The Year in Machine Learning (Part Three)

This is the third installment in a four-part review of 2016 in machine learning and deep learning. In Part One, I 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. In Part Two, I surveyed significant developments in Open Source machine learning projects, such as R, Python, Spark, Flink, H2O, TensorFlow, and others.

In this installment, we will review the machine learning and deep learning initiatives of Big Tech Brands — industry leaders with big budgets for software development and marketing. Big Tech Brands fall into three groups:

— SAS is the software revenue leader in predictive analytics. It has a unique business model and falls into its own category.

— Companies such as IBM, Microsoft, Oracle, SAP, and Teradata have all have strong franchises in the data warehousing market, and all except Teradata offer widely used business intelligence software. These companies have the financial strength to develop, market and cross-sell machine learning software to their existing customer base, and can impact the market if they choose to do so.

Dell and HPE dabbled in advanced analytics and exited the market in 2016.

I covered Google and Amazon Web Services in Part One. Although neither company has a strong position in business analytics at present, they are making moves in that direction. Google set up Google Cloud Machine Learning as a distinct product group this year to service that market, and Amazon introduced QuickSight, a business analytics service.

Regular readers know that I favor open source software — as do most data scientists. Among the companies covered in this installment, IBM and Microsoft are making substantial commitments to the open source model, including direct contributions to open source software projects. They deserve kudos for that. Teradata is investing in Presto SQL, for which they get polite applause. Oracle and SAP leverage open source software in their solutions but make no significant contributions. SAS embraces open source the way a cat embraces a porcupine.

In Part Four, I will survey machine learning startups, and deliver results from the Bottom Story of the Year poll.

SAS

SAS leads the market in licensing revenue for advanced and predictive analytics software, according to IDC. The company has a loyal following among statisticians, actuaries, life scientists and others whose work depends on statistical analysis.

Partnering with IBM, SAS built its business in the 1970s on the strength of its software for the IBM System/360 mainframe. IBM promoted the software to its enterprise customers to increase adoption and use of its hardware. SAS software still runs on the mainframe, and the company continues to earn a significant share of its revenue on that platform. IBM has mainframe customers who use the big box exclusively for SAS.

In the 1990s, SAS successfully transitioned to a multi-vendor architecture and rebuilt its software to run on many different hardware platforms and operating systems. During this period, SAS established a reputation for industrial-strength and enterprise-grade software — in contrast to vendors like SPSS, who focused on building easy-to-use software for the desktop.

On the face of it, SAS has struggled to transition from server-based computing to the contemporary world of distributed architecture and cloud platforms. In the past ten years, the company has announced multiple initiatives to improve the performance and scalability of its products, with mixed success. In April, SAS announced Viya, its third attempt to deliver advanced analytics in a distributed MPP architecture.

What is SAS Viya? How does it differ from SAS’ previous attempts at high-performance design? Let’s peruse the brochure:

Cloud-ready, elastic and scalable

 

SAS Viya is built to be elastic and scalable for both private and public clouds. Analytical, in-memory computations are optimized for unconstrained environments, but they can also adjust for constrained environments. The elastic processing automatically adapts to needs and available resources – spinning up or winding down computing capacity as needed. Elastic scalability lets you quickly experiment with different scenarios and apply more complex approaches to larger amounts of streaming data.

Ahem. Any software is “cloud-ready,” in the sense that a Linux instance is a Linux instance whether it runs on-premises or in the cloud. And any software is elastic when you deploy it in a virtual appliance, such as an Amazon Machine Image. That includes SAS 9.4, which SAS touted as “cloud-ready” in 2014, and previous versions of SAS, which you could deploy in AWS even though SAS did not formally support the platform.

If you want to spin up software instances, however, you need software licenses. With open source software, such as Python, R, or Spark, that’s not an issue — you can spin up as many instances as you like without violating license agreements. Commercial software is more complicated since you need to pay for the licenses you want to spin up. Some vendors, like HPE and Teradata, tried to address this problem by marketing their own cloud platforms to compete with Amazon Web Services; they failed miserably. Others, like Oracle, partner with AWS to deliver their software in the cloud — either as a bundled managed service or on a “Bring Your Own License” (BYOL) model.

You can’t have elastic computing with commercial software without a flexible licensing model. Pay-for-what-you-use licensing poses a problem for vendors like SAS, because if customers only pay for what they use, they invariably pay a lot less than they do under term licensing. Most commercial software customers are over-licensed — they’re paying for a lot of software they don’t use. That is why revenue from on-premises software licensing is declining much faster than revenue from cloud-based subscriptions is rising. In the cloud, you can do more with less.

The bottom line is this: unless Viya is available under an elastic pricing model, nobody cares that it is “cloud-ready, elastic and scalable.”

If you want to have a little fun, the next time your SAS rep touts Viya’s elasticity, ask him what it will cost per hour to license the software. Watch him squirm.

Open analytics coding environment

 

Empower your data scientists with SAS Analytics that are easily available from a variety of programming languages. Whether it’s a Python notebook, Java client, Lua scripting interface or SAS, your modelers and data scientists can easily access the power of SAS for data manipulation, advanced analytics and analytical reporting.

We’ve all been waiting for the ability to run SAS from Lua.

Resilient architecture with guaranteed failover

 

For answers you depend on, you need analytical processing power you can count on. You need all your analytical computations to finish processing without interruption. The fault-tolerant design of SAS Viya automatically detects server failure, even in multiplatform processing environments, and redistributes processing as needed. It also manages several copies of data on the processing cluster. If a machine in the cluster becomes unavailable or fails, the required data is retrieved from another block to quickly continue processing. These self-healing mechanisms ensure high availability for uninterrupted processing and automated recovery.

“It runs on Hadoop.”

Interviewed in Forbes, SAS CEO Jim Goodnight speaks at length about Viya:

We are ready for big data…(we) just released our first version of our new Viya architecture, which is massively parallel computing where we spread the data out over dozens of servers and then use all the cores inside those servers to process the data in parallel. So we might have 500 cores working on the data all at once in parallel, and that allows it to handle some really, really big problems that we’ve never even thought of before. Things like logistic regression.

