Implementing an Optimized Analytics Solution on IBM Power Systems

Implementing an Optimized Analytics Solution on IBM Power Systems

by Dino Quintero, Kanako Harada, Reinaldo Tetsuo Katahira, Antonio Moreira de Oliveira Neto, Robert Simon, Brian Yaeger
Released June 2016
Publisher(s): IBM Redbooks
ISBN: 9780738441689

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Book description

This IBM® Redbooks® publication addresses topics to use the virtualization strengths of the IBM POWER8® platform to solve clients' system resource utilization challenges and maximize systems' throughput and capacity.

This book addresses performance tuning topics that will help answer clients' complex analytic workload requirements, help maximize systems' resources, and provide expert-level documentation to transfer the how-to-skills to the worldwide teams.

This book strengthens the position of IBM Analytics and Big Data solutions with a well-defined and documented deployment model within a POWER8 virtualized environment, offering clients a planned foundation for security, scaling, capacity, resilience, and optimization for analytics workloads.

This book is targeted toward technical professionals (analytics consultants, technical support staff, IT Architects, and IT Specialists) who are responsible for providing analytics solutions and support on IBM Power Systems™.

Table of contents

  1. Front cover
  2. Notices
    1. Trademarks
  3. IBM Redbooks promotions
  4. Preface
    1. Authors
    2. Now you can become a published author, too!
    3. Comments welcome
    4. Stay connected to IBM Redbooks
  5. Chapter 1. Introduction
    1. 1.1 Why IBM POWER8
    2. 1.2 POWER8 processor technology
    3. 1.3 POWER8 scale-out servers
  6. Chapter 2. Solution reference architecture
    1. 2.1 Big data and analytics general architectures
    2. 2.2 Hardware reference architecture
      1. 2.2.1 IBM Power Systems servers
      2. 2.2.2 General architecture for BigInsights for Apache Hadoop
      3. 2.2.3 General architecture for analytics applications
      4. 2.2.4 Networking
      5. 2.2.5 Data storage
      6. 2.2.6 IBM Data Engine for Analytics reference architecture
    3. 2.3 Software reference architecture
      1. 2.3.1 IBM BigInsights for Apache Hadoop and IBM Open Platform with Apache Hadoop clusters
      2. 2.3.2 DB2 with BLU Acceleration
      3. 2.3.3 Predictive Analytics with SPSS
      4. 2.3.4 Reporting insights with IBM Cognos Business Intelligence
      5. 2.3.5 Spectrum Scale and File Placement Optimizer
      6. 2.3.6 Cluster management
    4. 2.4 Solution reference architecture
      1. 2.4.1 Solution scenario overall topology
      2. 2.4.2 Solution scenario architecture
  7. Chapter 3. IBM POWER8 for analytics workloads
    1. 3.1 Value proposition
      1. 3.1.1 Data processing capacity (more data per second)
      2. 3.1.2 More density for same workload
      3. 3.1.3 More resiliency for the data
    2. 3.2 Advantages
      1. 3.2.1 POWER8 memory bandwidth
      2. 3.2.2 POWER8 I/O bandwidth
      3. 3.2.3 POWER8 performance
      4. 3.2.4 IBM Spectrum Scale advantages
  8. Chapter 4. Scenario: How to implement the solution components
    1. 4.1 Basic infrastructure requirements
    2. 4.2 Using Ambari to deploy BigInsights with Spectrum Scale
      1. 4.2.1 Understanding supported deployment approaches, including Spectrum Scale with Ambari
      2. 4.2.2 Download software
      3. 4.2.3 Set up and install the Ambari server
      4. 4.2.4 Deploying the IBM Open Platform edition of BigInsights
      5. 4.2.5 Installing the BigInsights value-add packages
    3. 4.3 DB2 with BLU Acceleration to store structured data
      1. 4.3.1 DB2 system requirements
      2. 4.3.2 DB2 license requirements and functionality
      3. 4.3.3 IBM DB2 with BLU Acceleration deployment
      4. 4.3.4 Set up the DB2 instance
      5. 4.3.5 GOSALES Cognos Business Intelligence sample database
      6. 4.3.6 SPSS Collaboration and Deployment Services database repository
