Tag: Oracle Advanced Analytics

Big Analytics Roundup (April 6, 2015)

Late posting today due to holiday travel.

In the week following Spark Summit East, a number of Spark skeptics surfaced, a sign that people take Spark seriously.

The top item of the week, though, is Tiernan Ray’s interview with Michael Stonebraker in Barrons, a must-read.

Analytic Software

Forrester published its latest “wave” for Big Data Predictive Analytics Solutions, an inaptly named report that lumps together solutions that can work with Big Data and those that cannot.  I’ll write a more detailed summary later this week.  Quick takes:  Alteryx, Oracle and RapidMiner did well, but Alpine and Microsoft clearly need to shift some of their analyst relations spending from Gartner to Forrester.

Apache Drill

Apache Drill announces Release 0.8.

Apache Spark

Analysis

In opensource.com, Jen Wike Hugar interviews key Spark contributor Reynold Xin.

Mike Vizard, in the aptly named Talkin’ Cloud, describes the high potential for Spark in the cloud.  (Though he does not mention it, more than half of respondents to a recent Typesafe survey of Spark users said they deploy it in the cloud.)

Matei Zaharia, creator of Spark and CTO of Databricks, held an Ask Me Anything last week on Reddit.  Key takeaways: no, Matei is not a musician, and yes, he likes Nutella. 

Spark has clearly reached a point of inflection when skeptical analysis emerges.  Criticism is healthy, of course, but what the skeptics all seem to share is an ignorance of machine learning and streaming applications, and the challenge of making those applications work well in MapReduce.  In other words, they all seem to misunderstand the purpose of Spark, and would do well to learn more about the platform before quibbling on the margins.

  • Professional cat herder Andrew Oliver compares Spark to Tableau and, shockingly, finds it wanting.  Also, Andrew heard people say unflattering things about Hadoop at Spark Summit East.  Who knew that Hadoop devotees are so sensitive?
  • In DataMill, Nicole Leskowski asks if Apache Spark is the next big thing in Big Data Analytics, a question that would have been timely last year.
  • In TechTarget, Jack Vaughan wonders whether Spark is just a shiny new object, while ruminating about Digital Equipment and the PDP-11.  His point will be lost on most readers.
  • Returning to ZDNet from GigaOm, Andrew Brust asks if Spark is overhyped, citing unnamed second-hand sources that tell him Spark is “not ready for prime time.”   Note to Andrew: you can download the software here.

Spark Core

Matei Zaharia celebrates Spark’s fifth birthday with a brief history.

On the Cloudera blog, Sandy Ryza concludes his series on tuning Spark jobs.

Spark Streaming

On the Databricks blog. Cody Koeninger, Davies Liu and Tathagata Das describe the new direct Kakfa API available in Spark 1.3

Databricks

Databricks announced that Timeful, a startup specializing in intelligent time management, has deployed its recommendation engine in Databricks Cloud.  Case study available here.

Hadoop Ecosystem

In Datanami, Hadoop skeptic Alex Woodie asks if Hadoop needs a reality check, observing that the leading Hadoop distributors do not make money, a trait shared by most industries at comparable points of maturity.  Woodie cites Wikibon’s Big Data revenue summary as evidence that there is little money in Hadoop, without considering the validity of Wikibon’s data (which is self-reported by the vendors and lacks consistent definitions).  Even if we accept the Wikibon data at face value, Woodie also fails to note that startup Palantir (which is totally into Hadoop) now reports more Big Data revenue than industry leader SAS.  Another unanswered question: if Hadoop is so inconsequential, why has Teradata lost half its market value since 2012?

IBM

IBM announces BigInsights 4.0 just nine months after releasing BigInsights 3.0.  BigInsights includes the usual Hadoop bits, plus:

  • BigSQL, a federation engine for SQL across relational databases and Hadoop
  • Big Sheets, a Datameer-like spreadsheet-on-Hadoop tool
  • SystemML, a home-grown machine learning library that runs in MapReduce
  • Text analytics capability
  • Big R, an interface that can push embarrassingly parallel R processing into Hadoop

Streaming and Real-Time Processing

On the O’Reilly Radar blog, Ben Lorica describes platforms and applications for processing data streams.

