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Big Data Analytics Projects - Real World with Pentaho
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Big Data Analytics Projects - Real World with Pentaho


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  • 1. Big Data Analytics Projects in the Real World Mark Kromer Pentaho Big Data Analytics Product Manager @mssqldude @kromerbigdata
  • 2. 1. The Big Data Technology Landscape 2. Big Data Analytics 3. Big Data Analytics Scenarios: ❯ Digital Marketing Analytics • Hadoop, Aster Data, SQL Server ❯ Sentiment Analysis • MongoDB, SQL Server ❯ Data Refinery • Hadoop, MPP, SQL Server, Pentaho 4. SQL Server in the Big Data world (Quasi-Real World) What we’ll (try) to cover today
  • 3. Big Data 101 3 V’s ❯ Volume – Terabyte records, transactions, tables, files ❯ Velocity – Batch, near-time, real-time (analytics), streams. ❯ Variety – Structures, unstructured, semi-structured, and all the above in a mix Text Processing ❯ Techniques for processing and analyzing unstructured (and structured) LARGE files Analytics & Insights Distributed File System & Programming
  • 4. Big Data ≠ NoSQL ❯ NoSQL has similar Internet-scale Web origins of Hadoop stack (Yahoo!, Google, Facebook, et al) but not the same thing ❯ Facebook, for example, uses Hbase from the Hadoop stack ❯ NoSQL does not have to be Big Data Big Data ≠ Real Time ❯ Big Data is primarily about batch processing huge files in a distributed manner and analyzing data that was otherwise too complex to provide value ❯ Use in-memory analytics for real time insights Big Data ≠ Data Warehouse ❯ I still refer to large multi-TB DWs as “VLDB” ❯ Big Data is about crunching stats in text files for discovery of new patterns and insights ❯ Use the DW to aggregate and store the summaries of those calculations for reporting Mark’s Big Data Myths
  • 5. • Batch Processing • Commodity Hardware • Data Locality, no shared storage • Scales linearly • Great for large text file processing, not so great on small files • Distributed programming paradigm Hadoop 1.x
  • 6. © Hortonworks Inc. 2014 Hadoop 1 vs Hadoop 2 HADOOP 1.0 HDFS (redundant, reliable storage) MapReduce (cluster resource management & data processing) HDFS2 (redundant, highly-available & reliable storage) YARN (cluster resource management) MapReduce (data processing) Others HADOOP 2.0 Single Use System Batch Apps Multi Purpose Platform Batch, Interactive, Online, Streaming, …
  • 7. © Hortonworks Inc. 2014 YARN: Taking Hadoop Beyond Batch Applications Run Natively in Hadoop HDFS2 (Redundant, Reliable Storage) YARN (Cluster Resource Management) BATCH (MapReduce) INTERACTIVE (Tez) STREAMING (Storm, S4,…) GRAPH (Giraph) IN-MEMORY (Spark) HPC MPI (OpenMPI) ONLINE (HBase) OTHER (Search) (Weave…) Store ALL DATA in one place… Interact with that data in MULTIPLE WAYS with Predictable Performance and Quality of Service
  • 8. © Hortonworks Inc. 2014 Tez – Introduction 1. Distributed execution framework targeted towards data-processing applications. 2. Based on expressing a computation as a dataflow graph. 3. Highly customizable to meet a broad spectrum of use cases. 4. Built on top of YARN – the resource management framework for Hadoop. 5. Open source Apache incubator project and Apache licensed.
  • 9. © Hortonworks Inc. 2014 Tez – Deep Dive – DAG API DAG dag = new DAG(); Vertex map1 = new Vertex(MapProcessor.class); Vertex map2 = new Vertex(MapProcessor.class); Vertex reduce1 = new Vertex(ReduceProcessor.class); Vertex reduce2 = new Vertex(ReduceProcessor.class); Vertex join1 = new Vertex(JoinProcessor.class); ……. Edge edge1 = Edge(map1, reduce1, SCATTER_GATHER, PERSISTED, SEQUENTIAL, MOutput.class, RInput.class); Edge edge2 = Edge(map2, reduce2, SCATTER_GATHER, PERSISTED, SEQUENTIAL, MOutput.class, RInput.class); Edge edge3 = Edge(reduce1, join1, SCATTER_GATHER, PERSISTED, SEQUENTIAL, MOutput.class, RInput.class); Edge edge4 = Edge(reduce2, join1, SCATTER_GATHER, PERSISTED, SEQUENTIAL, MOutput.class, RInput.class); ……. dag.addVertex(map1).addVertex(map2) .addVertex(reduce1).addVertex(reduce2) .addVertex(join1) .addEdge(edge1).addEdge(edge2) .addEdge(edge3).addEdge(edge4); reduce 1 map2 reduce 2 join1 map1 Scatter_Gather Bipartite Sequential Scatter_Gather Bipartite Sequential Simple DAG definition API
