IBM Smarter Analytics für Big Data

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1 FZI Forschungszentrum Informatik am Karlsruher Institut für Technologie Technologie Workshop Big Data 22. Juni 2015 Axel J. Schwarz Mobil: IBM Smarter Analytics für Big Data v1.3 1

2 Paradigm shift enabled by Big Data Analytics is expanding from enterprise data to big data, creating new opportunities for competitive advantage Traditional Approach Structured, analytical, logical New Approach Creative, holistic thought, intuition Transaction Data Data Warehouse Hadoop & Streaming Data Web Logs, URLs Contact Center notes Customer Data Core System Data Structured Repeatabl e Linear Enterprise Integration Unstructured Exploratory Iterative Text Data: s, chats Social Data Log data 3 rd Party Data Traditional Sources New Sources Geolocation data 2

3 Paradigm shift enabled by Big Data Leverage more of the data being captured TRADITIONAL APPROACH BIG DATA APPROACH All available information Analyzed information All available information analyzed Analyze small subsets of information Analyze all information 3

4 Paradigm shift enabled by Big Data Reduce effort required to leverage data TRADITIONAL APPROACH BIG DATA APPROACH Small amount of carefully organized information Large amount of messy information Carefully cleanse information before any analysis Analyze information as is, cleanse as needed 4

5 Paradigm shift enabled by Big Data Data leads the way and sometimes correlations are good enough TRADITIONAL APPROACH BIG DATA APPROACH Hypothesis Question Data Exploration Answer Data Insight Correlation Start with hypothesis and test against selected data Explore all data and identify correlations 5

6 Paradigm shift enabled by Big Data Leverage data as it is captured TRADITIONAL APPROACH BIG DATA APPROACH Data Analysis Data Repository Analysis Insight Insight Analyze data after it s been processed and landed in a warehouse or mart Analyze data in motion as it s generated, in real-time 6

7 TRADITIONAL APPROACH IBM Smarter Analytics für Big Data Paradigm shift enabled by Big Data - Summary Analyze small subsets of information Analyze all information All available information Analyzed information All available information analyzed BIG DATA APPROACH Hypothesis Question Data Exploration Answer Data Insight Correlation Start with hypothesis and test against selected data Explore all data and identify correlations 7

8 TRADITIONAL APPROACH IBM Smarter Analytics für Big Data Paradigm shift enabled by Big Data - Summary Analyze small subsets of information Analyze all information EVERY All available information EXACT SLOW Hypothesis Question Analyzed information THING LACK OF Data Exploration All available information analyzed BIG DATA APPROACH Answer Data Insight CONTROL Correlation Start with hypothesis and test against selected data Explore all data and identify correlations 8

9 IBM Big Data & Analytics Referenz-Architektur Komponenten - Überblick 9

10 IBM Big Data & Analytics Referenz-Architektur Komponenten - Detaillierung 10

11 IBM Big Data & Analytics Referenz-Architektur Komponenten Fokus 11

12 In-Memory Big Data Processing - Apache Spark IBM Investing in Four Catalysts for Big Data Adoption Open Source Innovation Technical Standards Familiar Interfaces & Integration with Established Tools New Analytics Capabilities 12

13 In-Memory Big Data Processing - Apache potentially the most significant open source project of the next decade. To further accelerate open source innovation for the Spark ecosystem, IBM is taking the following actions: 13 IBM will build Spark into the core of the company s analytics and commerce platforms. IBM's Watson Health Cloud will leverage Spark as a key underpinning for its insight platform, helping to deliver faster time to value for medical providers and researchers as they access new analytics around population health data. IBM will open source its breakthrough IBM SystemML machine learning technology and collaborate with Databricks to advance Spark s machine learning capabilities. IBM will offer Spark as a Cloud service on IBM Bluemix to make it possible for app developers to quickly load data, model it, and derive the predictive artifact to use in their app. IBM will commit more than 3,500 researchers and developers to work on Spark-related projects [ ], and open a Spark Technology Center in San Francisco [ ]. IBM will educate more than 1 million data scientists and data engineers on Spark through extensive partnerships with AMPLab, DataCamp, MetiStream, Galvanize and BigData University MOOC.