Someone should feed Dr. G. better talking points. Just for the record, commercially available software for logistic regression running in a massively parallel (MPP) environment first hit the market in 1989. Distributed logistic regression is currently available in multiple software packages, including one introduced by SAS five years ago.

Logistic regression (a non-linear model) is an iterative process. Essentially, you’re trying to estimate the parameters in the model, and so you take a guess, you’ve got to run through the data using that guess, then to refine it and do another guess and run through the data again, and you keep doing this over and over and over until the parameters converged or they don’t change much at all anymore. That can take 25 to 30 passes of the data. Now, in the old days, we used to have to read the data that many times. Now, it’s in memory. We put it in memory and it stays in memory. It’s spread out over 500 cores and then each one just does a little piece of the work, and so we can do those 25 iterations in just a few minutes, whereas it used to take hours.

It’s just like Spark, but with a license key.

(Viya’s) really our third generation of massively parallel computing. We’ve been working on this problem for seven years, and this is our third major crack at doing it, and this time we’ve got everything figured out.

In 2018 he’ll be talking about a fourth crack in nine years.

It’s possible that Viya works better than SAS’ previous cracks at high-performance analytics. That is a weak hurdle, however; SAS needs to demonstrate that its high-cost proprietary distributed framework is better than Apache Spark, which is rapidly emerging as the standard enterprise platform for Big Data.

While SAS supports machine learning techniques in several different products, it lags in deep learning. The SAS Marketing team created some helpful content about deep learning, but look carefully at that page — you won’t find an actual product for deep learning. Yes, I know that SAS Enterprise Miner supports multilayer perceptrons; but SAS does not support GPUs, Xeon Phi, Intel Nervana or any other high-performance architecture that will make it possible for you to train a deep neural net while you’re young.

If you think that an eighteen-year-old product running on one server is sufficient for your deep learning project, you should definitely talk to SAS. Keep in mind, though, that there is a reason that NVIDIA’s DGX-1 GPU-accelerated deep learning box has the power of 250 conventional servers: you actually need that kind of horsepower.

The rest of SAS’ business seems to be chugging along well enough. A combination of renewals, upgrades and upsells in existing accounts should produce low single-digit revenue growth for 2016, which is not a bad track record when you consider the declines reported by IBM, Oracle, and Teradata.

Business Analytics Leaders

The five companies in this group sell at least a billion dollars a year in business analytics software, according to IDC’s most recent worldwide software market share report. However, most of their revenue comes from data warehousing and business intelligence software; they all trail SAS in predictive analytics revenue.

Software licensing revenue is a misleading measure, however, due to the growing presence of open source software. IBM, Microsoft, and Oracle for example, actively use open source machine learning software to extend the reach of their data warehousing and business intelligence platforms, where they both have strong entries. IBM uses Spark as a foundation for many of its products; Microsoft has integrated R with SQL Server and PowerBI, and actively promotes the use of R for its enterprise customers. Oracle has taken a similar approach.

IBM

Unlike SAS, declining tech giant IBM never invested in a proprietary distributed framework for SPSS, its flagship software for advanced analytics. Instead, the company chose to leverage in-database engines (DB2, Netezza, and Oracle) and open source frameworks (MapReduce and Spark.)

IBM contributes to Apache Spark, which it uses in several products, and also to Apache SystemML. IBM Research developed the core of SystemML, which IBM donated to Apache in 2015. IBM has also visibly contributed to the Spark community through its efforts in education and training.

In 2016, IBM continued to market SPSS Statistics and SPSS Modeler, software brands it acquired in 2007. Release 18 of SPSS Modeler, announced in March, includes such things as support for machine learning in DB2 and support for IBM’s General Parallel File System (GPFS) in BigInsights. There aren’t too many data scientists who care about such things, but they appeal to the 150 or so enterprises with CIOs who still believe that nobody ever got fired for buying IBM.

In Part One of this review, I covered IBM’s machine learning moves in IBM Cloud, which I would characterize as Shakespearean, as in Much Ado About Nothing.

Microsoft

Microsoft had quite a year in machine learning and deep learning. As I noted in Parts One and Two, in 2016 MSFT launched cognitive APIs in Azure for vision, speech, language, knowledge, and search; a managed service for Spark in Azure HDInsight; enhancements to Azure Machine Learning and Version 2.0 of its deep learning framework, rebranded as Microsoft Cognitive Toolkit.

That’s just for starters.

In January, Microsoft announced Microsoft R Server, a rebranding of the product it acquired with Revolution Analytics in 2015. Microsoft R Server includes an enhanced R distribution, a scalable back-end, and integration tools. During the year, Microsoft two major releases for R Server. In Release 8, the company added push-down integration with Spark. Release 9 updated the Spark integration for Spark 2.0, and added MicrosoftML, a new R package for machine learning.

Microsoft announced SQL Server 2016 in March with embedded SQL Server R Services. On the Revolutions blog, David Smith reports on the launch. Tomaž Kaštrun explains what you can do with R services in SQL Server.

In November, after an extended preview, Microsoft announced the general availability of R Server for Azure HDInsight, a scale-out implementation of R integrated with Spark clusters created from HDInsight.

Also in Azure, Microsoft added a Linux version of the Data Science Virtual Machine (DSVM). Previously available as a Windows instance, DSVM includes Revolution R Open, Anaconda, Visual Studio Community Edition, PowerBI Desktop, SQL Server Express and the Azure SDK.

PowerBI, Microsoft’s powerful visualization tool, added R support in August. In ComputerWorld, Sharon Machlis, an R user, enthused. More here, on the Revolutions blog.

R Tools for Visual Studio launched to public preview in March, and to general availability in September. Also in September, Microsoft released the Microsoft R Client, a free data science tool that works with Microsoft R Open and the ScaleR distributed back end.

