The Year in SQL Engines

As an addendum to my year-end review of machine learning and deep learning, I offer this survey of SQL engines. SQL is the most widely used language for data science according to O’Reilly’s 2016 Data Science Salary Survey. Most projects require at least some SQL operations, and many need nothing but SQL.

This review covers six open source leaders: Hive, Impala, Spark SQL, Drill, HAWQ, and Presto; plus, for completeness, Calcite, Kylin, Phoenix, Tajo, and Trafodion. Omitted: two commercial options, Oracle Big Data SQL and IBM Big SQL, which IBM has not yet rebranded as “Watson SQL.”

(A reader asks: What about Druid? My response: erm. On inspection, I agree that Druid belongs in this category, so check it out.)

I use the term ‘SQL Engine’ loosely. Hive, for example, is not an engine; it’s a framework that uses the MapReduce, Tez, or Spark engines to run queries. And it doesn’t run SQL; it runs HiveQL, an SQL-like language that closely approximates SQL. ‘SQL-in-Hadoop’ is also inapt; while Hive and Impala work primarily with Hadoop, Spark, Drill, HAWQ, and Presto also work with a wide variety of other data storage systems.

Unlike relational databases, SQL engines operate independently of the data storage system. In contrast, relational databases bundle the query engine and storage into a single tightly coupled system, which permits certain types of optimization. Uncoupling them, on the other hand, provides greater flexibility, though at the potential loss of performance.

Figure 1, below, shows the relative popularity of the leading SQL engines according to DB-Engines, a website maintained by the Austrian consultancy Solid IT. DB-engines computes a monthly popularity score for more than 200 database systems. The score reflects search engine queries; mentions in online discussions; job offers; mentions in professional profiles, and tweets.

Figure 1

screen-shot-2017-01-31-at-1-04-43-pm
Source: DB-Engines, January 2017 http://db-engines.com/en/ranking

Although Impala, Spark SQL, Drill, Hawq, and Presto consistently beat Hive on measures such as runtime performance, concurrency, and throughput, Hive remains the most popular (at least by the DB-Engines metric). There are three reasons why that is so:

— Hive is the default option for SQL in Hadoop, supported in every distribution. The others align with specific vendors and cater to niche users.

— Hive has closed the performance gap to the other engines. Most of the Hive alternatives launched in 2012 when analysts would rather kill themselves than wait for a Hive query to finish. But while Impala, Spark, Drill, et.al. ran away like rabbits back then, Hive just kept chugging along, tortoise-like, with incremental improvements. Today, while Hive is not the fastest choice, it’s a lot better than it was five years ago.

— While bleeding-edge speed is cool, most organizations know that the world does not end if a junior marketing manager has to wait ten seconds to find out if the chicken wings outperformed the buffalo burgers in the Duxbury restaurant last Tuesday.

As you can see in Figure 2, below, the top SQL engines compete well for user interest compared to leading commercial data warehouse appliances.

Figure 2

screen-shot-2017-01-31-at-2-27-15-pm
Source: DB-Engines, January 2017 http://db-engines.com/en/ranking

The best measure of health for an open source project is the size of its active developer community. Hive and Presto have the largest base of contributors, as shown in Figure 3, below. (Data for Spark SQL is unavailable.)

Figure 3

screen-shot-2017-01-31-at-2-52-27-pm
Source: Open Hub https://www.openhub.net/

In 2016, ClouderaHortonworks, Kognitio, and Teradata waded into the Battle of the Benchmarks Tony Baer summarizes. I’m sure that you will be shocked to learn that the vendor’s preferred SQL engine outperformed the others in each of these studies, which begs the question: are benchmarks bullshit?

AtScale‘s biannual benchmark is not BS. AtScale, a BI startup, markets software that brokers between BI front ends and SQL backends. The company’s software is engine-neutral — it seeks to run on as many as possible — and its broad experience in BI gives the testing a real-world flavor.