14 In-Memory Big Data Processing - Apache Spark The Combination: The Flexibility of Spark on a Stable Hadoop Platform Unlimited Scale Ease of Development In-Memory Performance Enterprise Platform Wide Range of Data Formats Combine Workflows 14

15 In-Memory Big Data Processing - Apache Spark Die wichtigsten Bewohner des Hadoop-Zoos Quelle: Uwe Seiler: Zoo voller Gehege, in: ix Developer Big Data, 2/2015, S.37 15

16 In-Memory Big Data Processing - Apache Spark What s the scoop with Hadoop? Der Link zum Video findet sich im Anhang dieser Präsentation. 16

17 In-Memory Big Data Processing - Apache Spark Spark Libraries Spark SQL Spark Streaming GraphX MLlib SparkR Apache Spark 17

18 In-Memory Big Data Processing - Apache Spark Spark on Hadoop Spark SQL Spark Streaming GraphX MLlib SparkR Apache Spark Apache Hadoop-YARN Apache Hadoop-HDFS Slave node 1 Slave node 2 Slave node n Resource management Storage management Compute layer 18

19 In-Memory Big Data Processing - Apache Spark IBM Open Platform with Apache Hadoop 100% open source code Commitment to currency: days, not months Includes Spark Free for production use Decoupled Apache Hadoop from IBM analytics and data science technologies Production support offering available IBM Open Platform with Apache Hadoop HDFS MapReduce Spark Hive HCatalog Pig YARN Ambari HBase Flume Sqoop Solr/Lucene Apache Open Source Components 19

20 In-Memory Big Data Processing - Apache Spark IBM is Committed to Open Source Open source technologies are the base for IBM software and solutions IBM s long history of deep open source commitment Apache Software Foundation: Founding member in 1999 Cloud Foundry: #1 contributor; Basis for Bluemix OpenStack: #4 contributor; Basis for IBM s IaaS Linux: #3 contributor; IBM first enterprise backer of Linux Hadoop/Spark: Extensive investment in open source contribution; Integration with Analytics software Application Systems 20 Infrastructure

21 In-Memory Big Data Processing - Apache Spark IBM BigInsights for Apache Hadoop for Apache Hadoop IBM BigInsights Analyst Big SQL BigSheets IBM BigInsights Data Scientist Text Analytics Machine Learning with Big R Big R Big SQL BigSheets IBM BigInsights Enterprise Management POSIX Distributed File System Multi-workload, Multi-tenant scheduling IBM Open Platform with Apache Hadoop 21

22 Link: 22

23 Exkurs: SIDAP Echtzeit-Betriebsanalyse Betrieb auf Basis globaler Erfahrung Otto von Bismarck, Winston Churchill: Der Weise lernt von den Fehlern der Anderen, der Dumme aus den Eigenen 23 Device: Gerät, Apparat, Einrichtung, Verfahren, Einheit Quelle: Dr. T. Pötter, Dr. M. Steffen (beide Bayer Technology Services)

24 Exkurs: SIDAP Echtzeit-Betriebsanalyse Betrieb auf Basis globaler Erfahrung 24 Quelle: Dr. T. Pötter, Dr. M. Steffen (beide Bayer Technology Services)

25 Exkurs: SIDAP Echtzeit-Betriebsanalyse Betrieb auf Basis globaler Erfahrung 25 Quelle: Dr. T. Pötter, Dr. M. Steffen (beide Bayer Technology Services)

26 Exkurs: SIDAP Echtzeit-Betriebsanalyse Betrieb auf Basis globaler Erfahrung Die Qualität der Vorhersagen von Fehlern wächst mit der Anzahl der Einträge in die Datenbasis. 26 Quelle: Dr. T. Pötter, Dr. M. Steffen (beide Bayer Technology Services)

27 Exkurs: SIDAP Datenaggregation im Rahmen von SIDAP Quelle: Dr. T. Pötter, Dr. M. Steffen (beide Bayer Technology Services) 27