Microsoft data scientists Gopi Krishna Kumar, Hang Zhang and Jacob Spoelstra developed a methodology for data science, which they presented at the Microsoft Machine Learning and Data Science Summit 2016 in September. David Smith reports. The method, which the authors call Team Data Science Process, includes a standard directory structure for managing project artifacts using a system such as Git. It also includes open source utilities to support the process.

Other than that, it was a quiet year in Redmond.

Oracle

Oracle has a surprisingly robust set of machine learning tools that appeal to Oracle-centric organizations. They include:

Oracle Data Mining (ODM), a suite of machine learning algorithms that run as native SQL functions in Oracle Database.

Oracle Data Miner, a client application for ODM with a business user interface.

Oracle R Distribution (ORD), an enhanced free R distribution.

Oracle R Enterprise (ORE), Oracle R Distribution packaged with tools to integrate R with Oracle Database.

Oracle R Advanced Analytics for Hadoop (ORAAH), a set of R bindings with native algorithms and an interface to Spark.

Oracle claims that ORAAH’s native algorithms are faster than Spark, but ORAAH has only two algorithms, so nobody cares. Oracle OEMs Cloudera, so the Spark release is at least one major release behind the rest of the world.

Other than some dot releases for the components cited above, I don’t see a lot of movement for Oracle in 2016.

SAP

SAP introduced an update to its predictive analytics capabilities, now branded as SAP Business Objects Predictive Analytics 3.0. This product includes two separate automation capabilities, one branded as Predictive Factory, the second as HANA Automated Predictive Library. Predictive Factory, like SAS Factory Miner, is a scripting tool that enables a data scientist to create a modeling pipeline and schedules it for execution; it does not automate the data science process itself.  HANA Automated Predictive Library is a set of functional calls that users can include in SQL scripts.

HANA Automated Predictive Library is a set of functional calls that users can include in SQL scripts. It’s a product that might appeal to SAP HANA bigots and nobody else.

SAP acquired KXEN and its InfiniteInsight software in 2014. Customer satisfaction promptly dropped through the floor, and SAP trails all other advanced analytics vendors rated in a Gartner survey. Legacy InfiniteInsight customers fall into two camps: (a) those whose IT organizations are heavily invested in SAP, and (b) everyone else. The former seem to be sticking with the software as SAP integrates it into its product line; the latter are heading for the exits.

Teradata

Declining data warehouse vendor Teradata thinks of itself as an analytics powerhouse. In reality, most of its revenue comes from data warehousing, where the company gets high marks from analysts like Gartner.

You could say that Teradata has a commanding position at the bottom of the analytics stack.

Teradata’s executive leadership — if you can call it that — completely missed the implications of Hadoop and cloud computing. Instead, they bet that the Teradata brand was beloved by IT executives, who would keep on buying boxes in bulk. As a result of that blinkered view of the world, the company today is worth a third of what it was worth five years ago. Its product sales have declined for ten straight quarters, seven in a row at double digits.

After a dismal first quarter, Teradata’s board fired accepted the resignation of CEO Mike Koehler; longtime board member Victor Lund stepped into the breach. In September, at the Teradata Partners conference, Lund announced that Teradata would reposition itself as an “analytics solutions” firm.

That may not sit well with SAS, Teradata’s primary partner for advanced analytics software, which also views itself as an “analytic solutions” firm. The difference, of course, is that SAS has been delivering solutions for a long time and has street cred with executives because it actually has sophisticated business solutions, with actual software and intellectual property, while Teradata appears to have little more than big ideas and PowerPoint.

Pro tip for Teradata management: just because you want to move up the value chain does not mean that you have the ability to do so.

In other developments, the company announced that Aster finally supports Spark, two years after anyone might have cared. Teradata also announced that Aster’s analytics are now available for deployment in Hadoop. Aster on Hadoop is a bladeless knife without a handle — a commercial machine learning library that competes with umpteen open source libraries. Aster also competes with another Teradata partner, Fuzzy Logix, whose dbLytix library is six times richer and more mature.

If someone proposes to bet that “solutions” and unbundled Aster will reverse Teradata’s decline, take the under.

Other Tech Giants

We mention two remaining giants, Dell and HPE, only to note their passing from the scene.

HPE

HPE announced the sale of its software assets (including Vertica and Haven) to U.K.-based Micro Focus for $2.5 billion in cash. Under terms of the deal, Micro Focus also granted equity with a soft valuation of $6.3 billion directly to HPE shareholders. HPE paid almost $20 billion over ten years for these assets. The valuation works out to about 2.4 times revenue, which means that both parties agree the business has little or no growth potential. Micro Focus has a reputation for firing people cutting costs, so if you’re working for Haven or Vertica, this may be a good time to dust off your resume.

In March, HPE announced Haven OnDemand, available on Microsoft Azure. Haven is a loose bundle of software assets salvaged from the train wreck of Autonomy, Vertica, ArcSight and HP Operations Management machine learning suite, initially branded as HAVEn and announced by HP in June 2013.  In 2015, HP released Haven on Helion Public Cloud, HP’s failed cloud platform. So the March announcement is a re-re-release of the software.

Three years into its product life cycle, Haven hasn’t exactly caught on with data scientists. Just 2 out of 2,895 respondents to the KDnuggets 2016 Data Science Software Usage poll and none in the O’Reilly 2016 Data Science Salary Survey said they use the software. Adding insult to injury, Haven failed to make KDnuggets’ list of the top 50 machine learning APIs, a list that includes the likes of Ersatz, Hutoma, and Skyttle.

Vertica still has some traction with data lovers whose analysis needs are simple enough to satisfy with SQL. Currently, it’s the 28th most popular relational database, according to DB-Engines, which is about on par with Netezza and Greenplum and a lot better than Aster. Expect this ranking to drop like a stone in the hands of Micro Focus.

Dell/EMC

Dell entered the advanced analytics business by acquiring Statsoft in 2014, a move that impressed nobody. In 2016, Dell exited by selling its software division to private equity investors.

