The Big Deal About Big Data

1. The Big Deal About Big Data @db2Dean facebook.com/db2Dean www.db2Dean.com Dean CompherData Management Technical Professional for UT, NVdcomphe@us.ibm.com www.db2Dean.com Slides Created and Provided by: • Paul Zikopoulos • Tom Deustch

2. Why Big DataHow We Got Here

3. …by the end of 2011, this was about 30 billion and growing even faster In 2005 there were 1.3 billion RFID tags in circulation… 3

4. An increasingly sensor-enabled and instrumented business environment generates HUGE volumes of data with MACHINE SPEED characteristics… LHR  JKF: 640TBs 1 BILLION lines of codeEACH engine generating 10 TB every 30 minutes!

5. 350B Transactions/YearMeter Reads every 15 min. 3.65B – meter reads/day 120M – meter reads/month

6. In August of 2010, Adam Savage, of “Myth Busters,” took a photo of his vehicle using his smartphone. He then posted the photo to his Twitter account including the phrase “Off to work.” Since the photo was taken by his smartphone, the image contained metadata revealing the exact geographical location the photo was taken By simply taking and posting a photo, Savage revealed the exact location of his home, the vehicle he drives, and the time he leaves for work

7. The Social Layer in an Instrumented Interconnected World 4.6 billion camera phones world wide 30 billion RFID tags today (1.3B in 2005) 12+ TBsof tweet data every day 100s of millions of GPS enabled devices sold annually ? TBs ofdata every day 2+ billion people on the Web by end 2011 25+ TBs oflog data every day 76 million smart meters in 2009… 200M by 2014

8. Twitter Tweets per Second Record Breakers of 2011

9. Extract Intent, Life Events, Micro Segmentation Attributes Pauline Name, Birthday, Family Tom Sit Not Relevant - Noise Tina Mu Monetizable Intent Not Relevant - Noise Jo Jobs Wishful Thinking Location Relocation SPAMbots Monetizable Intent

10. Big Data Includes Any of the following Characteristics Extracting insight from an immense volume, variety and velocity of data, in context, beyond what was previously possible Variety: Velocity: Volume: Manage the complexity of data in many different structures, ranging from relational, to logs, to raw text Streaming data and large volume data movement Scale from Terabytes to Petabytes (1K TBs) to Zetabytes (1B TBs)

11. Bigger and Bigger Volumes of Data • Retailers collect click-stream data from Web site interactions and loyalty card data • This traditional POS information is used by retailer for shopping basket analysis, inventory replenishment, +++ • But data is being provided to suppliers for customer buying analysis • Healthcare has traditionally been dominated by paper-based systems, but this information is getting digitized • Science is increasingly dominated by big science initiatives • Large-scale experiments generate over 15 PB of data a year and can’t be stored within the data center; sent to laboratories • Financial services are seeing large and large volumes through smaller trading sizes, increased market volatility, and technological improvements in automated and algorithmic trading • Improved instrument and sensory technology • Large Synoptic Survey Telescope’s GPixel camera generates 6PB+ of image data per year or consider Oil and Gas industry

12. The Big Data Conundrum • The percentage of available data an enterprise can analyze is decreasing proportionately to the available to it • Quite simply, this means as enterprises, we are getting “more naive” about our business over time • We don’t know what we could already know…. Data AVAILABLE to an organization SignalsandNoise 001100110010010101001010011110010010110101101000100100010001010011110010001001000100100011001000100100010010001010001001000 Data an organization can PROCESS

13. Why Not All of Big Data Before: Didn’t have the Tools?

14. Applications for Big Data Analytics Smarter Healthcare Multi-channel sales Log Analysis Finance Homeland Security Search Quality Telecom Traffic Control Manufacturing Trading Analytics Fraud and Risk Retail: Churn, NBO

15. Most Requested Uses of Big Data Log Analytics & Storage Smart Grid / Smarter Utilities RFID Tracking & Analytics Fraud / Risk Management & Modeling 360° View of the Customer Warehouse Extension Email / Call Center Transcript Analysis Call Detail Record Analysis +++

16. So What Is Hadoop?

17. Hadoop Background Apache Hadoop is a software framework that supports data-intensive applications under a free license. It enables applications to work with thousands of nodes and petabytes of data. Hadoop was inspired by Google Map/Reduce and Google File System papers. Hadoop is a top-level Apache project being built and used by a global community of contributors, using the Java programming language. Yahoo has been the largest contributor to the project, and uses Hadoop extensively across its businesses. Hadoop is a paradigm that says that you send your application to the data rather than sending the data to the application 17