AtScale’s key findings from its most recent round, which included Hive, Impala, Spark SQL, and Presto:

— All four engines successfully ran AtScale’s BI benchmark queries.

— Each engine has its own performance “sweet spot” depending on data volume, query complexity, and concurrent users.

– Impala and Spark SQL outperform the others in queries against small data sets

– On large data sets, Impala and Spark SQL handle complex joins better than the others

– Impala and Presto demonstrate the best results in concurrency tests

— All engines showed 2X-4X performance gains in the six months since AtScale’s previous benchmark.

Alex Woodie reports on the test results; Andrew Oliver analyzes.

Let’s dive into the individual projects.

Apache Hive

Apache Hive was the first SQL framework in the Hadoop ecosystem. Engineers at Facebook introduced Hive in 2007 and donated the code to the Apache Software Foundation in 2008; in September 2010, Hive graduated to top-level Apache project status. Every major player in the Hadoop ecosystem distributes and supports Hive, including Cloudera, MapR, Hortonworks, and IBM. Amazon Web Services offers a modified version of Hive as a cloud service in Elastic MapReduce (EMR).

Early releases of Hive used MapReduce to run queries. Complex queries required multiple passes through the data, which impaired performance. As a result, Hive was not suitable for interactive analysis. Led by Hortonworks, the Stinger initiative markedly enhanced Hive’s performance, notably through the use of Apache Tez, an application framework that delivers streamlined MapReduce code. Tez and ORCfile, a new storage format, produced a significant speedup for Hive queries.

Cloudera Labs spearheaded a parallel project to re-engineer Hive’s back end to run on Apache Spark. After an extended beta, Cloudera released Hive-on-Spark to general availability in early 2016.

More than 100 individuals contributed to Hive in 2016. The team announced Hive 2.0 in February and Hive 2.1 in June. Hive 2.0 includes improvements to several improvements to Hive-on-Spark, plus performance, usability, supportability and stability enhancements. Hive 2.1 includes Hive LLAP (“Live Long and Process”), which combines persistent query servers and optimized in-memory caching for high performance. The team claims a 25X speedup.

In September, the Hivemall project entered the Apache Incubator, as I noted in Part Two of my machine learning year-end roundup. Originally developed by Treasure Data and donated to the Apache Software Foundation, Hivemall is a scalable machine learning library implemented as a collection of Hive UDFs designed to run in Hive, Pig or Spark SQL with MapReduce, Tez or Spark. The team plans an initial release in Q1 2017.

Apache Impala

Cloudera launched Impala, an open source MPP SQL engine, in 2012, as a high-performance alternative to Hive. Impala works with HDFS and HBase, and it leverages Hive metadata; however, it bypasses MapReduce to run queries. Mike Olson, Cloudera’s Chief Strategy Officer,

Mike Olson, Cloudera’s Chief Strategy Officer, argued in late 2013 that Hive’s architecture was fundamentally flawed. In Olson’s view, developers could only deliver high-performance SQL with a whole new approach, exemplified by Impala. In 2014 Cloudera released a series of benchmarks in January, May, and September. In these tests, Impala showed progressive improvement in query runtime, and significantly outperformed Hive on Tez, Spark SQL, and Presto. In addition to running fast, Impala performed particularly well in concurrency, throughput, and scalability.

In 2015, Cloudera donated Impala to the Apache Software Foundation, where it entered the Apache Incubator program. Cloudera, MapR, Oracle and Amazon Web Services distribute Impala;  Cloudera, MapR, and Oracle provide commercial build and installation support.

Impala made steady progress in the Apache Incubator in 2016. The team cleaned up the code, ported it to Apache infrastructure and delivered Release 2.7.0, its first Apache release in October. The new version includes performance and scalability improvements, as well as some other minor enhancements.

In September, Cloudera published results of a study that compared Impala to Amazon Web Services’ Redshift columnar database. The report is interesting reading, though subject to the usual caveats about vendor benchmarks.

Spark SQL

Spark SQL is a Spark component for structured data processing. The Apache Spark team launched Spark SQL in 2014 and absorbed Shark, an early Hive-on-Spark project. It quickly became the most widely used Spark module.