Goodbye, Dell. We hardly knew ye.

Disruption: It’s All About the Business Model

This post is an excerpt adapted from my book, Disruptive Analytics, available soon from Apress and Amazon. (Note: under my contract with Apress I am legally obligated to link to their site, but it’s not yet possible to order the book there. Use the Amazon link if you want the book.)

The analytics business is booming. Technology consultant IDC estimates total spending for analytic services, software and hardware exceeded $120 billion in 2015; through 2019, IDC forecasts that spending will increase to $187 billion, an 11% compound annual growth rate.

Powerful forces are at work in the economy today:

  • Digital transformation of the economy and rapidly declining storage costs combine to create a flood of data.
  • The number of data sources is exploding. Data sources are everywhere: on-premises, in the cloud, in consumers’ pockets, in vehicles, in RFID chips, and so forth.
  • The “long march” of Moore’s Law: cheap computing power makes machine learning and deep learning techniques practical.

So, if analytics is such a hot field, why are the industry leaders struggling?

  • Oracle’s cloud revenue growth fails to offset declining software and hardware sales.
  • SAP’s cloud revenue grows, but total software revenue is flat.
  • IBM reports seventeen straight quarters of declining revenue. Mass layoffs
  • Microsoft underperforms analysts’ expectations despite 120% growth in Azure cloud revenue.
  • Predictive analytics leader SAS reports five years of low single-digit revenue growth; Executive Vice President and Chief Marketing Officer departs.
  • Data warehousing leader Teradata shuffles its leadership team after four years of declining product revenue.

Product quality is not the problem. Each company offers products that industry analysts rate highly:

  • Forrester and Gartner recognize IBM, SAS, SAP and Oracle as leaders in data quality tools.
  • Gartner rates Oracle, SAP, IBM, Microsoft and Teradata as leaders in data warehousing.
  • Forrester rates Microsoft, SAP, SAS, and Oracle as leaders in agile business intelligence.
  • Gartner recognizes SAS and IBM as leaders in Advanced Analytics.

The answer, in a word, is disruption. Clayton Christensen of the Harvard Business School outlined the theory of disruptive innovation in 1997. Summarizing the argument briefly:

  • Industries consist of value networks, collections of suppliers, channels, and buyers linked by relationships.
  • Innovations disrupt industries when they create a new value network.
  • Not all innovations are disruptive. Many are introduced by market leaders to sustain a competitive position.
  • Disruptive innovations tend to be introduced by outsiders.
  • Purely technological innovation is not disruptive; what matters is the business model enabled by the new technology.

For a more detailed exposition of the theory, read Christensen’s book.

Christensen identified two forms of disruption. Low-end disruption occurs when industry leaders enhance products faster than customers can assimilate the enhancements; the disruptor enters the market with a “good enough” product and a better value proposition. The disruptor’s innovation makes it possible to serve customers at a lower cost than the industry leaders can deliver.

New market disruption takes place when the disruptor innovates in ways enabling it to serve customers that are not served by the industry leaders.

Technology alone does not disrupt industries; incumbents can and do innovate. New business models enabled by new technology are the cutting edge of disruption. Frequently, incumbents cannot respond effectively to new business models; this is partly due to “blinders” caused by changing value networks, and partly out of fear of cannibalizing existing business arrangements. Two business models, in particular, are disrupting the business analytics world today:

  • Open source software business models offer an increasingly attractive alternative to commercial software licensing. The Hadoop ecosystem displaces conventional data warehousing; R and Python displace commercial software for advanced analytics.
  • The elastic business model made possible by cloud computing undercuts conventional software licensing. When customers pay only for what they use, they pay a lot less.

Disruption does not mean that leading companies like Oracle, IBM and SAS will go out of business. Blockbuster may be the poster child for disrupted businesses, but most cases are less dire; for the business analytics leaders, disruption means they will struggle to grow. Slow growth is less benign than it sounds. As McKinsey notes, the rule today is “Grow or Go”: companies that cannot define a credible growth strategy will be acquired by other companies or by private equity.

The alternative to revenue growth is increasing profitability. But when revenue is flat or declining, that usually means job cuts.

job-cuts
Disruption looks like this.

Consider what happened to Teradata. Late in 2012, the company started missing sales targets; in early 2013, it stunned investors by reporting an absolute decline in sales. Management offered excuses; Wall Street punished the stock, driving it down by half in the face of a bull market for tech stocks.

Teradata’s leadership continued to miss sales and earnings targets; Wall Street drove the stock price down to a fraction of its 2012 peak. While it is tempting to blame the problem on poor leadership, Teradata’s persistent failure to accurately forecast its sales and earnings is a clear sign that its leadership no longer understood the value networks in which they operated. The world had changed; the value networks created in Teradata’s rise to leadership no longer existed; the mental models managers used to understand the market no longer worked.

There are two distinct types of disruption. The first is disruptive innovation within the analytics value chain. Here are two recent examples:

Hadoop. The Hadoop ecosystem disrupts the data warehousing industry from below. Hadoop does not do everything a relational database can do, but it does just enough to offer an attractive value proposition for the right use cases. When first introduced, Hadoop’s capabilities were very limited compared to data warehouse appliances. But Hadoop’s flexibility and low cost were highly attractive for applications that did not need the performance and features of a data warehouse appliance. While established vendors struggle to maintain flat and declining revenue, companies that offer solutions built on Hadoop grow at double-digit rates.

Tableau. Tableau virtually created the market for agile, self-service discovery. The charting and visualization features in Tableau are available in mainstream business intelligence tools. But while business intelligence vendors target the IT organization and continually add complexity to their product, Tableau targets the end user with a simple, easy to use and versatile tool. As a result, Tableau has increased its revenue tenfold in five years, leapfrogging over many other BI vendors.

