Spark is the Future of Analytics

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Time’s up.

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

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

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

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

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

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

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

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

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

Here are some projects under way to improve Spark:

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

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

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

— A cost-based optimizer for Spark SQL.

— Improved interfaces to data sources.

— Continuing improvements to the Python and R APIs.

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

(6) More cool stuff is on the way.

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

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

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

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

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

Three Apache projects in the Incubator build on Spark:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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Magic Quadrant for Advanced Analytics Platforms, 2016

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

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

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

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

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

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

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

The Year in 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

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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

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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

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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 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 (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 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.

Big Analytics Roundup (March 9, 2015)

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

Hortonworks

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

Alpine Data Labs

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

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

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

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

Apache Spark

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

H2O

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

General

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

Machine Learning in Hadoop: Part One

Much has changed since I last blogged on this subject a year ago (here and here).  This is the first of a three-part blog covering the current state of play for machine learning in Hadoop.  I use the term “machine learning” deliberately, to refer to tools that can learn from data in an automated or semi-automated manner; this includes traditional statistical modeling plus supervised and unsupervised machine learning.  For convenience, I will not cover fast query tools, BI applications, graph engines or streaming analytics; all of those are important, and deserve separate treatment.

Every analytics vendor claims the ability to work with Hadoop.  In Part One, we cover five things to consider when evaluating how well a particular machine learning tool integrates with Hadoop: deployment topology, hardware requirements, workload integration, data integration, and the user interface.  Of course, these are not the only things an organization should consider when evaluating software; other features, such as support for specific analytic methods, required authentication protocols and other needs specific to the organization may be decisive.

Deployment Topology

Where does the machine learning software reside relative to the Hadoop TaskTracker and Data Nodes (“worker nodes”)?  Is it (a) distributed among the Hadoop worker nodes; (b) deployed on special purpose “analytic” nodes or (c) deployed outside the Hadoop cluster?

Distribution among the worker nodes offers the best performance; under any other topology, data movement will impair performance.  If end users tend to work with relatively small snippets of data sampled from the data store, “beside” architectures may be acceptable, but fully distributed deployment is essential for very large datasets.

Deployment on special purpose “analytic” nodes is a compromise architecture, usually motivated either by a desire to reduce software licensing fees or avoid hardware upgrades for worker node servers.  There is nothing wrong with saving money, but clients should not be surprised if performance suffers under anything other than a fully distributed architecture.

Hardware Requirements

If the machine learning software supports distributed deployment on the Hadoop worker nodes, can it run effectively on standard Hadoop node servers?  The definition of a “standard” node server is a moving target; Cloudera, for example, recognizes that the appropriate hardware spec depends on planned workload.  Machine learning, as a rule, benefits from a high memory spec, but some machine learning software tools are more efficient than others in the way they use memory.

Clients are sometimes reluctant to implement a fully distributed machine learning architecture in Hadoop because they do not want to replace or upgrade a large number of node servers.  This reluctance is natural, but the problem is attributable in part to a gap in planning and rapidly changing technology.  Trading off performance for cost reduction may be the right thing to do, but it should be a deliberate decision.

Workload Integration

If the machine learning software can be distributed among the worker nodes, how well does it co-exist with other MapReduce and non-MapReduce applications?  The gold standard is the ability to run under Apache YARN, which supports resource management across MapReduce and non-MapReduce applications.   Machine learning software that pushes commands down to MapReduce is also acceptable, since the generated MapReduce jobs run under existing Hadoop workload management.

Software that effectively takes over the Hadoop cluster and prevents other jobs from running is only acceptable if the cluster will be dedicated to the machine learning application.   This is not completely unreasonable if the Hadoop cluster replaces a conventional standalone analytic server and file system; the TCO for a Hadoop cluster is very favorable relative to a dedicated high-end analytic server.  Obviously, clients should know how they plan to use the cluster when considering this.

Data Integration

Ideally, machine learning software should be able to work with every data format supported in Hadoop; most machine learning tools are more limited in what they can read and write. The ability to work with uncompressed text in HDFS is table stakes; more sophisticated tools can work with sequence files as well, and support popular compression formats such as Snappy and Bzip/Gzip.  There is also growing interest in use of Apache Avro.   Users may also want to work with data in HBase, Hive or Impala.

There is wide variation in the data formats supported by machine learning software; clients are well advised to tailor assessments to the actual formats they plan to use.

User Interface

There are many aspects of the user interface that matter to clients when evaluating software, but here we consider just one aspect:  Does the machine learning software require the user to specify native MapReduce commands, or does it effectively translate user requests to run in Hadoop behind the scenes?

If the user must specify MapReduce, Hive or Pig it begs the question: why not just perform that task directly in MapReduce, Hive or Pig?

In Part Two, we will examine current open source alternatives for machine learning in Hadoop.