Spark SQL users can run SQL queries, read data from Hive, or use it as means to create Spark Datasets and DataFrames. (Datasets are distributed collections of data; DataFrames are Datasets organized into named columns.) The Spark SQL interface provides Spark with information about the structure of the data and operations to be performed; Spark’s Catalyst optimizer uses this information to construct an efficient query.

In 2015, Spark’s machine learning developers introduced the ML API, a package that leveraged Spark DataFrames instead of the lower-level Spark RDD API. This approach proved to be attractive and fruitful; in 2016, with Release 2.0, the Spark team placed the RDD-based API in maintenance mode. The DataFrames API is now the primary interface for Spark machine learning.

Also in 2016, the team released Structured Streaming, in an Alpha release as of Spark 2.1.0. Structured Streaming is a stream processing engine built on Spark SQL. Users can query streaming data sources in the same manner as static sources, and they can combine streaming and static sources in a single query. Spark SQL runs the query continuously and updates results as streaming data arrives. Structured Streaming delivers exactly-once fault-tolerance guarantees through checkpointing and Write Ahead Logs.

Apache Drill

In 2012, a group led by MapR, one of the leading Hadoop distributors, proposed to build an open-source version of Google’s Dremel, a distributed system for interactive ad-hoc analysis. They named the project Apache Drill. Drill languished in the Apache Incubator for more than two years, finally graduating in late 2014. The team delivered its 1.0 release in 2015.

MapR distributes and supports Apache Drill.

More than 50 individuals contributed to Drill in 2016. The team delivered five dot releases in 2016. Key enhancements include:

  • Web authentication
  • Support for the Apache Kudu columnar database
  • Support for HBase 1.x
  • Dynamic UDF support

Two key Drill contributors left MapR to start Dremio in 2015; the startup remains in stealth mode.

Apache HAWQ

Pivotal Software introduced HAWQ as a commercially licensed high-performance SQL engine in 2012 and attempted to market it with minimal success. Changing strategy, Pivotal donated the project to Apache in June 2015, and it entered the Apache Incubator program in September 2015.

Fifteen months later, HAWQ remains in the Incubator. The team released HAWQ 2.0.0.0 in December, with a load of bug fixes. I suspect the project will graduate in 2017.

One small point in HAWQ’s favor is its support for Apache MADlib, the machine-learning-in-SQL project that is also still in the Incubator. The combination of HAWQ and MADlib should be a nice consolation to the folks who bought Greenplum and wonder what the hell happened.

Presto

Facebook engineers initiated the Presto project in 2012 as a fast interactive alternative to Hive. Rolled out in 2013, the software successfully supported more than a thousand Facebook users and more than 30,000 queries per day on petabytes of data. Facebook released Presto to open source in 2013.

Presto supports ANSI SQL queries across a range of data sources, including Hive, Cassandra, relational databases or proprietary file systems (such as Amazon Web Services’ S3.)  Presto queries can federate data from multiple sources.  Users can submit queries from C, Java, Node.js, PHP, Python, R and Ruby.

Airpal, a web-based query tool developed by Airbnb, offers users the ability to submit queries to Presto through a browser. Qubole provides a managed service for Presto. AWS delivers a Presto service on EMR.

In June 2015, Teradata announced plans to develop and support the project.  Under an announced three-phase program, Teradata proposed to integrate Presto into the Hadoop ecosystem, enable operation under YARN and enhance connectivity through ODBC and JDBC. Teradata offers its own distribution of Presto, complete with a data sheet. In June, Teradata announced the certification of Information Builders, Looker, Qlik, Tableau, and ZoomData, with MicroStrategy and Microsoft Power BI on the way.

Presto is a very active project, with a vast and vibrant contributor community. The team cranks out releases faster than Miki Sudo eats hot dogs — I count 42 releases in 2016. Teradata hasn’t bothered to summarize what’s new, and I don’t plan to sift through 42 sets of release notes, so let’s just say it’s better.