Disruption within the analytics value chain is pertinent for readers who plan to invest in analytics technology for their organization. Technologies at risk of disruption are risky investments; they may have abbreviated useful lives, and their suppliers may suffer from business disruption. Taking a “wait-and-see” attitude towards disrupted technologies makes good sense, if only because prices will likely decline in the future.

The second type is disruption by innovations in analytics. Examples of disruption by analytics are harder to find, but they do exist:

Credit Scoring. General-purpose credit scoring introduced by Fair, Isaac and Co. in 1987 virtually created a national market in credit cards.  Previously, banks issued credit cards to their local customers, with whom they had an established relationship. Uniform credit scoring enabled a few large issuers to identify creditworthy clients in the general population, without a prior relationship.

Algorithmic Trading. When the U.S. Securities and Exchange Commission authorized electronic trading in regulated securities in 1998, market participants quickly moved to develop algorithms that could arbitrage between markets, arbitrage between indexes and the underlying stocks and exploit other short-term opportunities. Traders that most effectively deployed machine learning for electronic trading grew at the expense of other traders.

For startups and analytics practitioners, disruption by analytics is essential. Startups must disrupt their industries if they want to succeed. Using analytics to differentiate a product is a way to create a disruptive business model or to create new markets.

There is a common theme across the four examples: the business model enabled by the technology and not the technology itself drives the disruption. Hadoop and Tableau do less than the legacy products they compete against; what they do, however, is sufficient for a class of use cases, for which they provide a better value proposition. Credit scoring and algorithmic trading created fundamentally new ways to lend and invest; while these applications attracted technological innovations as they expanded, it was the new business models they created that disrupted the lending and investing industries.

To illustrate the importance of the business model, consider the case of columnar serialization, a significant innovation in data warehousing that did not disrupt the industry. In 2005, Vertica introduced a commercial columnar database, a technology that is well-suited to high-performance analytics (as we explain in Chapter Two of Disruptive Analytics). Vertica successfully built a customer base, but did not create a unique business model; by 2010 the leading data warehouse vendors had introduced columnar serialization into their products. HP acquired Vertica in 2011 for about $250 million, a price well below the $1.7 billion IBM paid for Netezza, a competing data warehouse appliance vendor.

Here are some takeaways for the reader to consider.

First, if you want to invest in new business analytics technology, ask yourself:

  • Are we paying for what we use, or for what we might use?
  • What particular value do commercial software options offer over open source alternatives?

Second, if you want to use analytics to create a disruptive innovation, ask yourself:

  • What new business model does this support?
  • Can we disrupt incumbents from below with a better value proposition?
  • Can we reach new markets and new customers who are underserved by existing value networks?

There is one additional takeaway: nobody ever disrupted anything by managing data. Keep that in mind the next time a data warehousing vendor tries to tell you that their Big Box is a “strategic” investment. We’ll explore that in another excerpt from the book.

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.

Screen Shot 2016-08-08 at 10.38.16 AM

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:

Screen Shot 2016-07-18 at 8.26.11 AM

— 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 (July 25, 2016)

We have some more summer reading this week; plus, Splice Machine announces availability of its open source Community Edition, and Google launches two new machine learning APIs. There are so many Spark stories I’ve created a special section for them. Plus we have the usual explainers, perspectives, and news.

Quant headhunter Linda Burtch repeats her survey of working analysts in her network. Preference for using SAS has steadily declined over the three years she has conducted the poll; this year a clear majority chose R or Python over SAS. Preference for open source correlates with education; the more you know, the less likely you are to use SAS.

Oracle, IBM, SAP, and Microsoft have all reported Q2 revenue and earnings, but Teradata is still crunching the numbers. I’ll do a general earnings roundup when TDC gets around to reporting its numbers. TDC’s stock price has outperformed the others since June 30, which suggests the market expects a good second quarter. Meanwhile, TDC acquires another consultancy and reveals who bought Aprimo.

Summer Reading

Adrian Colyer lists his five favorite papers from the past several months and outlines his philosophy, which you must read. And here is another link to last week’s top paper on data bazaars versus data cathedrals.

Splice Machine Shifts to Open Core

Hadoop-based RDBMS vendor Splice Machine announces general availability for its open source community edition and offers a sandbox hosted on AWS.  Sam Dean approves; Andrew Brust reports; Dave Ramel explains. Jack Germain describes Splice Machine’s changing business model.

Spark Stories

— Databricks’ Spark survey is still accepting responses. Go and fill it out if you have not done so already.

— The Spark PMC has voted favorably on a release candidate for Spark 2.0, which is now in packaging for general availability.

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

— Alex Woodie touts LevyxSpark, an enhanced Spark distribution based on open source Apache Spark. LevyxSpark includes some open source enhancements, plus Levyx Helium, an SSD-based key-value store.

— In a webcast, Alexander Ulanov summarizes options for deep learning on Spark.

— Sam Weaver explains how to use the new MongoDB connector for Spark.

Explainers

— Nita Dembla and Gopal Vijayaraghavan explain improvements in Hive 2.1.

— Siddharth Anand introduces Apache Airflow (Incubating), a platform to author, schedule, and monitor DAGs. Sounds like Apache Beam.

— Data Artisans’ Stephan Ewan explains savepoints in Apache Flink.

Perspectives

— Jack Clark profiles Google’s land grab in deep learning. Short version: TensorFlow is blowing away Caffe, Torch, Theano, dl4j, CNTK, and DSSTNE.

— Greg Satell theorizes about Google’s open source strategy as if a “razor and blades” strategy is something new and brilliant.

— In Fortune, Barb Darrow profiles cloud computing’s disruptive impact.

— Sam Dean confuses machine learning with artificial intelligence.

— Syncsort’s Paige Roberts interviews Dr. Ellen Friedman.

— Drew Breunig poses a theory about the business implications of machine learning.

— BuzzFeed’s Adam Kelleher attempts to explain bias, fails.

— IBM exec Rob Thomas co-authors a blog about machine learning. It’s about what you would expect from an IBM exec.

Open Source News

— Open source columnar storage engine Apache Kudu graduates to top-level status.

