Big Analytics Roundup (March 14, 2016)

HPE wins the internet this week by announcing the re-re-release of Haven, this time on Azure.  The other big story this week: Flink announces Release 1.0.

Third Time’s a Charm

Hewlett Packard Enterprise (HPE) announces Haven on Demand on Microsoft Azure; PR firestorm ensues.  Haven  is a loose bundle of software assets salvaged from the train wreck of Autonomy, Vertica, ArcSight and HP Operations Management machine learning suite, originally branded as HAVEn and announced by HP in June, 2013.  Since then, the software hasn’t exactly gone viral; Haven failed to make KDnuggets’ list of the top 50 machine learning APIs last December, a list that includes the likes of Ersatz, Hutoma and Skyttle.

One possible reason for the lack of virality: although several analysts described Haven as “open source”, HP did not release the Haven source code, and did not offer the software under an open source license.

Other than those two things, it’s open source.

In 2015, HP released Haven on Helion Public Cloud, HP’s failed cloud platform.

So this latest announcement is a re-re-release of the software. On paper, the library looks like it has some valuable capabilities in text, images video and audio analytics.  The interface and documentation look a bit rough, but, after all, this is a first third release.

Jim’s Latest Musings

Angus Loten of the WSJ’s CIO Journal interviews SAS CEO Jim Goodnight, who increasingly sounds like your great-uncle at Thanksgiving dinner, the one who complains about “these kids today.”  Goodnight compares cloud computing to mainframe time sharing.  That’s ironic, because although SAS runs in AWS, it does not offer elastic pricing, the one thing that modern cloud computing shares with timesharing.

Goodnight also pooh-poohs IoT, noting that “we don’t have any major IoT customers, and I haven’t seen a good example of IoT yet.”  SAS’ Product Manager for IoT could not be reached for comment.

Meanwhile, SAS held its annual analyst conference at a posh resort in Steamboat Springs, Colorado; in his report for Ventana Research, David Menninger gushes.

Herbalife Messes Up, Blames Data Scientists

Herbalife discloses errors reporting non-financial information, blames “database scripting errors.” The LA Times reports; Kaiser Fung comments.

Explainers

— Several items from the morning paper this week:

  • Adrian Colyer explains CryptoNets, a combination of Deep Learning and homohorphic encryption.  By encrypting your data before you load it into the cloud, you make it useless to a hacker.
  • Adrian explains Neural Turing Machines.
  • Adrian explains Memory Networks.
  • Citing a paper published by Google last year, Adrian explains why using personal knowledge questions for account recovery is a really bad thing.

— Data Artisans’ Robert Metzger explains Apache Flink.

— In a video, Eric Kramer explains how to leverage patient data with Dataiku Data Science Studio.

Perspectives

— In InfoWorld, Serdar Yegulalp examines Flink 1.0 and swallows whole the argument that Flink’s “pure” streaming is inherently superior to Spark’s microbatching.

— On the MapR blog, Jim Scott offers a more balanced view of Flink, noting that streaming benchmarks are irrelevant unless you control for processing semantics and fault tolerance.  Scott is excited about Flink ease of use and CEP API.

— John Leonard interviews Vincent de Lagabbe, CTO of bitcoin tracker Kaiko, who argues that Hadoop is unnecessary if you have less than a petabyte of data.  Lagabbe prefers Datastax Enterprise.

— Also in InfoWorld, Martin Heller reviews Azure Machine Learning, finds it too hard for novices.  I disagree.  I used AML in a classroom lab, and students were up and running in minutes.

Open Source Announcements

— Flink announces Release 1.0.  DataArtisans celebrates.

Teradata Watch

CEO Mike Koehler demonstrates confidence in TDC’s future by selling 11,331 shares.

Commercial Announcements

— Objectivity announces that Databricks has certified ThingSpan, a graph analytics platform, to work with Spark and HDFS.

— Databricks announces that adtech company Sellpoints has selected the Databricks platform to deliver a predictive analytics product.

Looking Ahead: Big Analytics in 2016

Every year around this time I review last year’s forecast and publish some thoughts about the coming year.

2015 Assessment

First, a brief review of my predictions for 2015:

(1) Apache Spark usage will explode.

Nailed it.

(2) Analytics in the cloud will take off.

In 2015, all of the leading cloud platforms — AWS, Azure, IBM and Google — released new tools for advanced analytics and machine learning.  New cloud-based providers specializing in advanced analytics, such as Qubole and Domino Data, emerged.

Cloud platform providers do not break out revenue by workload, so it’s difficult to measure analytics activity in the cloud; anecdotally, though, there are a growing number of analysts, vendors and service providers whose sole platform is the cloud.

(3) Python will continue to gain on R as the preferred open source analytics platform.

