Introduction to Microsoft Hadoop


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This is the slide deck from the June meeting of the Boise Web Technologies Group. Cindy Gross from Microsoft presented on Hadoop.

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  • Hadoop is part of NOSQL (Not Only SQL) and it’s a bit wild. You explore in/with Hadoop. You learn new things. You test hypotheses on unstructured jungle data. You eliminate noise.Then you take the best learnings and share them with the world via a relational or multidimensional database.Atomicity, consistency, isolation, durability (ACID) is used in relational databases to ensure immediate consistency. But what if eventual consistency is good enough? In stomps BASE – Basically available, soft state, eventual consistencyScale up or scale out?Pay up front or pay as you go?Which IT skills do you utilize?
  • Hive is a database that sits on top of Hadoop. HiveQL (HQL) generates (possibly multiple) MapReduce programs to execute the joins, filters, aggregates, etc. The language is very SQL-like, perhaps closer to MySQL but still very familiar.
  • Get your data from anywhere. There’s a data explosion and we can now use more of it than ever before. The portal provides an easy interface to pull in data from sources including secure FTP, Amazon S3, Azure blob store, Azure Data Market. Use Sqoop to move data between Hadoop and SQL Server, PDW, SQL Azure. The Hive ODBC driver lets you display Hive data in Excel or apps.
  • Many equate big data to MapReduce and in particular Hadoop. However, other applications like streaming, machine learning, and PDW type systems can also be described as big data solutions. Big Data is unstructured, flows fast, has many formats, and/or has quickly changing formats. How big is “big” really depends on what is too big/complex for your environment (hardware, people, software, processes). It’s done by scaling out on commodity (low end enterprise level) hardware.
  • Big data solutions are comprised of matching the right set of tools to the right set of problems (architectures are compositional, not monolithic)Need to select appropriate combinations of storage, analytics and consumers.
  • For demo steps see:
  • Big data is often described as problems that have one or more of the 3 (or 4) Vs – volume, velocity, variety, variability. Think about big data when you describe a problem with terms like tame the chaos, reduce the complexity, explore, I don’t know what I don’t know, unknown unknowns, unstructured, changing quickly, too much for what my environment can handle now, unused data.Volume = more data than the current environment can handle with vertical scaling, need to make sure of data that it is currently too expensive to useVelocity = Small decision window compared to data change rate, ask how quickly you need to analyze and how quickly data arrivesVariety = many different formats that are expensive to integrate, probably from many data sources/feedsVariability = many possible interpretations of the data
  • It’s not the hammer for every problem and it’s not the answer to every large store of data. It does not replace relational or multi-dimensional dbs, it’s a solution to a different sort of problem. It’s a new, specialized type of db for certain scenarios. It will feed other types of dbs.
  • Microsoft takes what is already there, makes it run on Windows, and offers the option of full control or simplificationHadoop in the cloud simplifies managementHadoop on Windows lets you reuse existing skillsJavaScript opens up more hiring optionsHive ODBC Driver / Excel Addin lets you combine data, move dataSqoop moves data – Linux based version to/from SQL available now, Windows based soon
  • Demo2 –Mashup1)      Hive Panea.       Excel, blank worksheet, datab.      Use your HadoopOnAzure clusterc.      Object = Gender2007 or whatever table you pre-loaded in Hive (select * from gender2007 limit 200)d.      KEY POINT = pulled data from multiple files across many nodes and displayed via ODBC is user friendly format – not easy in Hadoop world2)      PowerPivota.       KEY POINTS = uses local memory, pulls data from multiple data sources (structured and unstructured), can be stored/scheduled in Sharepoint, creates relationships to add value -- MASHUPb.      Excel file DeviceAnalysisByRegion.xlsx (worksheet with region/country data, relationship defined between Gender2007 country and this country data), click on PowerPivot tab and open blank tabc.       Click on PowerPivot Window – show each tab is data from a different source – hivesampletable (Hadoop/unstructured) and regions (could be anything/structured)d.      Click on diagram view – show relationships, rich valuee.      Pivot table.pivotchart.newf.        Close hive query windowg.       Values = count of platform, axis=platform, zoom to selectionh.      Slicers Vertical = regions hierarchyi.         Region = North America, country = Canada == Windows Phone jokesj.        KEY Load to Sharepoint, schedule refreshes, use for Power View
  • Expand your audience of decision makers by making BI easier with self-service, visualizationOur products interact and work together + one company for questions/issuesUse existing hardware, employeesExpand options for hiring/training/re-training with familiar tools Familiar tools = less rampup timeCloud = elasticity, easy scale up/down, pay for what you useEasier to move data to/from HDFS
  • It’s about separating the signal from the noise so you have insight to make decisions to take action. Discover, explore, gain insight.
  • Familiar tools, new tools, ease of use
  • Take action! All the exploring doesn’t help if you don’t do something! Something might be starting another round of exploring, but eventually DO SOMETHING!
  • Introduction to Microsoft Hadoop

    1. 1. INTRODUCTION TO HADOOP Cindy Gross | @SQLCindy | SQLCAT PM
    2. 2. SELECTdeviceplatform, state, countryFROM hivesampletableLIMIT 200;
    3. 3. Sqoopto/fromrelational
    4. 4. HadoopNot fully pre-structured
    5. 5. Hadoop EcosystemSnapshot
    6. 6. OpenSourceApacheHadoop
    7. 7. Hadoop: The Definitive Guide by Tom WhiteSQL Server Sqoop!/search/%23bigdataHive http://hive.apache.orgExcel to Hadoop via Hive ODBC On Azure Videos Big Data Lee Nolan Gross
    8. 8. @SQLCindy / @SQLCATWoman
    9. 9. INTRODUCTION TO HADOOP Cindy Gross | @SQLCindy | SQLCAT PM