28 Exkurs: SIDAP Use-Cases 28

29 Exkurs: SIDAP Use-Cases und Aufgabenbereiche der Partner 29

30 Raw Logs and Machine Data IBM Smarter Analytics für Big Data Exkurs: SIDAP Echtzeit-Betriebsanalyse (Operations Analysis) Real-time Monitoring InfoSphere Streams Capture Data Stream SPSS Modeler Identify Anomaly Decision Management Historical Reporting and Analysis InfoSphere BigInsights Raw Data SPSS Modeler Predict and Classify Aggregate Results Dashboard/BI SPSS Modeler Predict and Score Data Warehouse Store Results Federated Navigation and Discovery 30

31 Exkurs: Internet of Things IBM s Internet of Things Foundation Secure Device Registration Scalable Device Connectivity Historian Visual wiring PAYG SaaS pricing Powered by IBM MessageSight technology Manage Assemble Collect Connect 31

32 Exkurs: Internet of Things IoT becomes a Composable Business IoT end-end solutions Connected appliance solutions, Smarter home solutions App tips open community IoT-related Bluemix services Rules, Push, Geo location, Analytics, Asset management, Predictive Maintenance IoT Foundation Secure Device Registration, Scalable Device Connectivity, Historian, Visual wiring Device recipe open community 32 Devices & Gateways

33 Exkurs: Internet of Things IBM Bluemix How to build a smarter App Der Link zum Video findet sich im Anhang dieser Präsentation. 33

34 Exkurs: Internet of Things IBM Internet of Things Foundation 34

35 Exkurs: Internet of Things IBM Internet of Things Foundation Status Quo 35

36 Exkurs: Internet of Things IBM Internet of Things Foundation Status Quo 36

37 Exkurs: Internet of Things IBM Internet of Things Foundation Status Quo 37

38 Exkurs: Internet of Things IBM Internet of Things Foundation Status Quo 38

39 Exkurs: Internet of Things IBM Internet of Things Foundation Status Quo 39

40 Exkurs: Internet of Things Device Recipes make it faster Connect 40

41 Exkurs: Internet of Things Application Connection Connect Publish the same data to many applications with MQTT Visualisation on app or Brower based interface App on Bluemix Access control via Application Registration & Secure Token Compose with other IoT Services in Bluemix e.g. HD Insights, Notification services, Twitter analytics 41

42 IBM Smarter Analytics für Big Data Exkurs: Internet of Things Beispiel 42

43 IBM Smarter Analytics für Big Data Exkurs: Internet of Things Beispiel 43

44 Empfehlung Ia IBM Bluemix Link: 44

45 Empfehlung Ib IBM Internet of Things Zone auf Bluemix IBM Internet of Things Foundation IoT Zone in Bluemix ibm.biz/try_iot oder https://bluemix.net/solutions/iot 45

46 Empfehlung II IBM Watson Analytics Link: m/analytics/watson -analytics/ 46

47 Empfehlung III Big Data University Link: m/ 47

48 Dipl. Wirtsch.-Ing. Axel J. Schwarz Mobik: Software Client Architect Vielen Dank! 48

49 Quellen und Referenzen Literatur/Blog-Beiträge Videos Links Ryan Baxter: Bluemix and the Internet of Things Bernard Marr: Spark Or Hadoop -- Which Is The Best Big Data Framework? Paul Miller - IBM Backs Apache Spark For Big Data Analytics IBM IBM Big Data: How it works - https://www.youtube.com/watch?v=u5jwc89xbzi What s the Scoop with Hadoop? - https://www.youtube.com/watch?v=bcgqijxcdo8 How to build a smarter app - https://www.youtube.com/watch?v=e5tknbwl2co Learn how to create a mobile app quickly using IBM BlueMix! - https://www.youtube.com/watch?v=dbxn2mz90b0 IBM Emerging Technology - Node-RED: IBM developerworks - IoT: https://www.ibmdw.net/iot/ IBM developerworks - Build a cloud-ready temperature sensor with the Arduino Uno and the IBM IoT Foundation: Bluemi Bildquellen IBM sowie lizenzfreie digitale Bilder der Medien-Bibliotheken: 123RF (http://www.123rf.com) und Stock.XCHNG (http:///www.sxc.hu). 49

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