Advanced Analytics and Big Data (August 2014)


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Summary of currently available options for advanced analytics in Hadoop

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Advanced Analytics and Big Data (August 2014)

  1. 1. 1 Advanced Analytics with Big Data Thomas W. Dinsmore
  2. 2. Advanced Analytics with Big Data •What do we mean by “Big Data”? •Do we need to use all of the data? •What analytics can run inside Big Data platforms? 2
  3. 3. Big Data •Data that cannot be efficiently handled in a relational database •The three Vs: •Volume •Variety •Velocity 3
  4. 4. Big Data Platforms •Hadoop ecosystem: MapReduce, Hive, Impala, Spark etc •Appliances: Teradata, IBM PureData, Pivotal, Oracle BDA, Vertica, Par Accel/Redshift etc etc •NoSQL/NewSQL: Cassandra, Mongo, MemSQL •Streaming engines: Infosphere Streams 4 Convergence: Federated SQL engines (e.g.) Pivotal Hawq
  5. 5. 6 Analytics Platform For aggregate models, you can simply sample the data and work offline.
  6. 6. 7 Anomaly Detection Affinity Analysis Microsegmentation Social Network Analysis Collaborative Filtering However, for some use cases you may need to use all of the data.
  7. 7. 8 Catastrophic Risk Modeling Modeling with Fine-grained Behavioral Data For other use cases, using all of the data is worth extra time and effort.
  8. 8. 9 HDFS HDFS HDFS HDFS HDFS HDFS Data Most legacy analytic packages can read HDFS files.
  9. 9. 10 HDFS HDFS HDFS HDFS HDFS HDFS MapReduce Data Some tools also provide pass-through capabilities.
  10. 10. 11 HDFS HDFS HDFS HDFS HDFS HDFS MapReduce Advantages •Co-exists w/ other applications •Integrated workload management •Simplified administration Disdvantages •MapReduce latency Several tools translate user requests to MapReduce. This eliminates data movement and co-exists well with other applications.
  11. 11. 12 YARN HDFS Map Reduce HDFS Map Reduce HDFS Map Reduce HDFS Map Reduce HDFS Map Reduce HDFS Map Reduce Advantages •Easy to adapt legacy apps •Isolates analytic workload Disdvantages •Data moves within the cluster •Requires YARN YARN (*) makes it possible to bypass MapReduce and run analytics in memory on dedicated nodes. (*) Yet Another Resource Negotiater
  12. 12. 13 HDFS Map Reduce YARN HDFS Map Reduce HDFS Map Reduce HDFS Map Reduce HDFS Map Reduce HDFS Map Reduce HDFS Map Reduce Advantages •Lowest latency Disdvantages •Upgrade every node •Requires YARN Distributing in-memory analytics across the Hadoop cluster minimizes internal data movement.
  13. 13. 14 Open Source Projects
  14. 14. Apache Mahout •Apache incubator project (2007) •Machine learning library •Included in most distributions •Thin acceptance, few contributors •Diverse architecture •Single-node •MapReduce •New algos run on Spark •Recently cleaned up 15
  15. 15. Apache Giraph •Apache top-level project •Runs in MapReduce •Dedicated graph engine •Used by Facebook, few others •Dead in the water •No presence in leading distros •No significant commercial support •No releases in 13 months •No recent code commits on Git 16
  16. 16. GraphLab •Carnegie Mellon project (2009) •Distributed in-memory engine: •Primarily graph analysis •Selected machine learning algos •Interface from Java, JavaScript, Python •GraphLab Inc provides commercial support (2013, $6.75MM) •Independent distribution, or through Pivotal •Minimal development effort past six months 17
  17. 17. 0xdata H2O •Vendor-driven open source project •0xdata sells support, customization •Distributed in-memory prediction engine •Multiple deployment options: •Standalone (with HDFS) •Over YARN •In MapReduce •Claims 2,000+ users •4 public references •Used by a leading P&C insurer •Java, R, Python and Scala interfaces 18
  18. 18. Apache Spark •Top-level Apache project (2/14) •Release 1.02 (8/14) •Distributed in-memory analytics •Machine learning •Graph analytics •Streaming analytics •Fast SQL •Compatible with Hadoop storage •Integrated with YARN •Scala, Python, Java interfaces (+SparkR) •Growing ecosystem •Supported in leading Hadoop distributions 19
