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BI
Degree of Intelligence
Com
petit
ive
Adv
anta
ge
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
Accessand
Reporting
DATAINFORMATI
ONKNOWLEDG
EINTELLIGENC
E
Raw
BasicArchitecture of OBIEE
Client Presentation Services
BI Server
BI Scheduler
Repository
OO
Oracle
SAP
Siebel
Data Sourc
eData Source
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
• 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)
Manage System Components (BI Server, BI Presentation Server etc)
Start, Stop and Restart all System Components and Managed Servers
Configure Preferences and DefaultsScale out System ComponentsPerformance Monitoring and Diagnostics
Oracle Enterprise Manager Fusion Middleware Control
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
CachingWeb 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.
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.
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
.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.
.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.
Approaches to OLAP ServersThree possibilities for OLAP servers(1) Relational OLAP (ROLAP)(2) Multidimensional OLAP (MOLAP)(3) Hybrid OLAP (HOLAP)
ROLAP: Dimensional Modeling Using Relational DBMSRelational 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
ProductsIBM DB2, Oracle, Sybase IQ, RedBrick, Informix
Points to be noticed about ROLAPDefines complex, multi-dimensional data with
simple modelReduces the number of joins a query has to
processAllows the data warehouse to evolve with rel.
low maintenanceCan contain both detailed and summarized
data.ROLAP is based on familiar, proven, and
already selected technologies.BUT!!!SQL for multi-dimensional manipulation of
calculations.
MOLAP: Dimensional Modeling Using the Multi Dimensional ModelMDDB: a special-purpose data modelSpecialized data structures • Multicubes vs HypercubesArray-based storage structuresDirect access to array data structuresSometimes on top of relational DBProducts
Pilot, Arbor Essbase, Gentia
Points to be noticed about MOLAP
Pre-calculating or pre-consolidating transactional data improves speed.
BUTFully 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.
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
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.
Multi-dimensional access Multidimensio
nal Viewer
RelationalViewer
ClientMDBMS Server
Multi-dimensionaldata
SQL-Read
RDBMS Server
Userdata Meta data
Deriveddata
SQL-Reach
Through
SQL-Read
Data Flow in HOLAP
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.
BI ARCHITECTURE
Information Sources Data Warehouse Server(Tier 1)
OLAP Servers(Tier 2)
Clients(Tier 3)
OperationalDB’s
SemistructuredSources
extracttransformloadrefreshetc.
DataWarehouse
e.g., MOLAP
e.g., ROLAP
serve
OLAP
Query/Reporting
Data Mining
serve
serve
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