While Python continues to add functionality and gain users, so does R, so it’s hard to say that one is gaining on the other.

(4) H2O will continue to win respect and customers in the Big Analytics market.

In 2015, H2O doubled its user base, expanded its paid subscriber base fourfold and landed a $20 million “B” round.  Not bad for a company that operates on a true open source business model.

(5) SAS customers will continue to seek alternatives.

Among analytic service providers (ASPs) the exit from SAS is a stampede.

With a half dozen dot releases, SAS’ distributed in-memory products are stable enough that they are no longer the butt of jokes.  Customer adoption remains thin; customers are loyal to SAS’ legacy software, but skeptical about the new stuff.

2016 Themes

Looking ahead, here is what I see:

(1) Spark continues its long march into the enterprise.

With Cloudera 6, Spark will be the default processing option for Cloudera workloads.  This does not mean, as some suggest, that MapReduce is dead; it does mean that a larger share of new workloads will run on Spark.  Many existing jobs will continue to run in MapReduce, which works reasonably well for embarrassingly parallel workloads.

Hortonworks and MapR haven’t followed Cloudera with similar announcements yet, but will do so in 2016.  Hortonworks will continue to fiddle around with Hive on Tez, but will eventually give up and embrace Hive on Spark.

SAS will hold its nose and support Spark in 2016.  Spark competes with SAS’ proprietary back end, but it will be forced to support Spark due to its partnerships with the Hadoop distributors.  Analytic applications like Datameer and Microsoft/Revolution Analytics ScaleR that integrate with Hadoop through MapReduce will rebuild their software to interface with Spark.

Spark Core and Spark SQL will remain the most widely used Spark components, with general applicability across many use cases.  Spark MLLib suffers from comparison with alternatives like H2O and XGBoost; performance and accuracy need to improve.  Spark Streaming faces competition from Storm and Flink; while the benefits of “pure” streaming versus micro-batching are largely theoretical, it’s a serious difference that shows up in benchmarks like this.

With no enhancements in 2015, Spark GraphX is effectively dead.  The project leadership team must either find someone interested in contributing, fold the library into MLLib, or kill it.

(2) Open source continues to eat the analytics software world.

If all you read is Gartner and Forrester, you may be inclined to think that open source is just a blip in the market.  Gartner and Forrester ignore open source analytics for two reasons: (1) they get paid by commercial vendors, and (2) users don’t need “analysts” to tell them how to evaluate open source software.  You just download it and check it out.

Surveys of actual users paint a different picture.  Among new grads entering the analytics workforce, using open source is as natural as using mobile phones and Yik Yak; big SAS shops have to pay to send the kids to training.  The best and brightest analysts use open source tools, as shown by the 2015 O’Reilly Data Science Salary Survey;  while SAS users are among the lowest paid analysts, they take consolation from knowing that SPSS users get paid even less.

IBM’s decision in 2015 to get behind Spark exemplifies the movement towards open source.  IBM ranks #2 behind SAS in advanced analytics software revenue, but chose to disrupt itself by endorsing Spark and open-sourcing SystemML.  IBM figures to gain more in cloud and services revenue than it loses in cannibalized software sales.  It remains to be seen how well that will work, but IBM knows how to spot a trend when it sees it.

Microsoft’s acquisition of Revolution Analytics in 2015 gives R the stamp of approval from a company that markets the most widely implemented database (SQL Server) and the most widely used BI tool (Excel).  As Microsoft rolls out its R server and SQL-embedded R, look for a big jump in enterprise adoption.  It’s no longer possible for folks to dismiss R as some quirky tool used by academics and hobos.

The open source business model is also attracting capital.  Two analytics vendors with open source models (H2O and RapidMiner) recently landed funding rounds, while commercial vendors Skytree and Alpine languish in the funding doldrums and cut headcount.  Palantir and Opera, the biggest dogs in the analytics startup world, also leverage open source.

Increasingly, the scale-out distributed back end for Big Analytics is an open source platform, where proprietary architecture sticks out like a pimple.  Commercial software vendors can and will thrive when they focus on the end user.  This approach works well for AtScale, Alteryx, RapidMiner and ZoomData, among others.

(3) Cloud emerges as the primary platform for advanced analytics.

By “cloud” I mean all types of cloud: public, private, virtual private and hybrid, as well as data center virtualization tools, such as Apache Mesos.  In other words, self-service elastic provisioning.

High-value advanced analytics is inherently project-oriented and ad-hoc; the most important questions are answered only once.  This makes workloads for advanced analytics inherently volatile.  They are also time-sensitive and may require massive computing resources.

This combination  — immediate need for large-scale computing resources for a finite period — is inherently best served by some form of cloud.  The form of cloud an organization chooses will depend on a number of factors, such as where the source data resides, security concerns and the organization’s skills in virtualization and data center management.  But make no mistake: organizations that do not leverage cloud computing for advanced analytics will fall behind.