Gartner Advanced Analytics Magic Quadrant 2015

Gartner’s latest Magic Quadrant for Advanced Analytics is out; for reference, the 2014 report is here; analysis from Doug Henschen here.  Key changes from last year:

  • Revolution Analytics moves from Visionary to Niche
  • Alpine and Microsoft move from Niche to Visionary
  • Oracle, Actuate and Megaputer drop out of the analysis
Gartner 2015 Magic Quadrant, Advanced Analytics
Gartner 2015 Magic Quadrant, Advanced Analytics

Gartner changed its evaluation criteria this year to reflect only “native” (e.g. proprietary) functionality; as a result, Revolution Analytics dropped from Visionary to Niche.   Other vendors, it seems, complained to Gartner that the old criteria were “unfair” to those who don’t leverage open source functionality.  If Gartner applies this same reasoning to other categories, it will have to drop coverage of Hortonworks and evaluate Cloudera solely on the basis of Impala.  🙂

Interestingly, Gartner’s decision to ignore open source functionality did not impact its evaluation of open source vendors RapidMiner and KNIME.

Based on modest product enhancements from Version 4.0 to Version 5.0, Alpine jumped from Niche to Visionary.   Gartner’s inclusion criteria for the category mandate that “a vendor must offer advanced analytics functionality as a stand-alone product…”; this appears to exclude Alpine, which runs in Pivotal Greenplum database (*).  Gartner’s criteria are flexible, however, and I’m sure it’s purely coincidental that Gartner analyst Gareth Herschel flacks for Alpine.

(*) Yes, I know — Alpine supports other databases and Hadoop as well.   The number of Alpine customers who use it in anything other than Pivotal can meet in Starbucks at one of the little tables in the back.

Gartner notes that Alpine “still lacks depth of functionality. Several model techniques are either absent or not fully developed within its tool.”  Well, yes, that does seem important.   Alpine’s promotion to Visionary appears to rest on its Chorus collaboration capability (originally developed by Greenplum).  It seems, however, that customers don’t actually use Chorus very much; as Gartner notes, “adoption is currently slow and the effort to boost it may divert Alpine’s resources away from the core product.”

Microsoft’s reclassification from Niche to Visionary rests purely on the basis of Azure Machine Learning (AML), a product still in beta at the time of the evaluation.  Hardly anyone uses MSFT’s “other” offering for analytics (SQL Server Analytic Services, or SSAS), apparently for good reason:

  • “The 2014 edition of SSAS lacks breadth, depth and usability, in comparison with the Leaders’ offerings.”
  • “Microsoft received low scores from SSAS customers for its willingness to incorporate their feedback into future versions of the product.”
  • “SSAS is a low-performing product (with poor features, little data exploration and questionable usability.”

On paper, AML is an attractive product, though it maxes out at 10GB of data; however, it seems optimistic to rate Microsoft as “Visionary” purely on the basis of a beta product.  “Visionary” is a stretch in any case — analytic software that runs exclusively in the cloud is by definition a niche product, as it appeals only to a certain segment of the market.  AML’s most attractive capabilities are its ability to run Python and R — and, as we noted above — these no longer carry any weight with Gartner.

Dropping Actuate and Megaputer from the MQ simply recognizes the obvious.  It’s not clear why these vendors were included last year in the first place.

It appears that Oracle chose not to participate in the MQ this year.  Analytics that run in a single database platform are by definition niche products — you can’t use Oracle Advanced Analytics if you don’t have Oracle Database, and few customers will choose Oracle Database because it has Oracle Advanced Analytics.

 

Software for High Performance Advanced Analytics

Strata+Hadoop World week is a good opportunity to update the list of platforms for high-performance advanced analytics.  Vendors are hustling this week to announce their latest enhancements; I’ll post updates as needed.