Other Apache Projects

There are five other SQL-ish projects in the Apache ecosystem.

Apache Calcite

Apache Calcite is an open source framework for building databases. It includes:

— A SQL parser, validator and JDBC driver

— Query optimization tools, including a relational algebra API, rule-based planner, and a cost-based query optimizer.

Apache Hive uses Calcite for cost-based query optimization, while Apache Drill and Apache Kylin use the SQL parser.

The Calcite team pushed out five releases in 2016, with bug fixes and new adapters for Cassandra, Druid, and Elasticsearch.

Apache Kylin

Apache Kylin is an OLAP engine with a SQL interface. Developed by eBay and donated to Apache, Kylin graduated to top-level status in 2015.

A startup named Kyligence launched in 2016; it offers commercial support and a data warehousing product called KAP, FWIW. While the company has no funding listed in Crunchbase, a source tells me that it has strong backing and a large office in Shanghai.

Apache Phoenix

Apache Phoenix is a SQL framework that runs on HBase and bypasses MapReduce. Salesforce developed the software and donated it to Apache in 2013. The project graduated to top-level status in May 2014. Hortonworks includes Phoenix in the Hortonworks Data Platform. Since the leading SQL engines all work with HBase, it’s not clear why we need Phoenix.

Apache Tajo

Apache Tajo is a fast SQL data warehousing framework introduced in 2011 by Gruter, a Big Data infrastructure company, and donated to Apache in 2013. Tajo graduated to top level status in 2014. The project has attracted little interest from prospective users and contributors outside of Gruter’s primary market in South Korea. Other than a brief mention by Gartner’s Nick Heudecker, the project isn’t on anyone’s dashboard.

Apache Trafodion

Apache Trafodion is another SQL-on-HBase project, conceived by HP Labs, which tells you pretty much all you need to know. HP launched Trafodion in June 2014, a month after Apache Phoenix graduated to production. Six months later, it dawned on HP executives that there might be limited commercial potential for another SQL-on-HBase engine — I can see the facepalms — so they donated the project to Apache, where it entered the Incubator in May 2015.

Trafodion promises to be a transactional database if it ever gets out of incubation. Unfortunately, there are lots of options in that space, and the only competitive benefit the development team can articulate seems to be “it’s open source, so it’s cheap.”

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 (July 5, 2016)

Quite a few open source announcements this week. One of the most interesting is Apache Bahir, which includes a number of bits spun out from Apache Spark. It’s another indicator of the size and strength of Spark, in case anyone needs a reminder.

In other news, Altiscale and H2O.ai concurrently develop time travel: both vendors claim to support Spark 2.0, which isn’t generally available yet. The currently available Spark 2.0 preview release is not a stable release and the Spark team does not guarantee API stability. So at minimum anyone claiming to support Spark 2.0 will have to retest with the GA release.

Andrew Brust summarizes news from Hadoop Summit.

Microsoft’s Bill Jacobs explains Apache Spark integration through Microsoft R Server.  (Short version: Microsoft R previously pushed processing down to MapReduce, and now pushes down to Spark.) In a test, Microsoft found that shifting from MapReduce to Spark produced a 6X speedup, which is similar to what IBM achieved when it did the same thing with SPSS Analytics Server. Bill’s claim of 125X speedup is suspicious — he compares the performance of Microsoft R’s ScaleR distributed GLM algorithm running in a five-node Spark cluster with running GLM with an unspecified CRAN package on a single machine.

Owen O’Malley benchmarks file formats, concludes nothing. But it was fun!  Pro tip: if you’re going to spend time running benchmarks, use a standard TPC protocol.

Denny Lee introduces Databricks’ new Guide to getting started with Spark on Databricks.

Top Read/Watch

On YouTube and SlideShare: Slim Baltagi, Director of Enterprise Architecture at Capital One, presents his analysis of major trends in big analytics at Hadoop Summit.

