OBIEE ARCHITECTURE.ppt

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

BasicArchitecture of OBIEE
Client Presentation
Services
BI Server
BI Scheduler
Repository
OO
Oracle
SAP
Siebel
Data
Source
Data 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 Defaults
 Scale out System Components
 Performance 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

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.

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

Repository (RDP) File Define OBIEE
Solutions

.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 Servers
Three possibilities for OLAP servers
(1) Relational OLAP (ROLAP)
(2) Multidimensional OLAP (MOLAP)
(3) Hybrid OLAP (HOLAP)

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

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.

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

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.

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-
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

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)
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

OBIEE ARCHITECTURE.ppt

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OBIEE ARCHITECTURE.ppt