First some definition.  The scope of this analysis includes software with the following properties:

  • Support for supervised and unsupervised machine learning
  • Support for distributed processing
  • Open platform or multi-vendor platform support
  • Availability of commercial support

There are three main “architectures” for high-performance advanced analytics available today:

  • Integration with an MPP database through table functions
  • Push-down integration with Hadoop
  • Native distributed computing, freestanding or co-located with Hadoop

I’ve written previously about the importance of distributed computing for high-performance predictive analytics, why it’s difficult to deliver and potentially disruptive to the analytics ecosystem.

This analysis excludes software that runs exclusively in a single vendor’s data platform (such as Netezza Analytics, Oracle Advanced Analytics or Teradata Aster‘s built-in analytic functions.)  While each of these vendors seeks to use advanced analytics to differentiate its data warehousing products, most enterprises are unwilling to invest in an analytics architecture that promotes vendor lock-in.  In my opinion, IBM, Oracle and Teradata should consider open sourcing their machine learning libraries, since they’re effectively giving them away anyway.

This analysis also excludes open source libraries “in the wild” (such as Vowpal Wabbit) that lack significant commercial support.

Open Source Software

H2O 

Distributor: H2O.ai (formerly 0xdata)

H20 is an open source distributed in-memory computing platform designed for deployment in Hadoop or free-standing clusters. Current functionality (Release 2.8.4.4) includes Cox Proportional Hazards modeling, Deep Learning, generalized linear models, gradient boosted classification and regression, k-Means clustering, Naive Bayes classifier, principal components analysis, and Random Forests. The software also includes tooling for data transformation, model assessment and scoring.   Users interact with the software through a web interface, a REST API or the h2o package in R.  H2O runs on Spark through the Sparkling Water interface, which includes a new Python API.

H2O.ai provides commercial support for the open source software.  There is a rapidly growing user community for H2O, and H2O.ai cites public reference customers such as Cisco, eBay, Paypal and Nielsen.

MADLib 

Distributor: Pivotal Software

MADLib is an open source machine learning library with a SQL interface that runs in Pivotal Greenplum Database 4.2 or PostgreSQL 9.2+ (as of Release 1.7).  While primarily a captive project of Pivotal Software — most of the top contributors are Pivotal or EMC employees — the support for PostgreSQL qualifies it for this list.    MADLib includes rich analytic functionality, including ten different regression methods, linear systems, matrix factorization, tree-based methods, association rules, clustering, topic modeling, text analysis, time series analysis and dimensionality reduction techniques.

Mahout

Distributor: Apache Software Foundation

Mahout is an eclectic machine learning project incepted in 2011 and currently included in major Hadoop distributions, though it seems to be something of an embarrassment to the community.  The development cadence on Mahout is very slow, as key contributors appear to have abandoned the project three years ago.   Currently (Release 0.9), the project includes twenty algorithms; five of these (including logistic regression and multilayer perceptron) run on a single node only, while the rest run through MapReduce.  To its credit, the Mahout team has cleaned up the software, deprecating unsupported functionality and mandating that all future development will run in Spark.  For Release 1.0, the team has announced plans to deliver several existing algorithms in Spark and H2O, and also to deliver something for Flink (for what that’s worth).  Several commercial vendors, including Predixion Software and RapidMiner leverage Mahout tooling in the back end for their analytic packages, though most are scrambling to rebuild on Spark.

Spark

Distributor: Apache Software Foundation

Spark is currently the platform of choice for open source high-performance advanced analytics.  Spark is a distributed in-memory computing framework with libraries for SQL, machine learning, graph analytics and streaming analytics; currently (Release 1.2) it supports Scala, Python and Java APIs, and the project plans to add an R interface in Release 1.3.  Spark runs either as a free-standing cluster, in AWS EC2, on Apache Mesos or in Hadoop under YARN.

The machine learning library (MLLib) currently (1.2) includes basic statistics, techniques for classification and regression (linear models, Naive Bayes, decision trees, ensembles of trees), alternating least squares for collaborative filtering, k-means clustering, singular value decomposition and principal components analysis for dimension reduction, tools for feature extraction and transformation, plus two optimization primitives for developers.  Thanks to large and growing contributor community, Spark MLLib’s functionality is expanding faster than any other open source or commercial software listed in this article.