— Apache Chukwa announces Release 0.8, with security bug fixes, FWIW. Chukwa captures logs from distributed systems for monitoring and analysis. No, I never heard of it either.

Commercial Announcements

— Google announces open beta for its Cloud Natural Language and Cloud Speech APIs.

Hardware News

— Inspur, which claims to be China’s largest server manufacturer, announces availability of the Memory1 line of servers for big analytics. Inspur uses high-capacity flash DIMMs and memory expansion software to deliver up to 2TB of memory per server and up to 80TB per rack.

— Startup Wave Computing announces plans for a family of deep learning computers. Good luck to them. The history of computing isn’t kind to special purpose machines, which tend to eventually get buried by general purpose machines.

Funding News

— Redis Labs lands a $14 million “C” round led by Bain Capital and Carmel Ventures. Redis claims 6,200 enterprise customers and 55,000 accounts for its cloud service.

— Sift Security emerges from stealth, announces $3.25 million in angel funding. Sift uses graph analytics running on Spark and TitanDB to identify linked threats and incidents.

Big Analytics Roundup (July 18, 2016)

We have lots of fresh material to read on the beach this week — most notably, the “read of the week” below, which might be better labeled as the “read of the year.”  We have another streaming engine to kick around, a slew of earnings releases in the coming week, and some new releases from GraphLab Dato Turi.

If you haven’t already completed Databricks’ Spark survey, stop reading this and go do the survey.

On Wednesday, July 20, Teradata presents results of an “independent” benchmark of SQL on Hadoop engines, including Hive, Impala, Presto, and SparkSQL. Missing from the mix: Teradata Aster.

Call for Papers

CFP is open for Apache: Big Data Europe in Seville. Conference is November 14-16; CFP closes September 9

Read of the Week

Stop building data cathedrals; instead, build data bazaars. Adrian Colyer explains.

Yet Another Streaming Engine

The folks at Concord.io benchmark their product against Spark 1.6; not surprisingly, the results favor Concord.io. In Datanami, Alex Woodie touts the results. He should read his own summary of the recent OpsClarity survey, which contained this nugget:

Screen Shot 2016-07-18 at 8.26.11 AM

In other words, the whole debate about “true streaming” versus micro-batching is irrelevant to most organizations because they don’t need subsecond performance. It’s like arguing that a Ferrari is better than a Toyota Camry because the sports car can go 180 mph. Here in Mudville, you’ll be arrested if you go that fast, so the Camry’s big trunk and rear seat leg room look pretty good.

Performance is cool. But the current spate of streaming engines will not be resolved by performance tests. Commercial support, integration, depth of features, security and stability will determine which engines survive the shakeout.

Second Quarter Earnings Roundup

Five of the top six Business Analytics software vendors tracked by IDC are public companies, with quarterly earnings reports. (SAS is privately held). Here is the outlook for earnings releases:

— Oracle’s fiscal year ends May 31. Oracle does not report analytics revenue separately. For the fiscal quarter ended May 31, 2016, Oracle reports that growth in revenue from SaaS and PaaS cloud services barely offset a 12% decline in software license revenue, for overall flat software and services revenue.

— SAP expects to release Q2 financial results on Wednesday, July 20.

— Declining giant IBM will announce another quarter of fail on Monday, July 18.

— Microsoft will announce quarterly and fiscal year-end results on Tuesday, July 19.

— Teradata, like SAP, IBM, and Microsoft, closed the second quarter on June 30, but can’t crunch the numbers until Tuesday, August 2. Keep that in mind the next time TDC tries to sell you on their fast number crunching capabilities.

Explainers

— Ravelin’s Stephen Whitworth explains how to real-time fraud detection with Google BigQuery.

— Carol McDonald explains how to use Spark’s Random Forests capability, demonstrating with a loan credit risk dataset.

— Three more papers from Adrian Colyer:

  • Ambry: LinkedIn’s scalable geo-distributed object store.
  • Spheres of influence for viral marketing.
  • Progressive skyline computation.

— On the Hortonworks blog, Roshan Naik and Sapin Amin explain how they benchmarked performance improvements in Apache Storm 1.0.

— Jules Damji explains Spark APIs: RDDs, DataFrames, and Datasets.

— Lewis Gavin offers five tips to improve the performance of Spark apps.

— Qubole’s Rajat Venkatesh explains how to optimize queries with materialized views and Quark, Qubole’s SQL abstraction layer.

— In a recorded webinar, Hossein Falaki and Denny Lee explain how to perform exploratory analysis on large datasets with Spark and R.

— On the Revolutions blog, Joe Rickert explains the capabilities of several new R packages in CRAN.

— Barath Ravichander explains how to use R with SQL.

— Microsoft’s Sheri Gilley explains the ins and outs of SQL Server, PowerBI, and R.

— Roel M. Hogervorst explains how to submit an R package to CRAN. Bob Rudis elaborates.

— The Rcpp package enables R packages to leverage C or C++ code.  Dirk Eddelbuettel reveals that more than 700 CRAN packages now use Rcpp.

Perspectives

— On KDnuggets, deep learning mavens offer predictions about deep learning.

— Daniel Gutierrez interviews MapR’s Jack Norris, who is very excited about MapR.

— Alex Woodie describes Prama, TransUnion’s open source analytics platform built on MapR and Apache Drill.

Open Source Announcements

— Basho donates Riak TS for time series analysis to open source.

— Microsoft announces Microsoft R Client, a free development tool for use with Microsoft R Open.

— Apache Atlas announces version 0.7.0 – incubating.

Commercial Announcements

— GridGain, the company behind Apache Ignite, reports a 300X sales increase in the first half of 2016, which is not too surprising since the company was in stealth mode until last January.

— Microsoft announces GA for Azure SQL Data Warehouse, which may surprise those who thought it was already GA.

GraphLab Dato Turi announces the release of GraphLab Create 2.0, Turi Distributed and Turi Predictive Services. Marketing staff works feverishly to change brand names on all documents.

