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Data Integration and Data Warehousing for Cloud, Big Data and IoT: 
What’s New, What’s Coming … and What’s Missing Right Now

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Data Integration and Data Warehousing for Cloud, Big Data and IoT: 
What’s New, What’s Coming … and What’s Missing Right Now

  1. 1. Mark Rittman, Independent Analyst, MJR Analytics DATA INTEGRATION AND DATA WAREHOUSING FOR CLOUD, BIG DATA AND IOT: 
 WHAT’S NEW, WHAT’S COMING … AND WHAT’S MISSING RIGHT NOW BIG DATA WORLD, LONDON London, March 2017
  2. 2. •Oracle ACE Director, Independent Analyst •Past ODTUG Exec Board Member + Oracle Scene Editor •Author of two books on Oracle BI •Co-founder & CTO of Rittman Mead •15+ Years in Oracle BI, DW, ETL + now Big Data •Host of the Drill to Detail Podcast (www.drilltodetail.com) •Based in Brighton & work in London, UK About The Presenter 2
  3. 3. A BIT OF HISTORY… 3
  4. 4. •Data warehouses provided a unified view of the business •Single place to store key data and metrics •Joined-up view of the business •Aggregates and conformed dimensions •ETL routines to load, cleanse and conform data •BI tools for simple, guided access to information •Tabular data access using SQL-generating tools •Drill paths, hierarchies, facts, attributes •Fast access to pre-computed aggregates •Packaged BI for fast-start ERP analytics 4 Oracle MongoDB Oracle Sybase IBM DB/2 MS SQL MS SQL Server Core ERP Platform Retail Banking Call Center E-Commerce CRM 
 Business Intelligence Tools 
 Data Warehouse Access &
 Performance
 Layer ODS /
 Foundation
 Layer 4 Data Warehousing Back in Mid-2000’s
  5. 5. How Traditional RDBMS Data Warehousing Scaled-Up 5 Shared-Everything Architectures (i.e. Oracle RAC, Exadata) Shared-Nothing Architectures
 (e.g. Teradata, Netezza)
  6. 6. •Google needed to store and query their vast amount of server log files •And wanted to do so using cheap, commodity hardware •Google File System and MapReduce designed together for this use Around the Same Time… 6
  7. 7. •GFS optimised for particular task at hand - computing PageRank for sites •Streaming reads for PageRank calcs, block writes for crawler whole-site dumps •Master node only holds metadata •Stops client/master I/O being bottleneck, also acts as traffic controller for clients •Simple design, optimised for specific Google Need •MapReduce focused on simple computations on abstraction framework •Select & filter (MAP) and reduce (aggregate) functions, easily to distribute on cluster •MapReduce abstracted cluster compute, HDFS abstracted cluster storage •Projects that inspired Apache Hadoop + HDFS Google File System + MapReduce Key Innovations 7
  8. 8. •A way of storing (non-relational) data cheaply and easily expandable •Gave us a way of scaling beyond TB-size without paying $$$ •First use-cases were offline storage, active archive of data Hadoop’s Original Appeal to Data Warehouse Owners 8 (c) 2013
  9. 9. •Driven by pace of business, and user demands for more agility and control •Traditional IT-governed data loading not always appropriate •Not all data needed to be modelled right-away •Not all data suited storing in tabular form •New ways of analyzing data beyond SQL •Graph analysis •Machine learning Data Warehousing and ETL Needed Some Agility 9
  10. 10. •Hadoop started by being synonymous with MapReduce, and Java coding •But YARN (Yet another Resource Negotiator) broke this dependency •Hadoop now just handles resource management •Multiple different query engines can run against data in-place •General-purpose (e.g. MapReduce) •Graph processing •Machine Learning •Real-Time Processing Hadoop 2.0 - Enabling Multiple Query Engines 10
  11. 11. •Storing data in format it arrived in, and then applying schema at query time •Suits data that may be analysed in different ways by different tools •In addition, some datatypes may have schema embedded in file format •Key benefit - fast arriving data of unknown value can get to users earlier •Made possible by tools such as Apache Hive + SerDes,
