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A short overview about OBIEE architecture and various olap servers.

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  1. 1. Presented by ARC
  2. 2. BI Degree of Intelligence CompetitiveAdvantage How many, how often, where?Ad hoc reports Query/drill down Alerts Statistical analysis Forecasting/extrapolation Predictive modeling Optimization Standard reports What happened? Where exactly is the problem? What actions are needed? Why is this happening? What’s the best that can happen? What if these trends continue? What will happen next? Analysis Access and Reporting DATA INFORMATION KNOWLEDGE INTELLIGENCE Raw
  3. 3. BasicArchitecture of OBIEE Client Presentation Services BI Server BI Scheduler Repository OO Oracle SAP Siebel Data Source Data Source
  4. 4. Functional Architecture 
  5. 5. Technical Architecture
  6. 6.  System components are still C/C++ executable and are controlled by OPMN and managed by Fusion Middleware Control  Java Components are J2EE applications and are usually installed in the managed server and controlled by Fusion Middleware Control. SYSTEM AND JAVA COMPONENTS
  7. 7. • Its adopted to start, stop and monitor processes across system components (BI Server, BI Presentation Server, BI Scheduler and BI Cluster Controller). • You can either access OPMN through the command line (opmnctl), or Oracle’s recommended approach is to use a graphical interface within Fusion Middleware Control. • OPMN is also used in the 11g stack to control Essbase, Discoverer and other BI components, so it’s a tool that’s worth learning Oracle Process Manger and Notification Server(OPMN)
  8. 8.  Manage System Components (BI Server, BI Presentation Server etc)  Start, Stop and Restart all System Components and Managed Servers  Configure Preferences and Defaults  Scale out System Components  Performance Monitoring and Diagnostics Oracle Enterprise Manager Fusion Middleware Control
  9. 9.  Users queries via the Presentation Server  The Oracle BI Server converts these queries to physical SQL/MDX, via the Oracle BI Repository  Queries are passed to the underlying physical databases and OLAP cubes  Data returned to users in the form of dashboards and reports
  10. 10. Caching oracle BI Framework
  11. 11. Caching  Web Server: Oracle Analytics’ Web Server caches queries and query results. When a user submits a query, the web server examines the logical SQL to see if it matches an existing cached query. If it does, then the Web Server uses the results without re-submitting logical SQL to the Oracle BI Server.  Database Server:  The Oracle BI Server also allows queries that require extensive database processing to be pre-scheduled to run so that results are already available when users open their dashboards.
  12. 12. OBIEE Security: Repositories and RPD File Security  It contains all the metadata, security rules, database connection information and SQL used by an OBIEE application.  The RPD file is password protected and the whole file is encrypted.  Only the Oracle BI Administration tool can create or open RPD files and BI Administration tool runs only on Windows.
  13. 13. Security  Data level security: This controls the type and amount of data that you can see in a report.  Object level security: This provides security for objects stored in the Web Catalog, such as dashboards, dashboard pages, folders, and reports. (Web object security) or subject areas  User level Security User-level security refers to authentication and confirmation of the identity of a user based on the credentials provided. Infrastructure & Management Database Middleware Applications
  14. 14. Repository (RDP) File Define OBIEE Solutions
  15. 15. .rpd file  The physical layer:  Represents the physical structure of the data sources to which the Oracle BI Server submits queries.  Represents the actual tables and columns of a database/data source. • It also contains the connection definition to that database, or data source. • join definitions including primary and foreign keys.
  16. 16. .rpd contn..  Business Model mapping:  This is where business logic is added in to the mix in the form of formulas.  The business model simplifies the physical schema and maps the users’ business vocabulary to physical sources.  Your aggregation rules are defined here as well.
  17. 17. Traversing a Request to SQL
  18. 18. Approaches to OLAP Servers Three possibilities for OLAP servers (1) Relational OLAP (ROLAP) (2) Multidimensional OLAP (MOLAP) (3) Hybrid OLAP (HOLAP)
  19. 19. ROLAP: Dimensional Modeling Using Relational DBMS  Relational and specialized relational DBMS to store and manage warehouse data/OLAP supported on top of a relational database.  Special schema design: star, snowflake  Special indexes: bitmap, multi-table join  Proven technology (relational model, DBMS), tend to outperform specialized MDDB especially on large data sets  Products  IBM DB2, Oracle, Sybase IQ, RedBrick, Informix
  20. 20. Points to be noticed about ROLAP  Defines complex, multi-dimensional data with simple model  Reduces the number of joins a query has to process  Allows the data warehouse to evolve with rel. low maintenance  Can contain both detailed and summarized data.  ROLAP is based on familiar, proven, and already selected technologies. BUT!!!  SQL for multi-dimensional manipulation of calculations.
  21. 21. MOLAP: Dimensional Modeling Using the Multi Dimensional Model  MDDB: a special-purpose data model  Specialized data structures • Multicubes vs Hypercubes  Array-based storage structures  Direct access to array data structures  Sometimes on top of relational DB  Products  Pilot, Arbor Essbase, Gentia
  22. 22. Points to be noticed about MOLAP  Pre-calculating or pre-consolidating transactional data improves speed. BUT Fully pre-consolidating incoming data, MDDs require an enormous amount of overhead both in processing time and in storage. An input file of 200MB can easily expand to 5GB MDDs are great candidates for the <50GB department data marts.  Rolling up and Drilling down through aggregate data.  With MDDs, application design is essentially the definition of dimensions and calculation rules, while the RDBMS requires that the database schema be a star or snowflake.
  23. 23. Hybrid OLAP (HOLAP)  HOLAP = Hybrid OLAP:  Best of both worlds  Storing detailed data in RDBMS to optimize time of cube processing  Storing aggregated data in MDBMS for fast query performance  User access via MOLAP tools
  24. 24.  Vertical partitioning In this mode HOLAP stores aggregations in MOLAP for fast query performance, and detailed data in ROLAP to optimize time of cube processing. • Horizontal partitioning In this mode HOLAP stores some slice of data, usually the more recent one (i.e. sliced by Time dimension) in MOLAP for fast query performance, and older data in ROLAP.
  25. 25. Multi- dimensiona l access Multidimensiona l Viewer Relational Viewer ClientMDBMS Server Multi- dimensio naldata SQL-Read RDBMS Server User data Meta data Derived data SQL- Reach Through SQL-Read Data Flow in HOLAP
  26. 26. When deciding which technology to go for, consider: 1) Performance:  How fast will the system appear to the end-user?  MDD server vendors believe this is a key point in their favor. 2) Data volume and scalability:  While MDD servers can handle up to 50GB of storage, RDBMS servers can handle hundreds of gigabytes and terabytes.
  27. 27. BI ARCHITECTURE Information Sources Data Warehouse Server (Tier 1) OLAP Servers (Tier 2) Clients (Tier 3) Operational DB’s Semistructured Sources extract transform load refresh etc. Data Warehouse e.g., MOLAP e.g., ROLAP serve OLAP Query/Reporting Data Mining serve serve
  28. 28. THANK YOU