Big Analytics Roundup (May 9, 2016)

The big news this week: Teradata’s CEO Mike Keough walks the plank. TDC stock rises 21% on dismal numbers, which demonstrates how much Wall Street values leadership.

CRN releases its fourth annual Big Data 100 in listicle form to maximize clicks. Criteria for inclusion are “editor’s picks”, so whatever. I got through the As before giving up.

Dave Ramel details five leading Apache Big Data projects: Spark, Tez, Bigtop, REEF and Storm. What? It’s a nice summary of each, but Ramel is a slave to Apache’s silly classifications.

Bullshit Benchmarks

Here are four rules for benchmarks.

  1. Use a standard test protocol, such as TPC-DS.
  2. When there is no available standard, test multiple use cases. Make a decent effort to try a variety of workloads.
  3. Communicate with sponsors for all benchmarked software, or communicate with none of them.
  4. Publish your code and your data. (There’s this thing called GitHub….)

The ironically named Mammoth Data (current headcount: 15) violates all four rules in a Google-commissioned “study,” which concludes that Cloud Dataflow runs one use case faster than Spark. Professional cat herder Andrew Oliver replaces his Mammoth CEO hat with his analyst hat and touts the results.

Go to the back of the class, Andrew. Run more use cases, discuss results with the Spark team as well as the Google team, then let us know what you learned. I don’t doubt that Dataflow is a nifty tool, and look forward to seeing a benchmark we can trust.

Explainers

— Adrian Colyer focuses on time series:

  • Gorilla: a fast, scalable  in-memory time series database.
  • BTrDB (Berkeley Tree Database), optimized storage for time series processing.
  • The Tarzan algorithm, a technique that discovers surprising patterns in a time series database. (Fixed link — h/t Oliver Vagner).

— On BrightTalk, Databricks’ Reynold Xin explains the new bits in Spark 2.0, to be released soon.

— On the DataRobot blog, Quantopian’s Thomas Wiecki explains how to predict out-of-sample performance for trading algorithms.

— Indeed.com’s Preetha Appan explains algorithms and architecture for recommendation engines.

— In a webcast, Sean Owen and Yann Delacourt explain real-time analytics with Spark.

— Microsoft’s Lixun Zhang explains the differences among open source R, Microsoft R Open and Microsoft R Server.

Perspectives

— In Datanami, George Leopold profiles DataRobot, a machine learning startup. One point he gets wrong, DataRobot runs on Hadoop in the cloud and it runs on Hadoop on premises.

— On the Google Cloud blog, Tyler Akidau offers Google’s perspective on why they moved Cloud Dataflow development to Apache Beam. DataArtisans chirps support. Here’s what OpenHub has to say about Apache Beam:

Screen Shot 2016-05-09 at 11.01.28 AM

— In WSJ’s CIO Journal, Steven Norton interviews Airbnb’s Mike Curtis, who name-drops Apache Spark. In the same venue, Clint Boulton previously reported that Airbnb uses Spark in its Aerosolve project.

— Jim O’Reilly offers a summary of the differences among AWS, Azure and Google Cloud.

— On the Qubole blog, Monique Chmiel tries to summarize the pros and cons of Python, R and Scala for Big Data, and largely fails. None of the three is suitable for Big Data on its own, so you have to evaluate them for their APIs to scalable platforms like Spark. As of today, the Spark APIs for Scala and Python are clearly superior to the R API.

Commercial Announcements

News from commercial software providers, as well as commercial vendors that operate on an open source software model.

— Hortonworks announces that it lost $1.59 for every dollar it sold in Q1, which is slightly better than the $1.85 it lost in Q1 of 2015. At that rate, look for HDP to break even in 2018 or so, unless they run out of cash first. Wall Street drives stock down 18%.

— Teradata fires CEO, Wall Street celebrates. Don’t party too hard, guys; the numbers still stink.

Stuff I Really Don’t Care About

— Basho releases Riak TS to open source.

Teradata Reports Loss, Fires CEO

After three years of strategic floundering, Teradata now understands that it has a leadership problem, and announces a CEO change. Victor Lund, who heads the audit committee on the board, takes the helm.

Speaking to investors, Lund noted in his opening remarks that he is too old to be the permanent CEO, denied that he is merely a caretaker, and said he plans to find a new CEO in 90 days.

Asked about strategic missteps, Lund pointed to the Aprimo purchase; a safe comment given previous announcements. Beyond that he said that Teradata must change its culture and move faster.

In other words, he has no idea what to do. But he’s gung-ho.

Teradata also reports a net loss of $46 million, and a 20% decline in product revenue. Product revenue drives consulting and maintenance revenue, and a decline that steep implies a failing business model. Consulting revenue was up 4%: maintenance revenue up 2%. Selling, general and administrative expenses, down 5%; research and development down by 10%.

Screen Shot 2016-05-05 at 7.58.03 AM

CFO Steve Scheppmann announced a definitive agreement to sell the Marketing software business for $90 million, “below what we expected.” Teradata paid $525 million for Aprimo in 2011.

Steve, you’re supposed to buy low and sell high.

Demonstrating his keen insight into the data warehousing business, Scheppmann noted that buyers “are moving away from capex.” He noted that Q1 sales in the Americas were down because Teradata shuffled its sellers. (Asked about this in the previous investor call, Mike Keough denied that shuffling the sellers would impair sales.) Scheppmann also noted that some deals slipped to Q2, and expects some Q2 deals to slip into later quarters.

Lots of slippage going on.

Oliver Ratzesberger, president of Teradata Labs, painted a picture of Teradata everywhere: on-premises, in private cloud, and in public cloud. The fly in the ointment is that Teradata in AWS Marketplace is a single node version; Teradata without an MPP architecture is like a muscle car with a tiny engine. He noted that Teradata is accelerating plans to put an MPP version into the cloud, and now expects to do so by the end of this year, only five years after Oracle.