 Apache Drill and self-describing file formats, HDFS storage Advent of Schema-on-Read, and Data Lakes 11
  12. 12. •Data now landed in Hadoop clusters, NoSQL databases and Cloud Storage •Flexible data storage platform with cheap storage, flexible schema support + compute •Solves the problem of how to store new types of data + choose best time/way to process it •Hadoop/NoSQL increasingly used for all store/transform/query tasks Data Warehousing Circa 2010 : The “Data Lake” 12 Data Transfer Data Access Data Factory Data Reservoir Business Intelligence Tools Hadoop Platform File Based Integration Stream Based Integration Data streams Discovery & Development Labs Safe & secure Discovery and Development environment Data sets and samples Models and programs Marketing / Sales Applications Models Machine Learning Segments Operational Data Transactions Customer Master ata Unstructured Data Voice + Chat Transcripts ETL Based Integration Raw Customer Data Data stored in the original format (usually files) such as SS7, ASN.1, JSON etc. Mapped Customer Data Data sets produced by mapping and transforming raw data
  13. 13. DATA WAREHOUSING 
 & BIG DATA TODAY… 13
  14. 14. •On-premise Hadoop, even with simple resilient clustering, will hit limits •Clusters can reach 5000+ nodes, need to scale-up for demand peaks etc •Scale limits are encountered way beyond those for DWs… •… but future is elastically-scaled, query and compute-as-a-service On-Premise Big Data Analytics Hits Its Limits 14 Oracle Big Data Cloud Compute Edition Free $300 developer credit at:
 https://cloud.oracle.com/en_US/tryit
  15. 15. •New generation of big data platform services from Google, Amazon, Oracle •Combines three key innovations from earlier technologies: •Organising of data into tables and columns (from RDBMS DWs) •Massively-scalable and distributed storage and query (from Big Data) •Elastically-scalable Platform-as-a-Service (from Cloud) Elastically-Scalable Data Warehouse-as-a-Service 15
  16. 16. Example Architecture : Google BigQuery 16
  17. 17. •And things come full-circle … analytics typically requires tabular data •Google BigQuery based-on DremelX massively-parallel query engine •But stores data columnar and provides SQL interface •Solves the problem of providing DW-like functionality at scale, as-a-service •This is the future … ;-) BigQuery : Big Data Meets Data Warehousing 17
  18. 18. DATAFLOW PIPELINES 
 ARE THE NEW ETL… 18
  19. 19. New ways to do BI
  20. 20. New ways to do BI
  21. 21. MACHINE LEARNING & SEARCH FOR 
 “AUTOMAGIC” SCHEMA DISCOVERY 21
  22. 22. New ways to do BI
  23. 23. •By definition there's lots of data in a big data system ... so how do you find the data you want? •Google's own internal solution - GOODS ("Google Dataset Search") •Uses crawler to discover new datasets •ML classification routines to infer domain •Data provenance and lineage •Indexes and catalogs 26bn datasets •Other users, vendors also have solutions •Oracle Big Data Discovery •Datameer •Platfora •Cloudera Navigator Google GOODS - Catalog + Search At Google-Scale 23
  24. 24. A NEW TAKE ON BI… 24
  25. 25. •Came out if the data science movement, as a way to "show workings" •A set of reproducible steps that tell a story about the data •as well as being a better command-line environment for data analysis •One example is Jupyter, evolution of iPython notebook •supports pySpark, Pandas etc •See also Apache Zepplin Web-Based Data Analysis Notebooks 25
  26. 26. AND EMERGING OPEN-SOURCE
 BI TOOLS AND PLATFORMS 26
  27. 27. And Emerging Open-Source
 BI Tools and Platforms wp-content/uploads/2016/05/paper.pdf
  28. 28. And Emerging Open-Source
 BI Tools and Platforms
  29. 29. … Which Is What I’m Working On Right Now 30
  30. 30. THANK YOU 31
  31. 31. Mark Rittman, Independent Analyst, MJR Analytics DATA INTEGRATION AND DATA WAREHOUSING FOR CLOUD, BIG DATA AND IOT: 
 WHAT’S NEW, WHAT’S COMING … AND WHAT’S MISSING RIGHT NOW BIG DATA WORLD, LONDON London, March 2017

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