Big Data in the Real World


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Here I talk about examples and use cases for Big Data & Big Data Analytics and how we accomplished massive-scale sentiment, campaign and marketing analytics for Razorfish using a collecting of database, Big Data and analytics technologies.

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Big Data in the Real World

  1. 1. Big Data in the Real World Orlando PASS October 2013 Mark Kromer @kromerbigdata @mssqldude
  2. 2. What we’ll (try) to cover today ‣ What is Big Data? ‣ The Big Data and Apache Hadoop environment ‣ Big Data Analytics ‣ SQL Server in the Big Data world ‣ Microsoft + Hortonworks (Yahoo!) = HDInsights 2
  3. 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. 4. Mark’s Big Data Myths ‣ 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
  5. 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
  6. 6. Popular Hadoop Distributions Hosted PaaS Hadoop platforms: Amazon EMR, Pivotal, Microsoft Hadoop on Azure
  7. 7. Popular NoSQL Distributions Transactional-based, not analytics schemas
  8. 8. Popular MPP Distributions Big Data as distributed, scale-out, sharded data stores
  9. 9. Big Data Analytics Web Platform - Example
  10. 10. MapReduce Framework (Map) 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); } }
  11. 11. MapReduce Framework (Reduce & Job) 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; } }
  12. 12. Get Data into Hadoop ‣ ‣ ‣ ‣ 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.
  13. 13. Use Hive for Data Schema and Analysis 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;
  14. 14. Sqoop Data transfer to & from Hadoop & SQL Server ‣ 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.
  15. 15. SQL Server Big Data – Data Loading Amazon HDFS & EMR Data Loading Amazon S3 Bucket
  16. 16. 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)
  17. 17. SQL Server Big Data Environment ‣ 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
  18. 18. SQL Server Big Data Analytics Features ‣ Columnstore ‣ Sqoop adapter ‣ PolyBase ‣ Hive ‣ In-memory analytics ‣ Scale-out MPP
  19. 19. Microsoft’s Data Solution – Big Data & PDW Excel with PowerPivot Predictive Analytics Embedded BI Data Market Place Familiar End User Tools S S R S SSAS BI Platform Hundreds of TB of Data (structured) Petabytes of Data (Unstructured) Hadoop On Windows Azure Sensors Hadoop On Windows Server Devices Data Market Bots Connectors Parallel Data Warehouse Crawlers ERP CRM LOB APPs Unstructured and Structured Data 19 19
  20. 20. MICROSOFT BIG DATA immersive data experiences PowerPivot Self-Service Power View Collaboration Corporate Apps Devices connecting with worlds data Combine Discover Refine Microsoft HDInsight Server any data, any size, anywhere StreamInsight Parallel Data Warehouse Relational HDInsight Service Non-relational Analytical Streaming
  21. 21. Microsoft .NET Hadoop APIs ‣ WebHDFS ‣ Linq to Hive ‣ MapReduce ‣ C# ‣ Java ‣ Hive ‣ Pig ‣ ‣ SQL on Hadoop ‣ Cloudera Impala ‣ Teradata SQL-H ‣ Microsoft Polybase ‣ Hadapt
  22. 22. Data Movement to the Cloud ‣ Use Windows Azure Blob Storage • Already stored in 3 copies • Hadoop can read from Azure blob storage • Allows you to upload while using no Hadoop network or CPU resources ‣ Compress files • • • • Hadoop can read Gzip Uses less network resources than uncompressed Costs less for direct storage costs Compress directories where source files are created as well. 23
  23. 23. Wrap-up ‣ What is a Big Data approach to Analytics? ‣ Massive scale ‣ Data discovery & research ‣ Self-service ‣ Reporting & BI ‣ Why do we take this Big Data Analytics approach? ‣ TBs of change data in each subject area ‣ The data in the sources are variable and unstructured ‣ SSIS ETL alone couldn’t keep up or handle complexity ‣ SQL Server 2012 columnstore and tabular SSAS 2012 are key to using SQL Server for Big Data ‣ With the configs mentioned previously, SQL Server works great ‣ Analytics on Big Data also requires Big Data Analytics tools ‣ Aster, Tableau, PowerPivot, SAS, Parallel Data Warehouse