18. What Hadoop Is Not • It is not a replacement for your Database & Warehouse strategy • Customers need hybrid database/warehouse & hadoop models • It is not a replacement for your ETL strategy • Existing data flows aren’t typically changed, they are extended • It is not designed for real-time complex event processing like Streams • Customers are asking for Streams & BigInsights integration

19. So What Is Really New Here? • Cost effective / Linear Scalability. • Hadoop brings massively parallel competing to commodity servers. You can start small and scales linearly as your work requires. • Storage and Modeling at Internet-scale rather than small sampling • Cost profile for super-computer level compute capabilities • Cost per TB of storage enables superset of information to be modeled • Mixing Structured and Unstructured data. • Hadoop is its schema-less so it doesn’t care about the form the data stored is in, and thus allows a super-set of information to be commonly stored. Further, MapReduce can be run effectively on any type of data and is really limited by the creatively of the developer. • Structure can be introduced at the MapReduce run time based on the keys and values defined in the MapReduce program. Developers can create jobs that against structured, semi-structured, and even unstructured data. • Inherently flexible of what is modeled/analytics run • Ability to change direction literally on a moment’s notice without any design or operational changes • Since hadoop is schema-less, and can introduce structure on the fly, the type of analytics and nature of the questions being asked can be changed as often as needed without upfront cost or latency

20. Break It Down For Me Here… • Hadoop is a platform and framework, not a database • It uses both the CPU and disc of single commodity boxes, or node • Boxes can be combined into clusters • New nodes can be added as needed, and added without needing to change the; • Data formats • How data is loaded • How jobs are written • The applications on top

21. So How Does It Do That? At its core, hadoop is made up of; • Map/Reduce • How hadoop understands and assigns work to the nodes (machines) • Hadoop Distributed File System = HDFS • Where hadoop stores data • A file system that’s runs across the nodes in a hadoop cluster • It links together the file systems on many local nodes to make them into one big file system

22. What is HDFS • The HDFS file system stores data across multiple machines. • HDFS assumes nodes will fail, so it achieves reliability by replicating data across multiple nodes • Default is 3 copies • Two on the same rack, and one on a different rack. • The filesystem is built from a cluster of data nodes, each of which serves up blocks of data over the network using a block protocol specific to HDFS. • They also serve the data over HTTP, allowing access to all content from a web browser or other client • Data nodes can talk to each other to rebalance data, to move copies around, and to keep the replication of data high.

23. File System on my Laptop

24. HDFS File System Example

25. Map/Reduce Explained "Map" step: The program is chopped up into many smaller sub-problems. A worker node processes some subset of the smaller problems under the global control of the JobTracker node and stores the result in the local file system where a reducer is able to access it. "Reduce" step: Aggregation The reduce aggregates data from the map steps. There can be multiple reduce tasks to parallelize the aggregation, and these tasks are executed on the worker nodes under the control of the JobTracker. 25

26. The MapReduce Programming Model "Map" step: Program split into pieces Worker nodes process individual pieces in parallel (under global control of the Job Tracker node) Each worker node stores its result in its local file system where a reducer is able to access it "Reduce" step: Data is aggregated (‘reduced” from the map steps) by worker nodes (under control of the Job Tracker) Multiple reduce tasks can parallelize the aggregation 26

27. Map/Reduce Job Example

28. Murray 38 • Salt Lake 39 • Bluffdale 35 • Sandy 32 • Salt Lake 42 • Murray 31 Map Reduce Shuffle • Murray 38 • Bluffdale 37 • Murray 38 • Bluffdale 35 • Bluffdale 37 • Murray 30 • Murray 38 • Bluffdale 35 • Sandy 32 • Salt Lake 42 • Sandy 40 • Salt Lake 42 • Bluffdale 32 • Sandy 40 • Murray 27 • Salt Lake 25 • Bluffdale 37 • Sandy 32 • Salt Lake 23 • Murray 30 • Sandy 40 • Salt Lake 25 • Bluffdale 37 • Murray 30 • Sandy 40 • Salt Lake 25 • Sandy 32 • Salt Lake 42

29. MapReduce In more Detail • Map-Reduce applications specify the input/output locations and supply map and reduce functions via implementations of appropriate Hadoop interfaces, such as Mapper and Reducer. • These, and other job parameters, comprise the job configuration. The Hadoop job client then submits the job (jar/executable, etc.) and configuration to the JobTracker • The JobTracker then assumes the responsibility of distributing the software/configuration to the slaves, scheduling tasks and monitoring them, providing status and diagnostic information to the job-client. • The Map/Reduce framework operates exclusively on <key, value> pairs — that is, the framework views the input to the job as a set of <key, value> pairs and produces a set of <key, value> pairs as the output of the job, conceivably of different types. • The vast majority of Map-Reduce applications executed on the Grid do not directly implement the low-level Map-Reduce interfaces; rather they are implemented in a higher-level language, such as Jaql, Pig or BigSheets