For more detail about Spark, see my Apache Spark Page.

Commercial Software

Alpine Chorus

Vendor: Alpine Data Labs

Alpine targets a business user persona with a visual workflow-oriented interface and push-down integration with analytics that run in Hadoop or relational databases.  Although Alpine claims support for all major Hadoop distributions and several MPP databases, in practice most customers seem to use Alpine with Pivotal Greenplum database.  (Alpine and Greenplum have common roots in the EMC ecosystem).   Usability is the product’s key selling point, and the analytic feature set is relatively modest; however, Chorus’ collaboration and data cataloguing capabilities are unique.  Alpine’s customer list is growing; the list does not include a recent win (together with Pivotal) at a large global retailer.

dbLytix

Vendor: Fuzzy Logix

dbLytix is a library of more than eight hundred functions for advanced analytics; analytics run as database table functions and are currently supported in Informix, MySQL, Netezza, ParAccel, SQL Server, Sybase IQ, Teradata Aster and Teradata Database.  Embedded in SQL, analytics may be invoked from a range of application, including custom web interfaces, Microsoft Excel, popular BI tools, SAS or SPSS.   The software is highly extensible, and Fuzzy Logix offers a team of well-qualified consultants and developers for custom applications.

For those seeking the absolute cutting edge in advanced analytics, Fuzzy’s Tanay Zx Series offers more than five hundred analytic functions designed to run on GPU chips.  Tanay is available either as a software library or as an analytic appliance.

IBM SPSS Analytic Server

Vendor: IBM

Analytic Server serves as a Hadoop back end for IBM SPSS Modeler, a mature analytic workbench targeted to business users (licensed separately).  The product, which runs on Apache Hadoop, Cloudera CDH, Hortonworks HDP and IBM BigInsights, enables push-down MapReduce for a limited number of Modeler nodes.  Analytic Server supports most SPSS Modeler data preparation nodes, scoring for twenty-four different modeling methods, and model-building operations for linear models, neural networks and decision trees.  The cadence of enhancements for this product is very slow; first released in May 2013, IBM has released a single maintenance release since then.

RapidMiner Radoop

Vendor: RapidMiner

(Updated for Release 2.2)

RapidMiner targets a business user persona with a “code-free” user interface and deep selection of analytic features.  Last June, the company acquired Radoop, a three-year-old business partner based in Budapest.  Radoop brings to RapidMiner the ability to push down analytic processing into Hadoop using a mix of MapReduce, Mahout, Hive, Pig and Spark operations.

RapidMiner Radoop 2.2 supports more than fifty operators for data transformation, plus the ability to implement custom HiveQL and Pig scripts.  For machine learning, RapidMiner supports k-means, fuzzy k-means and canopy clustering, PCA, correlation and covariance matrices, Naive Bayes classifier and two Spark MLLib algorithms (logistic regression and decision trees); Radoop also supports Hadoop scoring capabilities for any model created in RapidMiner.

Support for Hadoop distributions is excellent, including Cloudera CDH, Hortonworks HDP, Apache Hadoop, MapR, Amazon EMR and Datastax Enterprise.  As of Release 2.2, Radoop supports Kerberos authentication.

Revolution R Enterprise

Vendor: Revolution Analytics

Revolution R Enterprise bundles a number of components, including Revolution R, an enhanced and commercially supported R distribution, a Windows IDE, integration tools and ScaleR, a suite of distributed algorithms for predictive analytics with an R interface.  A little over a year ago, Revolution released its version 7.0, which enables ScaleR to integrate with Hadoop using push-down MapReduce.   The mix of techniques currently supported in Hadoop includes tools for data transformation, descriptive statistics, linear and logistic regression, generalized linear models, decision trees, ensemble models and k-means clustering.   Revolution Analytics supports ScaleR in Cloudera, Hortonworks and MapR; Teradata Database; and in free-standing clusters running on IBM Platform LSF or Windows Server HPC.  Microsoft recently announced that it will acquire Revolution Analytics; this will provide the company with additional resources to develop and enhance the platform.