    4. 4.4 SPSS Analytical Decision Management
      1. 4.4.1 Outline of steps
      2. 4.4.2 Install the prerequisite items for AIX
      3. 4.4.3 Install the Installation Manager
      4. 4.4.4 Install and configure WebSphere Application Server
      5. 4.4.5 Install and configure the DB2 database
      6. 4.4.6 Install and configure SPSS Modeler Server
      7. 4.4.7 Install and configure IBM SPSS Collaboration and Deployment Service
      8. 4.4.8 Install and configure IBM SPSS Modeler Server Adapters for Collaboration and Deployment Services
      9. 4.4.9 Install IBM SPSS Analytical Decision Management
      10. 4.4.10 Install SPSS Collaboration and Deployment Service
    5. 4.5 Cognos for Dashboarding
      1. 4.5.1 Install IBM Java SDK 6.0
      2. 4.5.2 Install DB2 client
      3. 4.5.3 Install Apache HTTP server 2
      4. 4.5.4 Install and configure OpenLDAP
      5. 4.5.5 Configure Cognos Business Intelligence
      6. 4.5.6 Configure the Apache HTTP Server for Cognos
      7. 4.5.7 Copy DB2 client drivers to Cognos libraries
      8. 4.5.8 Apply Cognos fix packs
      9. 4.5.9 Install Framework Manager
      10. 4.5.10 Apply Cognos Fix Packs for client
  9. Chapter 5. Scenario: Integration of the components for the solution
    1. 5.1 IBM Big SQL integration
      1. 5.1.1 Proposed solution integration with IBM Big SQL
      2. 5.1.2 Configure the connection with IBM Data Server Manager for Big SQL
      3. 5.1.3 Configure the connection with the jsqsh command line
      4. 5.1.4 Cognos Business Intelligence data source by using the DB2 CLI connection
      5. 5.1.5 Cognos Business Intelligence data source by using the JDBC connection
      6. 5.1.6 IBM Big SQL federation
      7. 5.1.7 Loading GOSALESDW data into Big SQL
      8. 5.1.8 Loading Twitter Data into Big SQL
      9. 5.1.9 Querying data from Big SQL
    2. 5.2 Cognos Business Intelligence integration
      1. 5.2.1 OpenLDAP integration with Cognos Business Intelligence
      2. 5.2.2 IBM Big SQL data source configuration
      3. 5.2.3 IBM DB2 with BLU Acceleration data source configuration
    3. 5.3 SPSS Analytical Decision Management and Scoring Services integration
      1. 5.3.1 SPSS Analytical Decision Management integration with SPSS Modeler Server sample solution
      2. 5.3.2 SPSS Analytical Decision Management integration with DB2 and Big SQL
      3. 5.3.3 SPSS Scoring Services integration with Web services
  10. Chapter 6. Scenario: How to use the solution
    1. 6.1 Dashboard and reporting analysis
      1. 6.1.1 View of BigInsights
      2. 6.1.2 View of Cognos Business Intelligence
      3. 6.1.3 View of IBM SPSS Analytical Decision Management
      4. 6.1.4 Customer experience: Online shop
    2. 6.2 Type of analysis
      1. 6.2.1 Cognos Business Intelligence advantages
      2. 6.2.2 SPSS Analytical Decision Management advantages
    3. 6.3 Combinations and correlations of structured and unstructured data types
      1. 6.3.1 Sample case goal
      2. 6.3.2 Story of this sample case
      3. 6.3.3 How to score and deploy the scenario
      4. 6.3.4 Differences between Cognos Business Intelligence and SPSS Analytical Decision Management
      5. 6.3.5 Where the data comes from
    4. 6.4 Use cases and examples
      1. 6.4.1 Disclaimer
      2. 6.4.2 Installed software
      3. 6.4.3 How to implement the sample case
      4. 6.4.4 Links
  11. Appendix A. Advanced implementation
    1. Suggestions for Cognos Dynamic Cubes
    2. Modeler Advantage in IBM SPSS Analytical Decision Management
  12. Appendix B. Planning Ambari node roles
    1. Ambari node roles
  13. Related publications
    1. IBM Redbooks
    2. Online resources
    3. Help from IBM
  14. Back cover

Product information

  • Title: Implementing an Optimized Analytics Solution on IBM Power Systems
  • Author(s): Dino Quintero, Kanako Harada, Reinaldo Tetsuo Katahira, Antonio Moreira de Oliveira Neto, Robert Simon, Brian Yaeger
  • Release date: June 2016
  • Publisher(s): IBM Redbooks
  • ISBN: 9780738441689