30. JobTracker and TaskTrackers Map/Reduce requests are handed to the Job Tracker which is a master controller for the map and reduce tasks. Each worker node contains a Task Tracker process which manages work on the local node. The Job Tracker pushes work out to the Task Trackers on available worker nodes, striving to keep the work as close to the data as possible The Job Tracker knows which node contains the data, and which other machines are nearby If the work cannot be hosted on the actual node where the data resides, priority is given to nodes in the same rack This reduces network traffic on the main backbone network. If a Task Tracker fails or times out, that part of the job is rescheduled 30

31. Skill Required How To Create Map/Reduce Jobs • Map/reduce development in Java • Hard, few resources that know this • Pig • Open source language / Apache sub-project • Becoming a “standard” • Hive • Open source language / Apache sub-project • Provides a SQL-like interface to hadoop • Jaql • IBM Research Invented • More powerful than Pig when dealing with loosely structure data • Visa has been a development partner • BigSheets • BigInsights browser based application • Little development required • You’ll use this most often

32. Taken Together - What Does This Result In? • Easy To Scale • Simply add machines as your data and jobs require • Fault Tolerant and Self-Healing • Hadoop runs on commodity hardware and provides fault tolerance through software. • Hardware losses are expecting and tolerated • When you lose a node the system just redirects work to another location of the data and nothing stops, nothing breaks, jobs, applications and users don’t even know. • Hadoop Is Data Agnostic • Hadoop can absorb any type of data, structured or not, from any number of sources. • Data from many sources can be joined and aggregated in arbitrary ways enabling deeper analyses than any one system can provide. • Hadoop results can be consumed by any system necessary if the output is structured appropriately • Hadoop Is Extremely Flexible • Start small, scale big • You can turn nodes “off” and use for other needs if required (really) • Throw any data, in any form or format, you want at it • What you use it for can be changed on a whim

33. The IBM Big Data Platform

34. Analytic Sandboxes – aka “Production” • Hadoop capabilities exposed to LOB with some notion of IT support • Not really production in an IBM sense • Really “just” ad-hoc made visible to more users in the organization • Formal declaration of direction as part of the architecture • “Use it, but don’t count on it” • Not built for secutity

35. Production Usage with SLAs • SLA driven workloads • Guaranteed job completion • Job completion within operational windows • Data Security Requirements • Problematic if it fails or looses data • True DR becomes a requirements • Data quality becomes an issue • Secure Data Marts become a hard requirement • Integration With The Rest of the Enterprise • Workload integration becomes an issue • Efficiency Becomes A Hot Topic • Inefficient utilization on 20 machines isn’t an issue, on 500 or 1000+ it is • Relatively few are really here yet outside of Facebook, Yahoo, LinkedIn, etc… • Few are thinking of this but it is inevitable

36. IBM – Delivers a Platform Not a Product • Hardened Environment • Removes single points of failure • Security • All Components Tested Together • Operational Processes • Ready for Production • Mature / Pervasive usage • Deployed and Managed Like Other Mature Data Center Platforms • BIG INSIGHTS • Text Analytics, Data Mining, Streams, Others

37. The IBM Big Data Platform InfoSphere BigInsights Hadoop-based low latency analytics for variety and volume Hadoop Information Integration Stream Computing InfoSphere Information ServerHigh volume data integration and transformation InfoSphere StreamsLow Latency Analytics for streaming data MPP Data Warehouse IBM InfoSphere WarehouseLarge volume structured data analytics IBM Netezza High Capacity ApplianceQueryableArchive Structured Data IBM Netezza 1000BI+Ad Hoc Analytics on Structured Data IBM Smart Analytics SystemOperational Analytics on Structured Data IBM Informix TimeseriesTime-structured analytics

38. What Does a Big Data Platform Do? Analyze a Variety of Information Novel analytics on a broad set of mixed information that could not be analyzed before Analyze Information in Motion Streaming data analysisLarge volume data bursts and ad-hoc analysis Analyze Extreme Volumes of Information Cost-efficiently process and analyze PBs of informationManage & analyze high volumes of structured, relational data Discover and Experiment Ad-hoc analytics, data discovery and experimentation Manage and Plan Enforce data structure, integrity and control to ensure consistency for repeatable queries

39. Big Data Enriches the Information Management Ecosystem Active Archive Cost Optimization Master Data Enrichment via Life Events, Hobbies, Roles, +++ Establishing Information as a Service Audit MapReduce Jobs and tasks OLTP Optimization(SAP, checkout, +++) Who Ran What, Where, and When? Managing a Governance Initiative

40. Get More Information…

41. www.bigdatauniversity.com

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