SAS High Performance Analytics

Vendor: SAS

SAS High Performance Analytics (HPA) is a distributed in-memory analytics engine that runs in Teradata, Greenplum or Oracle appliances, on commodity hardware or co-located in Hadoop (Apache, Cloudera or Hortonworks).  In Hadoop, HPA can be deployed either in a symmetric configuration (SAS instance on each DataNode) or in an asymmetric configuration (SAS deployed on dedicated “Analysis” nodes within the Hadoop cluster.)  While an asymmetric architecture seems less than ideal (due to the need for data movement and shuffling), it reduces the need to upgrade the hardware on every node and reduces SAS software licensing costs.

Functionally, there are five different bundles, for statistics, data mining, text mining, econometrics and optimization; each of these is separately licensed.  End users leverage the algorithms from SAS Enterprise Miner, which is also separately licensed.  Analytic functionality is rich compared to available high-performance alternatives, but existing SAS users will be surprised to see that many techniques available in SAS/STAT are unavailable in HPA.

SAS first introduced HPA in December, 2011 with great fanfare.  To date the product lacks a single public reference customer; this could mean that SAS’ Marketing organization is asleep at the switch, or it could mean that customer success stories with the product are few and far between.  As always with SAS, cost is an issue with prospective customers; other issues cited by customers who have evaluated the product include HPA’s inability to run existing programs developed in Legacy SAS, and concerns about the proprietary architecture. Interestingly, SAS no longer talks up this product in venues like Strata, pointing prospective customers to SAS In-Memory Statistics for Hadoop (see below) instead.

SAS In-Memory Statistics for Hadoop

Vendor: SAS

SAS In-Memory Statistics for Hadoop (IMSH) is an analytics application that runs on SAS’ “other” distributed in-memory architecture (SAS LASR Server).  Why does SAS have two in-memory architectures?  Good luck getting SAS to explain that in a coherent manner.  The best explanation, so far as I can tell, is a “mud-on-the-wall” approach to new product development.

Functionally, IMSH Release 2.5 supports data prep with SAS DS2 (an object-oriented language), descriptive statistics, classification and regression trees (C4.5), forecasting, general and generalized linear models, logistic regression, a Random Forests lookalike, clustering, association rule mining, text mining and a recommendation system.   Users interact with the product through SAS Studio, a web-based IDE introduced in SAS 9.4.

Overall, IMSH is a better value than HPA.  SAS prices this software based on the number of cores in the servers upon which it is deployed; while I can’t disclose the list price per core, it’s fair to say that any configuration beyond a sandbox will rapidly approach seven figures for the first year fee.

Skytree

Product: Skytree Infinity

Skytree began life as an academic machine learning project (FastLab, at Georgia Tech); the developers shopped the distributed machine learning core to a number of vendors and, finding no buyers, launched as a commercial software vendor in January 2013.  Recently rebranded from Skytree Server to Skytree Infinity, the product now includes modules for data marshaling and preparation that run on Spark.  Distributed algorithms can run as a free-standing cluster or co-located in Hadoop under YARN.  The product has a programming interface; the vendor claims ability to run from R, Weka, C++ and Python.   Neither Skytree’s modest list of algorithms nor its short list of public reference customers has changed in the past two years.

Strata Report: Advanced Analytics in Hadoop

Here is a quick review of the capabilities for advanced analytics in Hadoop for five vendors at the recent Strata NYC conference:

0XData

Product(s)

  • H20 (open source project)
  • h2o (R package)

Description

Smart people from Stanford with VC backing and a social media program.   Services business model with open source software.  H20 is an open source library of algorithms designed for deployment in Hadoop or free-standing clusters;  aggressive vision, but currently available functionality limited to GLM, k-Means, Random Forests.   Update: 0xData just announced H20 2.0, which includes Distributed Trees and Regression, such as Gradient Boosting Machine (GBM), Random Forest (RF), Generalized Linear Modeling (GLM), k-Means and Principal Component Analysis (PCA).  They also claim to run “100X faster than other predictive analytics providers”, although this claim is not supported by evidence.  R users can interface through h2o package.  Limited customer base.  Partners with Cloudera and MapR.