Explainers

— In the second of a three-part series, Databricks’ Bill Chambers explains how to build data science applications on Databricks. Part one is here.

— William Lyon explains graph analysis with Neo4j and Game of Thrones, concludes that Lancel Lannister isn’t very important to the narrative.

graph-of-thrones

— On the AWS Big Data Blog, Sai Sriparasa explains how to transfer data from EMR to RDS with Sqoop.

— In part one of a series, LinkedIn’s Kartik Paramasivam disses Lambda, explains how to solve hard problems in stream processing with Apache Samza.

— Hortonworks’ Vinay Shukla and others explain the roadmap for Apache Zeppelin.

— Rajat Jaiswal explains Azure Machine Learning in the first of a multi-part series. It’s on DZone, which means the content was ripped from some other source, but I can’t find the original.

— A blogger named junkcharts explains the importance of simplicity in visualization.

Perspectives

— Roger Schank, who wrote the book on cognitive computing, parses IBM’s claims for Watson. He isn’t impressed.

— Werther Krause offers some pretty good recommendations for building a data science team.

Open Source Announcements

— The Apache Software Foundation announces Apache Bahir as a top-level project. Bahir aims to curate extensions for distributed analytic platforms. Initial bits include toolkits for streaming akka, streaming mqtt, streaming twitter and streamingmq. The team includes 16 committers from Databricks, 4 from UC Berkeley, 3 from Cloudera and 13 others. Sam dean reports.

— H2O.ai announces Sparkling Water 2.0. Sparkling Water is an H2O API for Spark, and a registered Spark package. Stories here, here, here, and here. Among the claimed enhancements:

  • Support for Apache Spark 2.0 and “backward compatibility with all previous versions.”
  • The ability to run Apache Spark and Scala through H2O’s web-based Flow UI.
  • Support for the Apache Zeppelin notebook.
  • H2O feature improvements and visualizations for MLlib algorithms, including the ability to score feature importance.
  • The ability to build Ensembles using H2O plus MLlib algorithms.
  • The power to export MLlib models as POJOs (Plain Old Java Objects).

— Alluxio (née Tachyon) announces Release 1.1. (Alluxio is an open source project for in-memory virtual distributed storage). Key bits include performance improvements, including master metadata scalability, worker scalability and better support for random I/O; improved access control features; usability improvements; and integration with Google Compute Engine.

— Apache Drill announces Release 1.7.0, with bug fixes and minor improvements.

— Qubole announces Quark, an open source project that optimizes SQL across storage platforms.

— MongoDB releases its own connector for Spark, supplementing the existing package developed by Stratio.

Commercial Announcements

— Altiscale claims support for Spark 2.0.

— AtScale announces a reseller agreement with Hortonworks.

— GridGain Systems announces Professional Edition 1.6, the commercially licensed enhanced version of Apache Ignite. Release 1.6 includes native support for Apache Cassandra.

— Hortonworks announces Microsoft Azure HDInsight as its premier cloud solution. They should have noted that Azure is Hortonworks only cloud solution.

— Zoomdata announces certification on the MapR Converged Data Platform.

Big Analytics Roundup (June 13, 2016)

Spark Summit 2016 met last week in SFO. There were many cool things; I will publish a separate report when presentations and videos are available.

KDnuggets releases results of its annual poll on data science software. Key findings:

  • Python use is up 51%, almost catches up to R, the #1 choice.
  • Excel and Tableau usage are up 47% and 49%, respectively.
  • Spark usage is up 91%, overtakes Hadoop.
  • SAS is down big time, drops from the top ten.

Meanwhile, Alex Woodie wraps statistics on Spark adoption, and Qubole’s Ari Amster reports on Spark usage among Qubole users.

Tim Spann recaps the week in Hadoop.

Spark Summit: Roundup of Roundups

— On the Databricks blog, Wayne Chan, Dave Wang, Jules Damji and Denny Lee recap highlights from the Summit.

— Jessica Davis rounds up the highlights.

— Jack Vaughan surrounds the story, quotes some old guy.