Ratzesberger also mentioned rebranding the Teradata architecture as IntelliFlex, and consulting-led solutions. He did not mention Aster. In fact, nobody mentioned Aster. Presumably, that old dog won’t hunt much longer.

Asked a slightly technical question, Ratzesberger rambled incoherently.

Lund, Scheppmann and Ratzesberger all spoke of the central role of consulting in leading Teradata out of the woods. If Teradata is serious about that, they’re going to have to go full open source, like Pivotal did last year. You can’t easily mix a strategic consulting business with a software business. Just ask IBM.

Big Analytics Roundup (April 18, 2016)

In hard news this week, Storm hits a milestone with Release 1.0, Google releases TensorFlow 0.8 with distributed computing support, and DataStax announces DataStax Enterprise Graph. And, following on NVIDIA’s DGX-1 announcement last week there are a number of items on Deep Learning featured below.

Deep Learning

— Adrian Colyer summarizes a paper that summarizes 900 other papers on Deep Learning.

— Data Science Central compiles a slew of links on Deep Learning.

— Nicole Hemsoth interviews NVIDIA Veep Marc Hamilton, who ruminates on the convergence of supercomputing and Deep Learning.

Explainers

— On the Pivotal Big Data blog, Alexey Grischchenko explains what’s up with Apache Hawq, the SQL-on-Hadoop-and-Greenplum engine that is now an Apache Incubator project. According to OpenHub, there’s a lot of activity on Hawq, and contributions are up sharply since it went Apache.

— In KDnuggets, Microsoft’s Brandon Rohrer publishes a handy pocket guide to data science.

— Nicholas A. Perez explains custom streaming sources in Spark.

— Ian Pointer explains Apache Beam, and how it aspires to be the uber-API.

— Abie Reifer explains Microsoft Azure HDInsight.

— Yong Feng of IBM’s Spark Technology Center explains results of a test run with Spark on Mesos.

— Gopal Wunnava explains geospatial intelligence with SparkR on Amazon EMR.

— IBM’s Fred Reiss explains SystemML, for those who missed his presentation at Spark Summit East.

— For masochistic sabremetricians, Nick Amato explains baseball statistics with Hive and Pig.

Perspectives

— Serdar Yegulalp reviews Apache Storm 1.0. He likes it.

— DataArtisans’ Kostas Tzoumas explains counting in streams, then touts Flink.

— Timothy Prickett Morgan reports on HPE’s efforts to put Spark on a Superdome. Results are interesting. But as with IBM running Spark on a mainframe, such efforts overlook a key benefit of Hadoop and Spark: the ability to avoid dealing with the likes of HPE and IBM.

— Katharine Kearnan interviews Nick Pentreath, one of the two Spark Committers IBM has hired. He predicts that in Spark 2.0, the ML pipeline API approaches parity with the MLlib API. Interestingly, he doesn’t expect a lot from SparkR.

— In Forbes, Chris Wilder recaps his visit to Google Cloud Platform NEXT 2016.

— Andrew Brust summarizes Hortonworks’ recent announcements, sees an emerging duopoly of Cloudera and Hortonworks. I’m not inclined to dismiss MapR and AWS so easily.

— Craig Stedman comments on Pivotal’s exit from the Hadoop distribution market, quotes some old guy wondering how much longer IBM will keep BigInsights alive. My take on Pivotal: honestly, I thought they exited a year ago.

— Cloud platform Altiscale’s Raymie Stata surveys Hadoop’s history, sees movement to the cloud.

— James Nunns wonders if the top Hadoop distributors can steal the show from Spark at Hadoop Summit 2016. If you count the number of times the word “Spark” appears in Hortonworks’ announcement, the answer is no.

— Ajay Khanna opines that absent data quality and metadata management, your data lake will turn into a data swamp.

— Nick Bishop interviews MSFT’s research chief, who assures him that AI is too stupid to wipe us out. I worry more about the chemtrails.

Open Source Announcements

— Apache Storm announces Release 1.0.0, with many enhancements. According to OpenHub, Storm is picking up steam, with 127 active contributors in the past 12 months.

— Google announces TensorFlow 0.8, with distributed computing support and new libraries for user-defined distributed models.

— Apache Mahout announces release of Mahout 0.12.0, with Flink bindings to the Samsara engine. Contributors from DataArtisans did most of the work, as most other contributors have long since exited this project.

Commercial Announcements

— DataStax announces DataStax Enterprise Graph (DSE Graph), built on Apache Cassandra and Apache Tinkerpop (a graph computing framework.) A year ago, Datastax acquired Aurelius, the commercial venture behind Titan, an open source distributed graph database; Titan uses Cassandra as a back end. DSE Graph includes extensions found in DataStax Enterprise, including security, search, analytics and monitoring tools. Alex Handy reports.

— Databricks announces new content for its Community Edition:

— Hortonworks previews HDP 2.4.2. Key bits:

  • Spark 1.6.1.
  • Spark SQL certified with ODBC.
  • Bug fixes for Spark/Oozie connection for Kerberos-enabled clusters.
  • Spark Streaming with Apache Kafka in a Kerberos-enabled cluster.
  • Spark SQL with ORC performance improvements.
  • Final technical preview of Apache Zeppelin with Kerberos, LDAP and identity propagation.

— Hortonworks also announces that Pivotal HDP is officially dead. Pivotal announces nothing.

— Teradata announces that its Think Big subsidiary is expanding its data lake and managed service offerings using Apache Spark. This is good news for the eight consultants at Think Big with Spark credentials, as it means less time spent on the bench. Meanwhile, Think Big contributes a distributed K-Modes in PySpark to open source, the first such contribution since 2014. For some reason, they did not contribute it to Spark packages.

— Atigeo, a “compassionate technology company”, announces that is has added Spark 1.6 to its xPatterns platform.

— Lucidworks announces release of Lucidworks View, a component that simplifies development of applications on Solr and Spark.

— DataRPM, “Cognitive Data Science” company with very little money announces partnership with Tamr, a data integration company with lots of money.

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 (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.