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OLAP - ONLINE ANALYTICAL  PROCESSING

SAP BW enables Online Analytical Processing (OLAP) for the staging of information from large amounts of operative and historical data. OLAP technology permits multi-dimensional analyses according to various business perspectives.

At the core of any OLAP system is the concept of an OLAP cube (also called a 'multidimensional cube' or a hypercube). It consists of numeric facts called measures which are categorized by   dimesnions .The cube metadata is typically created from a star schema or snowflake schema of tables  in a relational database.

The OLAP Area can be divided into three components : 

1. BEx Analyzer

2. BEx Web Application

3. BEx Mobile Intelligence

  OLTP - ONLINE TRANSACTION PROCESSING

Online transaction processing, or OLTP, refers to a class of systems that facilitate and manage transaction-oriented applications, typically for data entry and retrieval  transaction processing . OLTP has also been used to refer to processing in which the system responds immediately to user requests .The main emphasis for OLTP systems is put on very fast query processing, maintaining data integrity in multi-access environments and an effectiveness measured by number of transactions per second. In OLTP database there is detailed and current data, and schema used to store transactional databases is the entity model (usually 3NF).

In general we can say that OLTP provides source data to data warehouses and the OLAP is used to analyze it .So OLTP is also referred as Operative Environment and OLAP as Informative Environment.

 DIFFERENCES BETWEEN OLAP and OLTP


 

OLTP System

OLAP System

  Source of data

Operational data; OLTPs are the original source of the data.

Consolidation data; OLAP data comes from the
various OLTP Databases

 Purpose of data

To control and run fundamental business tasks

To help with planning, problem solving, and
decision support

Processing Speed

Typicall Very Fast

Depends on the amount of data involved; batch
data refreshes and complex queries may take
many hours; query speed can be improved by
creating indexes

Database Design

Highly normalized with many tables

Typically de-normalized with fewer tables; use of
star and/or snowflake schemas.

Backup and Recovery

Backup religiously; operational data is critical to run the business,
data loss is likely to entail
significant monetary loss and legal liability

Instead of regular backups, some environments
may consider simply reloading the OLTP data as a
recovery method

Age Of Data

Current

Historical

Queries

Relatively standardized and simple queries
Returning relatively few records

Often complex queries involving aggregations

Data Base Operations

Add , Modify , Delete , Update and Read

Read

What the data Reveals

A snapshot of ongoing business processes

Multi-dimensional views of various kinds of
business activities

Data Set

6 - 18 months

2 - 7 years