— Sam Dean summarizes what you need to know.

— Alex Handy collects the key bits.

— Andrew Brust separately corrals Day One and Day Two.

CFPs and Competitions

Flink Forward 2016, Berlin, September 12-14 (due June 30)

Parkinson’s Progression Markers Institute (PPMI) 2016 Challenge (due September 7)

Spark Summit Europe, Brussels, October 25-27 (closing date TBA)

Top Read

Adrian Colyer summarizes a paper on identifying architectural debt in software.

Explainers

— Deenar Torasker explains the new capabilities of HDFS.

— Ron Bodkin explains key considerations when designing continuous apps, in the second of a three part series. Part one is here.

— On his eponymous blog, Jesse Steinweg-Woods explains Gradient Boosted Trees with XGBoost in Python.

— Adam Warski explains how Kafka Streams fits into the stream processing landscape.

Perspectives

— H2O.ai’s Vinod Iyengar objects to what he calls the fragmentation of Spark support, correctly noting that Cloudera and Hortonworks support different versions of Spark in their distributions. Of course, nobody is obligated to use Spark with Cloudera and Hortonworks.

— From the Spark Summit on YouTube: Ben Lorica leads a panel discussion of incredibly smart and distinguished people, plus some old guy.

— Altiscale’s Barbara Lewis presents ten use cases for Big Data.

— Tim Wallis believes that AI will relieve boredom.

— Sam Dean touts Grappa, Drill and Kafka as successors to Spark. Grappa is going nowhere. Drill is great if all you want to do is SQL, and Kafka is great if all you want to do is streaming. Pro tip: there are no real-world analytic applications where all you want to do is streaming.

— Allen Downey opines that statistical tests are inflexible and opaque. Funny, my college roommate said the same thing when he flunked his Stat 101 mid-term.

Open Source Announcements

— LinkedIn announces release of PhotonML, a machine learning library for Spark. Feature detail here.

— Google releases TensorFlow 0.9.0, with iOS support. Speculation about deep learning on your phone ensues.

— Twitter donates DistributedLog to Apache.

Commercial Announcements

— Databricks announces general availability for the Databricks Community Edition, and completion of the first phase of Databricks Enterprise Security framework.

— Microsoft announces general availability for its managed Spark service in HDInsight, and summer availability for the Spark pushdown capability in R Server. The company also announced PowerBI support for Spark Streaming, which is confusing for those who thought PowerBI already supported Spark Streaming.

— IBM announces limited preview of a managed service branded as the Data Science Experience. IBM is coy about the details; the service definitely includes Spark, Jupyter and RStudio, H2O and “curated data sets”, and may include other bits. The service itself looks promising, but IBM’s claim to offer the “first development environment for Apache Spark” is BS.

— In an oddly opaque press release, H2O announces that it is “working with” IBM. H2O is open source software, and IBM requires no permission from H2O.ai for use or distribution; presumably, H2O will offer support contracts to users. H2O.ai did not respond to request for comment.

— Splice Machine announces plans to go open source; a company insider says they plan to donate the software to Apache. Dave Ramel reports.

Big Analytics Roundup (May 23, 2016)

Google announces that it has designed an application-specific integrated circuit (ASIC) expressly for deep neural nets. Tech press goes bananas. The chips, branded Tensor Processing Units (TPUs) require fewer transistors per operation, so Google can fit more operations per second into the chip. In about a year of operation, Google has achieved an order of magnitude improvement in performance per watt for machine learning.

Google’s Felipe Hoffa summarizes Mark Litwintschik’s work benchmarking different platforms with the New York City Taxi and Limo Commission’s public dataset of 1.1 billion trips. So far, Mark has tested PostgreSQL on AWS, ElasticSearch on AWS, Spark on AWS EMR, Redshift, Google BigQuery, Presto on AWS and Presto on Cloud Dataproc. Results make Google look good, but you should read Mark’s original posts.

Meanwhile, IBM fires more people. More here and here.