Key Points

  • True open source model
  • Comprehensive roadmap
  • Limited functionality
  • Limited user base
  • Performance claims undocumented

Alpine Data Labs

Product(s)

  • Alpine 2.8

Description

Alpine targets a business user persona with a visual workflow-oriented interface (comparable to SAS Enterprise Miner or SPSS Modeler).   Supports a reasonably broad range of analytic features.  Claims to run “in” a number of databases and Hadoop distributions, but company is opaque about how this works.  (Appears to be SQL/HiveQL push-down).   In practice, most customers seem to use Alpine with Greenplum.  Thin sales and customer base relative to claimed feature mix suggests uncertainty about product performance and stability.  Partners with Pivotal, Cloudera and MapR.

Key Points

  • Reasonable option for users already committed to Greenplum Database
  • Limited partner and user ecosystem
  • Performance and stability should be vetted thoroughly in POC

Oracle

Product(s)

Description

Oracle R Distribution (ORD) is a free distribution of R with bug fixes and performance enhancements; Oracle R Enterprise is a supported version of ORD with additional enhancements (detailed below).

Oracle Advanced Analytics (an option of Oracle Database Enterprise Edition) bundles Oracle Data Mining, a distributed data mining engine that runs in Oracle Database, and Oracle R Enterprise.   Oracle Advanced Analytics provides an R to SQL transparency layer that maps R functions and algorithms to native in-database SQL equivalents.  When in-database equivalents are not available, Oracle Advanced Analytics can run R commands under embedded R mode.

Oracle Connection to Hadoop  is an R interface to Hadoop; it enables the user to write MapReduce tasks in R and interface with Hive.  As of ORCH 2.1.0, there is also a fairly rich collection of machine learning algorithms for supervised and unsupervised learning that can be pushed down into Hadoop.

Key Points

  • Good choice for Oracle-centric organizations
  • Oracle Data Mining is a mature product with an excellent user interface
  • Must move data from Hadoop to Oracle Database to leverage OAA
  • Hadoop push-down from R requires expertise in MapReduce

SAS

Products

  • SAS/ACCESS Interface to Hadoop
  • SAS Scoring Accelerator for Cloudera
  • SAS Visual Analytics/SAS LASR Server
  • SAS High Performance Analytics Server

Description

SAS/ACCESS Interface to Hadoop enables SAS users to pass Hive, Pig or MapReduce commands to Hadoop through a connection and move the results back to the SAS server.   With SAS/ACCESS you can haul your data out of Hadoop, plug it into SAS and use a bunch of other SAS products, but that architecture is pretty much a non-starter for most Strata attendees.   Update:  SAS has announced SAS/ACCESS for Impala.

Visual Analytics is a Tableau-like visualization tool with limited predictive analytic capabilities; LASR Server is the in-memory back end for Visual Analytics.  High Performance Analytics is a suite of distributed in-memory analytics.   LASR Server and HPA Server can be co-located in a Hadoop cluster, but require special hardware.  Partners with Cloudera and Hortonworks.

Key Points

  • Legacy SAS connects to Hadoop, does not run in Hadoop
  • SAS/ACCESS users must know exact Hive, Pig or MapReduce syntax
  • Visual Analytics cannot work with “raw” data in Hadoop
  • Minimum hardware requirements for LASR and HPA significantly exceed standard Hadoop worker node specs
  • High TCO, proprietary architecture for all SAS products

Skytree

Product(s)

  • Skytree Server

Description

Academic machine learning project (FastLab, at Georgia Tech); with VC backing, launched as commercial software vendor January 2013.  Server-based technology, can connect to a range of data sources, including Hadoop.  Programming interface; claims ability to run from R, Weka, C++ and Python.  Good library of algorithms.  Partners with Cloudera, Hortonworks, MapR.  Skytree is opaque about technology and performance claims.

Key Points

  • Limited customer base, no announced sales since company launch
  • Hadoop integration is a connection, not “inside” architecture
  • Performance claims should be carefully vetted