Master Big Data with BI 3.0


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Big Data created an incredible hype in the market by establishing a whole new level of data exploration and analysis applications. It’s not a secret anymore that in order to keep up with the pace business world is moving today, organizations need to more easily and quickly access, interpret and distribute real-time analysis from a growing array of internal and external data to achieve their corporate objectives.

With the development of SQL Server 2012 and Panorama Necto, organizations have gained the ability not only to get a simple and secure access to Big Data, but to make it useful and meaningful for further exploration and advanced analysis. The application of Business Intelligence 3.0 capabilities enables them to master the vast amounts of unstructured data in a matter of minutes.

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  • Data sets of extreme volume and varietyThe “V”sVolume – exceed physical limited of vertical scalabilityVelocity - decision window small compared to data change rateVariety – many formats makes the integration expensive. Variability – many options for analysis
  • HDFS (Storage) => FilesMap Reduce (get the data), using native Java or Pig latin (Query Lang) => Map create giant hashtable, and reduce create a BLOB of mapped and reduced data. Hbase (NoSQL DB) => "Table"Hive (Metadata Store, "DW", more similar to SQL, where we plug in SQLSqoopPig is the query lang
  • As organizations collect more data it becomes inefficient to manage it in houseROI on hardware and maintenance is very highData retrieval and analysis becomes impossibly slow and requires a new approach to handle big data sizeMoving data to the cloud solves the 1st problemUsing Hadoop or similar framework solves the 2ndData can be spread across thousands of machinesQueries are distributed concurrently across machines, spreading the CPU load
  • Master Big Data with BI 3.0

    1. 1. Business Intelligence 3.0Master the World of BIG Data
    2. 2. We Live in a Data Explosion Era Data explosion caused by: Cloud computing, Rise of mobility, Globalization, Social media, machine data, web logs, sensor networks, RFID tags In 2012, the data overload will reach 2720 exabytes In 2015, the data overload will reach 7910 exabytes By 2020, the prediction is 35 Zettabytes.
    3. 3. Main trends in BI today: Big data It is easy to collect data, but difficult to make sense of it using traditional BI tools. The useful life of information has decreased, and so has the utility of BI tools Consumerization of Enterprise BI Use to get everything from the internet Access to analysis on their daily job Social & Contextual BI Mobile BI
    4. 4. The Challenge: “Decision Window” isNarrowing, while data is growing.. Lots of data need to be digested quickly, or risk of being irrelevant Decision need to be taken faster than competitors do The “holy grail” is to shorten the time from Data to Action Time Decision Window
    5. 5. BI 3.0 helps business users make sense of Big Data Consumerization Big Data BI 3.0 Of Enterprise BI Shortening the time from Data to Action…
    6. 6. Gartner: Analytics & BI are back to #1 Top technologies selected by CIOs
    7. 7. Gartner 10 top trends for 2012 Analytics Social & Contextual Tablets Cloud In memory Big data* more: mobile, apps store, internet of thing, low energy servers
    8. 8. What is BIG Data?
    9. 9. BIG Data is:Data sets of extreme volume and varietyThe “V”s Volume – exceed physical limited of scalability Velocity - decision window small compared to data change rate Variety – many formats makes the integration expensive. Variability – many options for analysis
    10. 10. Market sizing and growth In 2011 Big Data was a 9B$ business, which represents only 2% of 407B$ spent on Enterprise IT. By 2021 Big Data will become a huge 86B$ business, which will represents 11% of spent on Enterprise IT. 10 years CAGR of 25% for BigData compared to Enterprise IT CAGR of only 5%
    11. 11. What‟s in it for Business People?
    12. 12. The best „Return on Investment‟ fromBig data is Analytics Organizations collects huge amount of data The most common use case for such data is to analyze and find hidden insights, correlations, etc
    13. 13. Storing and processing Big Data with the Hadoop Framework Map SQLBigData HDFS Hbase Hive BI Reduce Server Hadoop framework BI Suite
    14. 14. + Enterprise-Ready• Native connector• Enhanced Security• Ease of deployment• Integration to the Enterprise Data Warehouse• Simplified programming• Seamless connectivity via BI tools and Excel
    15. 15. When you have a World of Data.. You need a better Compass
    16. 16. Panorama NectoTM BI 3.0: Build Your Corporate IntelligenceADVANCED ANALYTICS Easy-to-use analytics for business users and advanced analytical SOCIAL BI capabilities for power users Engaging platform for collaborative decision making Self Service CONTEXTUAL DISCOVERY Intelligent BI engine thatautomatically pushes relevant insights by understanding user’s behavior
    17. 17. Contextual discovery helps you focus on whats relevant INTERESTS GRAPH - Tags - Likes - Visits “This is what I like”SOCIAL GRAPH Example: Amazon, netflix- Friending- Discussions- Annotations DATA - Insights - Exceptions - Models “This is who I know” Example: Facebook, linkedin
    18. 18. Social BI, your essential tool for Big Data Leverage the Power of Many to get better Insights Work collaboratively for better and faster insights and decision making Create add-hoc teams to discuss subjects at hand Add unstructured knowledge layer Follow other’s work Enables you to work in an ever evolving data generation
    19. 19. Advanced Analytics on Big DataSlice and diceDrill-ups, drill-downs and drill-throughsAdvanced filteringSimple and bubble up exceptionsFormulas and parametersInstant calculated membersSliding filtersInteractive ChartingOne click interactive reportingLarge dimension handlingAdvanced MDX and DAX toolsAnd much, much more
    20. 20. True Self Service on Big Data Users can create their own Workboards or easily find the WorkBoard they should work on With Necto and SQL 2012 you can easily add your own sources of information and connect them to the organizational data for better and deeper insight
    21. 21. Big Data Example
    22. 22. Azure and Hadoop Windows Azure is Microsoft computing and storage Cloud Azure provides the Hadoop framework as storage and processing layer and solves the two issues of the data explosion Moving data to Azure removes the need of managing hardware and data maintenance Dealing with massive data analysis using the Azure hadoop framework
    23. 23. Public data Analyzing organizational data help business users understand WHAT has happened. Mashing up this data with public Big Data sources can help business users understand WHY things happened
    24. 24. Demonstration
    25. 25. The Big Data Demonstration Flow 1 2 3 4 Azure Hadoop Publish to Insights Data Cluster SQL 2012 through Market Necto
    26. 26. Try it yourself Download Necto from Request access to Hadoop on Azure Use those slides to guide you through the process, connect Necto to Hadoop and enjoy the experience.