Open Data Science Conference

The second annual Open Data Science Conference (ODSC) East met in Boston over the weekend. Attendance doubled from last year, to 2,400.

Registration was a snafu, because the conference organizers did not accurately predict walk-in traffic or staffing needs. The jokes write themselves.

Content was excellent. Keynoters included Stefan Karpinski (Julia co-creator), Kirk Borne of Booz Allen Hamilton, Ingo Mierswa, CTO of RapidMiner and Lukas Biewald, CEO of Crowdflower. Track leaders included JJ Allaire and Joe Cheng of RStudio, Usama Fayyad of Barclays and John Thompson of the US Census Bureau. Sponsors included Basis Technology, CartoDB, CrowdFlower, Dataiku, DataRobot, Dato, Exaptive, Facebook, H2O.ai, MassMutual, McKinsey, Metis, Microsoft, RapidMiner, SFL Scientific and Wayfair.

Prompted by a tweet, I stopped at the Dataiku table. The conversation went like this:

  • Me: What does Dataiku do, in 25 words or less?
  • Dataiku: DataRobot.
  • Me: What?
  • Dataiku: We do what DataRobot does.

At this point, it was clear to me that Mr. Dataiku either did not know what DataRobot does, or thought I don’t know what DataRobot does. So I changed the subject.

The next ODSC event is in October, in London.

Explainers

— Michael Armbrust and Tathagata Das explain Structured Streaming in Spark 2.0

— Adrian Colyer goes 5 for 5 for the week:

— Tim Hunter, Hossein Falaki and Joseph Bradley explain HyperLogLog and Quantiles in Spark.

— Microsoft’s Raymond Laghaeian explains how to use Azure ML predictions in Google Spreadsheet.

Perspectives

— Serdar Yegulalp cites PayScale data in noting that if you know Scala, Go, Python and Spark you can expect to make more money.

— Tim Spann weighs the advantages of Java and Scala, and explains DL4J.

— Sam Dean celebrates Drill’s first anniversary.

— Taylor Goetz delivers a brief history of Apache Storm.

Open Source Announcements

— MongoDB releases a new Spark Connector.

— Apache Tajo announces Release 0.11.3, with five bug fixes.

— Apache Mahout announces Release 0.12.1, a maintenance release that resolves an issue with Flink integration.

Commercial Announcements

— RedPoint Global snags a $12 million “C” round.

— TIBCO announces something called Accelerator for Apache Spark, a bundle of tools that connect TIBCO products with open source packages. While TIBCO refers to this component as open source, the software is available only to TIBCO customers, which means it isn’t Free and Open Source.

— MapR applauds itself.

Big Analytics Roundup (April 11, 2016)

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

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

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

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

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

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

Screen Shot 2016-04-10 at 7.52.54 AM

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

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

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

NVIDIA Unveils Deep Learning Chip

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

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

— Selected media reports:

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

Explainers

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

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

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

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

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

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

Perspectives

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

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

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

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

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

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

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

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

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

Open Source Announcements

— Qubole releases SQL optimizer Quark to open source.

— Flink releases version 1.0.1, a maintenance release.

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

— Airbnb open sources Caravel, a data exploration package.

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

— LinkedIn releases Dr. Elephant to open source.

Commercial Announcements

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

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

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

Big Analytics Roundup (April 4, 2016)

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

Slides from Strata available here.

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

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

Explainers

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

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

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

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

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

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

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

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

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

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

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

Perspectives

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

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

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

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

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

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

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

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

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

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

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

Open Source Announcements

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

Commercial Announcements

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

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

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

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

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

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

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

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

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

Big Analytics Roundup (March 21, 2016)

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

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

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

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

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

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

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

Explainers

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

— This week from the morning paper:

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

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

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

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

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

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

Perspectives

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

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

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

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

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

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

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

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

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

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

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

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

Teradata Watch

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

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

Open Source Announcements

— Three announcements from Apache projects:

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

Commercial Announcements

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

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

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

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

Big Analytics Roundup (March 7, 2016)

Hortonworks wins the internet this week beating the drum for its partnership with Hewlett-Packard Enterprise.  The story is down under “Commercial Announcements,” just above the story about Hortonworks’ shareholder lawsuit.