  • 10. © Hortonworks Inc. 2014 YARN Eco-system Page 10 Applications Powered by YARN Apache Giraph – Graph Processing Apache Hama - BSP Apache Hadoop MapReduce – Batch Apache Tez – Batch/Interactive Apache S4 – Stream Processing Apache Samza – Stream Processing Apache Storm – Stream Processing Apache Spark – Iterative applications Elastic Search – Scalable Search Cloudera Llama – Impala on YARN DataTorrent – Data Analysis HOYA – HBase on YARN Frameworks Powered By YARN Apache Twill REEF by Microsoft Spring support for Hadoop 2
  • 11. Apache Spark High-Speed In-Memory Analytics over Hadoop ● Open Source ● Alternative to Map Reduce for certain applications ● A low latency cluster computing system ● For very large data sets ● May be 100 times faster than Map Reduce for – Iterative algorithms – Interactive data mining ● Used with Hadoop / HDFS ● Released under BSD License
  • 12. Popular Hadoop Distributions
  • 13. Popular NoSQL Distributions Transactional-based, not analytical schemas
  • 14. Popular MPP Distributions Big Data as distributed, scale-out, sharded data stores
  • 15. Big Data Analytics Web Platform – RA 1
  • 16. Sentiment Analysis Reference Architecture 2 Big Data Platforms Hadoop PDW MongoDB Social Media Sources Data Orchestration Data Models Analytical Models OLAP Cubes Data Mining OLAP Analytics Tools, Reporting Tools, Dashboards
  • 17. Big Data Analytics
  • 18. • Distributed Data (Data Locality) ❯ HDFS / MapReduce ❯ YARN / TEZ ❯ Replicated / Sharded Data • MPP Databases ❯ Vertica, Aster, PDW, Greenplum … In-database analytics that can scale-out with distributed processing across nodes • Distributed Analytics ❯ SAS: Quickly solve complex problems using big data and sophisticated analytics in a distributed, in-memory and parallel environment.” • In-memory Analytics ❯ Microsoft PowerPivot (Tabular models) ❯ SAP HANA ❯ Tableau Big Data Analytics Core Tenets
  • 19. using Microsoft.Hadoop.MapReduce; using System.Text.RegularExpressions; public class TotalHitsForPageMap : MapperBase { public override void Map(string inputLine, MapperContext context) { context.Log(inputLine); var parts = Regex.Split(inputLine, "s+"); if (parts.Length != expected) //only take records with all values { return; } context.EmitKeyValue(parts[pagePos], hit); } } MapReduce Framework (Map)
  • 20. public class TotalHitsForPageReducerCombiner : ReducerCombinerBase { public override void Reduce(string key, IEnumerable<string> values, ReducerCombinerContext context) { context.EmitKeyValue(key, values.Sum(e=>long.Parse(e)).ToString()); } } public class TotalHitsJob : HadoopJob<TotalHitsForPageMap,TotalHitsForPageReducerCombiner> { public override HadoopJobConfiguration Configure(ExecutorContext context) { var retVal = new HadoopJobConfiguration(); retVal.InputPath = Environment.GetEnvironmentVariable("W3C_INPUT"); retVal.OutputFolder = Environment.GetEnvironmentVariable("W3C_OUTPUT"); retVal.DeleteOutputFolder = true; return retVal; } } MapReduce Framework (Reduce & Job)
  • 21. Linux shell commands to access data in HDFS Put file in HDFS: hadoop fs -put sales.csv /import/sales.csv List files in HDFS: c:Hadoop>hadoop fs -ls /import Found 1 items -rw-r--r-- 1 makromer supergroup 114 2013-05-07 12:11 /import/sales.csv View file in HDFS: c:Hadoop>hadoop fs -cat /import/sales.csv Kromer,123,5,55 Smith,567,1,25 Jones,123,9,99 James,11,12,1 Johnson,456,2,2.5 Singh,456,1,3.25 Yu,123,1,11 Now, we can work on the data with MapReduce, Hive, Pig, etc. Get Data into Hadoop
  • 22. create external table ext_sales ( lastname string, productid int, quantity int, sales_amount float ) row format delimited fields terminated by ',' stored as textfile location '/user/makromer/hiveext/input'; LOAD DATA INPATH '/user/makromer/import/sales.csv' OVERWRITE INTO TABLE ext_sales; Use Hive for Data Schema and Analysis
  • 23. sqoop import –connect jdbc:sqlserver://localhost –username sqoop -password password –table customers -m 1 > hadoop fs -cat /user/mark/customers/part-m-00000 > 5,Bob Smith sqoop export –connect jdbc:sqlserver://localhost –username sqoop -password password -m 1 –table customers –export-dir /user/mark/data/employees3 12/11/11 22:19:24 INFO mapreduce.ExportJobBase: Transferred 201 bytes in 32.6364 seconds (6.1588 bytes/sec) 12/11/11 22:19:24 INFO mapreduce.ExportJobBase: Exported 4 records. Sqoop Data transfer to & from Hadoop & SQL Server
  • 24. Role of NoSQL in a Big Data Analytics Solution ‣ Use NoSQL to store data quickly without the overhead of RDBMS ‣ Hbase, Plain Old HDFS, Cassandra, MongoDB, Dynamo, just to name a few ‣ Why NoSQL? ‣ In the world of “Big Data” ‣ “Schema later” ‣ Ignore ACID properties ‣ Drop data into key-value store quick & dirty ‣ Worry about query & read later ‣ Why NOT NoSQL? ‣ In the world of Big Data Analytics, you will need support from analytical tools with a SQL, SAS, MR interface ‣ SQL Server and NoSQL ‣ Not a natural fit ‣ Use HDFS or your favorite NoSQL database ‣ Consider turning off SQL Server locking mechanisms ‣ Focus on writes, not reads (read uncommitted)