Concerns about cloud security for advanced analytics are largely bogus: rent-seeking apologetics from IT personnel who (rightly) view the cloud as a threat to their fiefdom.  Sorry guys — the biggest data breaches in the past two years were from on-premises systems.  Arguably, data is more secure in one of the leading clouds than it is in on premises.

For more on this, read my book later this year. 🙂

(4) Automated machine learning tools become mainstream.

As I’ve written elsewhere, automated machine learning is not a new thing.  Commercial and open source tools that automate modeling in various ways have been available since the 1980s.  Most, however, automated machine learning by simplifying the problem in ways that adversely impact model quality.  In 2016, software will be available to enterprises that delivers expert-level predictive models that win Kaggle competitions.

Since analysts spend 80% of their time data wrangling, automated machine learning tools will not eliminate the hiring crunch in advanced analytics; one should be skeptical of vendor claims that “it’s so easy that even a caveman can do it.”  The primary benefit of automation will be better predictive models built consistently to best practices.  Automation will also expand the potential pool of users from hardcore data scientists to “near-experts”, people with business experience or statistical training who are not skilled in programming languages.

(5) Teradata continues to struggle.

Listening to Teradata’s Q3 earnings call back in November, I thought of this:

100_anniversary_titanic_sinking_by_esai8mellows-d4xbme8

CEO Mike Koehler, wiping pie from his face after another quarterly earnings fail, struggled to explain a coherent growth strategy.  It included (a) consulting services; (b) Teradata software on AWS; (c) Aster on commodity hardware.

Well, that dog won’t hunt.

— Teradata’s product sales drive its consulting revenue.  No product sales, no consulting revenue.   Nobody will ever hire Teradata for platform-neutral enterprise Big Data consulting projects, so without a strategy to build product sales, consulting  revenue won’t grow either.

— Teradata’s principal value added is its ability to converge software and hardware into an integrated appliance.  By itself, Teradata software itself is nothing special; there are plenty of open source alternatives, like Apache Greenplum.  Customers who choose to build a data warehouse on AWS have many options, and Teradata won’t be the first choice.  Meanwhile, IBM, Microsoft and Oracle are light years ahead of Teradata delivering true hybrid cloud databases.

— Aster on commodity hardware is a SQL engine with some prebuilt apps.  It runs through MapReduce, which was kind of cool in 2012 but DOA in today’s market: customers who want a SQL engine that runs on commodity hardware have multiple open source options, including Presto, which Teradata also embraces.

Meanwhile, Teradata’s leadership team actually spent time with analysts talking about the R&D tax credit, which seemed like shuffling deck chairs.  The stock is worth about a third of its value in 2012 because the company has repeatedly missed earnings forecasts, and investors have no confidence in current leadership.

At current market value, Teradata is acquisition bait, but it’s not clear who would buy it.  My money’s on private equity, who will cut headcount by half and milk the existing customer base.   There are good people at Teradata; I would advise them all to polish their resumes.

2015 in Big Analytics

Looking back at 2015, a few stories stand out:

  • Steady progress for Spark, punctuated by two big announcements.
  • Solid growth in cloud-based machine learning, led by Microsoft.
  • Expanding options for SQL and OLAP on Hadoop.

In 2015, the most widely read post on this blog was Spark is Too Big to Fail, published in April.  I wrote this post in response to a growing chorus of snark about Spark written by folks who seemed to know little about the project and its goals.

IBM Embraces Spark

IBM’s commitment to Spark, announced on Jun 15, lit up the crowds gathered in San Francisco for the Spark Summit.  IBM brings a number of things to Spark: deep pockets to build a community, extensive technical resources and a large customer base.  It also brings a clutter of aging and partially integrated products, an army of suits and no less than 164 Vice Presidents whose titles include the words “Big Data.”

When IBM announced its Spark initiative I joked that somewhere in the bowels of IBM, someone will want to put Spark on a mainframe.  Color me prophetic.

It’s too early to tell what substantive contributions IBM will make to Spark.  Unlike Mesosphere, Typesafe, Tencent, Palantir, Cloudera, Hortonworks, Huawei, Shopify, Netflix, Intel, Yahoo, Kixer, UC Berkeley and Databricks, IBM did not help test Release 1.5 in September.  This is a clear miss, given the scope of IBM’s resources and the volume of hype it puts out about its commitment to the project.

All that said, IBM brings respectability, and the assurance that Spark is ready for prime time.  This is priceless.  Since IBM’s announcement, we haven’t heard a peep from the folks who were snarking at Spark earlier this year.