Google releases a distributed version of TensorFlow, and HDP releases a new version of Dataflow.  We are reaching peak flow.

IBM demonstrates its core values.

Folks who fret about cloud security don’t understand that data is safer in the cloud than it is on premises.  There are simple steps you can take to reduce or eliminate concerns about data security.  Here’s a practical guide to anonymizing your data.

Explainers

In the morning paper, Adrian Colyer explains trajectory data mining,

On the AWS Big Data Blog, Manjeet Chayel explains how to analyze your data on DynamoDB with Spark.

Nicholas Perez explains how to log in Spark.

Altiscale’s Andrew Lee explains memory settings in part 4 of his series of Tips and Tricks for Running Spark on Hadoop.  Parts 1-3 are here, here and here.

Sayantam Dey explains topic modeling using Spark for TF-IDF vectorization.

Slim Baltagi updates all on state of Flink community.

Martin Junghanns explains scalable graph analytics with Neo4j and Flink.

On SlideShare, Vasia Kalavri explains batch and stream graph processing with Flink.

DataTorrent’s Thomas Weise explains exactly-once processing with DataTorrent Apache Apex.

Nishant Singh explains how to get started with Apache Drill.

On the Cloudera Engineering Blog, Xuefu Zhang explains what’s new in Hive 2.0.

On the Google Cloud Platform Blog, Matthieu Mayran explains how to build a recommender with the Google Compute Engine.

In TechRepublic, James Sanders explains Amazon Web Services in what he characterizes as a smart person’s guide.  If you’re not smart and still want to use AWS, go here.

Perspectives

We continue to digest analysis from Spark Summit East:

— Altiscale’s Barbara Lewis summarizes her nine favorite sessions.

— Jack Vaughan interviews attendees from CapitalOne, eBay, DataXu and some other guy who touts open source.

— Alex Woodie interviews attendees from Bloomberg and Comcast and grabs quotes from Tony Baer, Mike Gualtieri and Anjul Bhambhri, who all agree that Spark is a thing.

In other matters:

— In KDnuggets, Gregory Piatetsky attacks the idea of the “citizen data scientist” and give it a good thrashing.

— Paige Roberts probes the true meaning of “real time.”

— MapR’s Jim Scott compares Drill and Spark for SQL, offers his opinion on the strengths of each.

— Sri Ambati describes the road ahead for H2O.ai.

Open Source Announcements

— Google releases Distributed TensorFlow without an announcement.  On KDnuggets, Matthew Mayo applauds.

— Hortonworks announces a new release of Dataflow, which is Apache NiFi with the Hortonworks logo.  New bits include integrated security and support for Apache Kafka and Apache Storm.

— On the Databricks blog, Joseph Bradley et. al. introduce GraphFrames, a graph processing library that works with the DataFrames API.  GraphFrames is a Spark Package.

Commercial Announcements

— Hortonworks announces partnership with Hewlett Packard Enterprise to enhance Apache Spark.  HPE claims to have rewritten Spark shuffle for faster performance, and HDP will help them contribute the code back to Spark.  That’s nice.  Not exactly the ground-shaking announcement HDP touted at Spark Summit East, but nice.

— Meanwhile, Hortonworks investors sue the company, claiming it lied in a November 10-Q when it said it had enough cash on hand to fund twelve months of operations.  The basic issue is that Hortonworks burns cash faster than Kim Kardashian out for a spree on Rodeo Drive, spending more than $100 million in the first nine months of 2015, leaving $25 million in the bank.  Hortonworks claims analytic prowess; perhaps it should apply some of that know-how to financial controls.

— OLAP on Hadoop vendor AtScale announces 5X revenue growth in 2015, which isn’t too surprising since they were previously in stealth.  One would expect infinite revenue growth.

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.