  • 25. MongoDB and Enterprise IT Stack EDWHadoop Management&Monitoring Security&Auditing RDBMS CRM, ERP, Collaboration, Mobile, BI OS & Virtualization, Compute, Storage, Network RDBMS Applications Infrastructure Data Management Online Data Offline Data
  • 26. { _id : ObjectId("4e2e3f92268cdda473b628f6"), sourceIDs: { ABCSystemIDPart1: 8397897, ABCSystemIDPart2: 2937430, ABCSystemIDPart3: 932018 } accountType: “Checking”, accountOwners: [ { firstName : ”John", lastName: “Smith”, contactMethods: [ { type: “phone”, subtype: “mobile”, number: 8743927394}, { type: “mail”, address: “58 3rd St.”, city: …} ] possibleMatchCriteria: { govtID: 2938932432, fullName: “johnsmith”, dob: … } }, { firstName : ”Anne", maidenName: “Collins”, lastName: “Smith”, …} ], openDate: ISODate("2013-02-15 10:00”), accountFeatures { Overdraft: true, APR: 20, … } } General document per customer per account OR creditCardNumber: 8392384938391293 OR mortgageID: 2374389 OR policyID: 18374923
  • 27. Text Search Example (e.g. address typo so do fuzzy match) // Text search for address filtered by first name and NY > db.ticks.runCommand( “text”, { search: “vanderbilt ave. vander bilt”, filter: {name: “Smith”, city: “New York”} })
  • 28. //Find total value of each customer’s accounts for a given RM (or Agent) sorted by value db.accts.aggregate( { $match: {relationshipManager: “Smith”}}, { $group : { _id : “$ssn”, totalValue: {$sum: ”$value”} }}, { $sort: { totalValue: -1}} ) Aggregate: Total Value of Accounts
  • 29. SQL Server Big Data – Data Loading Amazon HDFS & EMR Data Loading Amazon S3 Bucket
  • 30. SQL Server Database ❯ SQL 2012 Enterprise Edition ❯ Page Compression ❯ 2012 Columnar Compression on Fact Tables ❯ Clustered Index on all tables ❯ Auto-update Stats Asynch ❯ Partition Fact Tables by month and archive data with sliding window technique ❯ Drop all indexes before nightly ETL load jobs ❯ Rebuild all indexes when ETL completes SQL Server Analysis Services ❯ SSAS 2012 Enterprise Edition ❯ 2008 R2 OLAP cubes partition-aligned with DW ❯ 2012 cubes in-memory tabular cubes ❯ All access through MSMDPUMP or SharePoint SQL Server Big Data Environment
  • 31. SQL Server Big Data Analytics Features
  • 32. DBA ETL/BI Developer Business Users & Executives Analysts & Data Scientists OPERATIONAL DATA BIG DATA DATA STREAMPUBLIC/PRIVATE CLOUDS Enterprise & Interactive Reporting Interactive Analysis Dashboards Predictive Analytics Pentaho Business Analytics Data Integration Instaview | Visual Map Reduce DIRECT ACCESS Pentaho Big Data Analytics
  • 33. Pentaho Big Data Analytics Accelerate the time to big data value • Full continuity from data access to decisions – complete data integration & analytics for any big data store • Faster development, faster runtime – visual development, distributed execution • Instant and interactive analysis – no coding and no ETL required
  • 34. Product Components Pentaho Data Integration • Visual development for big data • Broad connectivity • Data quality & enrichment • Integrated scheduling • Security integration • Visual data exploration • Ad hoc analysis • Interactive charts & visualizations Pentaho Dashboards • Self-service dashboard builder • Content linking & drill through • Highly customized mash-ups Pentaho Data Mining & Predictive Analytics • Model construction & evaluation • Learning schemes • Integration with 3rd part models using PMML Pentaho Enterprise & Interactive Reports • Both ad hoc & distributed reporting • Drag & drop interactive reporting • Pixel-perfect enterprise reports Pentaho for Big Data MapReduce & Instaview • Visual Interface for Developing MR • Self-service big data discovery • Big data access to Data Analysts Pentaho Analyzer
  • 35. ❯ Simple, easy-to-use visual data exploration ❯ Web-based thin client; in-memory caching ❯ Rich library of interactive visualizations • Geo-mapping, heat grids, scatter plots, bubble charts, line over bar and more • Pluggable visualizations ❯ Java ROLAP engine to analyze structured and unstructured data, with SQL dialects for querying data from RDBMs ❯ Pluggable cache integrating with leading caching architectures: Infinispan (JBoss Data Grid) & Memcached Pentaho Interactive Analysis & Data Discovery Highly Flexible Advanced Visualizations