Cloudera Announces “One Platform” Initiative

In September, Cloudera announced its One Platform initiative to unify Spark and Hadoop, an announcement that surprised everyone who thought Spark and Hadoop were already pretty well integrated.  As with the IBM announcement, the symbolism matters.  Some analysts took this announcement to mean that Cloudera is replacing MapReduce with Spark, which isn’t exactly true.  It’s fairer to say that in Cloudera’s vision, Hadoop users will rely more on Spark in the future than they do today, but MapReduce is not dead.

The “One Platform” positioning has more to do with Cloudera moving to stem the tide of folks who use Spark outside of Hadoop.  According to Databricks’ recent Spark user survey, only 40% use Spark under YARN, with the rest running in a freestanding cluster or on Mesos.  It’s an understandable concern for Cloudera; I’ve never heard a fish seller suggest that we should eat less fish.  But if Cloudera thinks “One Platform” will stem that tide, it is mistaken.  It all boils down to use cases, and there are many use cases for Spark that don’t need Hadoop’s baggage.

Microsoft Builds Credibility in Analytics

In 2015, Microsoft took some big steps to demonstrate that it offers serious solutions for analytics.  The acquisition of Revolution Analytics, announced in January, was the first step; in one move, Microsoft acquired a highly skilled team and valuable software assets.  Since the acquisition, Microsoft has rolled Revolution’s enhanced R distribution into SQL Server and Azure, opening both platforms to the large and growing R community.

Microsoft’s other big move, in February, was the official launch of Azure Machine Learning (AML).   First released in beta in June 2014, AML is both easy to use and powerful.  The UI is simple to understand, and documentation is excellent; built-in analytic functionality is very rich, and the tool is extensible with custom R or Python scripts.  Microsoft’s trial user program is generous, and clearly designed to encourage adoption and use.

Azure Machine Learning contrasts markedly with Amazon Machine Learning.  Amazon’s offering remains a skeleton, with minimal functionality and an API only a developer could love.  Microsoft is clearly making a play for the data science market as a way to leapfrog Amazon.  If analytic capabilities are driving your choice of cloud platform, Azure is by far your best option.

SQL Engines Proliferate

At the beginning of 2015, there were two main options for SQL on Hadoop: Hive for batch SQL and Impala for interactive SQL.  Spark SQL was still in Alpha; Drill was a curiosity; and Presto was something used at Facebook.

Several things happened during the year:

  • Hive on Tez established rough performance parity with the fast SQL engines.
  • Spark SQL went to general release, stabilized, and rolled out the DataFrames API.
  • MapR promoted Drill, and invested in improvements to the software.  Also, MapR’s Drill team spun off and started Dremio to provide commercial support.
  • Cloudera donated Impala to open source, and Pivotal donated Hawq.
  • Teradata placed its chips on Presto.

While it’s great to see so many options emerge, Hive continues to win actual evaluations.  Given Hive’s large user and contributor base and existing stock of programs, it’s unclear how much traction Hive alternatives have now that Hive on Tez offers competitive performance.  Obviously, Cloudera doesn’t think Impala offers a competitive advantage anymore, or they would not have donated the assets to Apache.

The other big news in SQL is TPC’s release of a benchmarking standard for decision support with Big Data.

OLAP on Hadoop Gets Real

For folks seeking to perform dimensional analysis in Hadoop, 2015 delivered not one but two options.  The open source option, Apache Kylin, originally an eBay project, just recently graduated to Apache top level status.  Adoption is limited at present, but any project used by eBay and Baidu is worth a look.

The commercial option is AtScale, a company that emerged from stealth in April.  Unlike BI-on-Hadoop vendors like Datameer and Pentaho, AtScale provides a dimensional layer designed to work with existing BI tools.  It’s a nice value proposition for companies that have already invested big time in BI tools, and don’t want to add another UI to the mix.

Funding for Machine Learning

H2O.ai’s recently announced B round is significant for a couple of reasons.  First, it validates H2O.ai’s true open source business model; second, it confirms the continued growth and expansion of the user base for H2O as well as H2O.ai’s paid subscription base.

Like Sherlock Holmes’ dog that did not bark, two companies are significant because they did not procure funding in 2015:

  • Skytree, whose last funding round closed in April 2013, churned its executive team and rebranded a couple of times.  It finally listed some new customers; interestingly, some are investors and others are affiliated with members of Skytree’s Board.
  • Alpine Data Labs, last funded in November 2013, struggled to distance itself from the Pivotal ecosystem.  Designed to run on Greenplum, Alpine offers limited functionality on Hadoop, which makes it unclear how this company survives.

Palantir continued to suck up capital like a whale feeding on krill.

Google TensorFlow

Google open sourced TensorFlow, so now we have sixteen open source Deep Learning frameworks instead of just fifteen.

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.