  19. 19. Analytic Features 22 0xdata H2O 2.2 Apache Giraph 1.1 Apache Mahout 0.9 Apache Spark 1.02 GraphLab 2.2 Prediction +++ + +++ Dimension Reduction + +++ + + Clustering + +++ + +++ Collaborative Filtering +++ + +++ Text Analytics +++ +++ Matrix Operations + +++ + Graph Analysis + + +++
  20. 20. Summary: Open Source •Giraph appears to be dead in the water •Mahout may be recovering from roadkill status •GraphLab outperforms Spark GraphX today in graph analytics •0xdata H2O currently has more machine learning features than Spark MLLib and a better R interface •Spark catching up fast •More resources and distribution •Integrated platform for ML and graph analysis 23
  21. 21. 24 Commercial Software
  22. 22. Alpine •Business user interface •Collaboration environment •Broad library of techniques •Strong cloud offering •Leverages Hadoop (multiple distros), Hawq or Pivotal Greenplum •Push-down MapReduce •Certified on Spark •Small but growing customer base 25
  23. 23. IBM SPSS Analytics Server •Introduced 2013 •Serves as “back end” for SPSS Modeler •Uses push-down MR •Limited analytic feature set •IBM supports on multiple Hadoop distros •Customer acceptance unknown 26
  24. 24. Revolution Analytics ScaleR •ScaleR library of distributed statistics, machine learning functions •Tools to distribute arbitrary R functions •Runs in Cloudera, Hortonworks, Teradata, LSF clusters, MS HPC •Hadoop edition uses MR push-down •Tools simplify installation in large clusters •R interface •Partnerships with Alteryx, Qlik, MicroStrategy, Tableau provide business interfaces 27
  25. 25. Skytree Server •Georgia Tech’s FastLab project, repurposed as commercial software •Distributed machine learning platform •Very opaque about technical details •User interface is an API •Co-located in Hadoop under YARN •Just certified by Hortonworks •Customer acceptance unknown •No new public references in a year •Used by leading credit card company 28
  26. 26. SAS High-Performance Analytics •Distributed in-memory analytics •Designed to run in special-purpose appliances (2011) •Repurposed to run in Hadoop (2013) •Co-exists poorly — cannot run SAS and MapReduce at the same time •Reads entire dataset into memory •Uses MPI to communicate among nodes •Requires upgrades from standard Hadoop infrastructure •Customer acceptance unknown •No public references •Generic success stories missing from Strata presos 29
  27. 27. SAS LASR Server •SAS’ “other” distributed in-memory platform •Back end for several end-user products •SAS Visual Analytics (2012) •SAS Visual Statistics (New) •SAS In-Memory Statistics for Hadoop (New) •Recently added statistics and machine learning •Does not read raw HDFS; must be transformed to proprietary SASHDAT •Like HPA, reads entire dataset into memory. •16 Core 256GB node can load 75GB table •Runs DS2 programs, not Legacy SAS programs •Fast, but with limited feature set •SAS claims 1,400 “sites” for Visual Analytics •Many of those are standalone boxes 30
  28. 28. Summary: Commercial •Alpine’s interface is compelling to business user •IBM Analytics Server is a good first release •RRE ScaleR appeals to R users, plays well in Hadoop sandbox •Skytree Server: strong in prediction •SAS: why two competing memory-centric architectures? 31
  29. 29. Progress •Spark: blindingly fast maturity •Rapidly expanding library of analytic features •Growing developer community, ecosystem •Commercial: from zero to many 32
  30. 30. Interesting Questions •Will Mahout get a second wind? •Will Spark MLLib displace 0xdata? •Will Spark GraphX catch up to GraphLab? •Can Spark Streaming compete with Storm and commercial entrants? •How quickly will customers adopt memory-centric architecture for analytics? •What will Alpine and MicroStrategy do with Spark? •When will SAS announce a reference customer for HPA/LASR in Hadoop? 33
  31. 31. Questions 34
  32. 32. Thank You 35
  33. 33. 36 Advanced Analytics with Big Data Thomas W. Dinsmore