’s headline editor: Presto is not an Apache project).  She correctly notes that Presto is faster than Hive-on-MapReduce, but that’s a low bar; just about everything is faster than Hive releases prior to 0.13, but Hive-on-Tez competes well with Drill, Impala, Presto and Spark SQL.  That’s a problem for Presto, because a challenger has to be outstanding at something.  Once again, it appears that Teradata is betting on the wrong horse.

Machine Learning

SparkR

Databricks’ Hossein Falaki delivers a presentation at Strata on supercharging R with Spark; slides here.  Spark’s R API is incomplete; as of Spark 1.5.1 it supports DataFrames operations (including SQL queries) and generalized linear models.  That’s better than nothing, but R users who need to do serious machine learning now need to look elsewhere.

Streaming Analytics

Apache Flink

On the MapR blog, Ellen Friedman introduces you to Flink.

Data Artisans’ Robert Metzger delivers a presentation about the architecture of Flink’s streaming runtime at ApacheCon Europe.

Spark Streaming

At Strata NY 2015, Databricks’ Tathagata Das describes the new bits in Spark Streaming.