Business Intelligence class : Lesson two

Today I will be writing about the architecture of SAP business intelligence and the differences between the way data is stored in traditional and BI systems. This is the second post in this series, you can view the first one in this business intelligence class series here

The BI system is modelled as a 3-tier architecture, the data warehouse, the BI platform and the reporting environment.

Data warehouse

The bottom and entry point is the data warehouse which is the interface to the source systems. This is where data is brought into the BI system from all the different data source type like Excel spreadsheets, Oracle databases or SAP R/3 systems.

BI platform

The middle layer is the BI platform, this is where all the data is stored and it is in a format that is suitable for analytical processing, the data is checked for integrity and successful loading.

Reporting environment

The top layer is the presentation layer, this is where all the tools are used to present meaningful information to the user. These tools are called the business explorer tools. Amongst them are the BEx query designer, BEx  Analyzer etc. These tools will be covered in a later lesson

OLAP vs. OLTP

Data in a BI system cannot be stored using traditional formats. Why? Because the traditional way of storing data in relational databases is meant for easy and fast access but not fast enough for analytical and reporting purposes. The traditional way of storing data is optimized for online transactional processing purposes (OLTP), while BI data is stored for online analytical purposes (OLAP).

What are the differences between these two?

Current vs. historical data

The data in OLTP database are up to date in real time while the data in OLAP is historical data since it is used for reporting.

High number of users vs. low traffic

Transactional databases are optimized for simultaneous multi-user access while analytical databases are optimized for large volume of data and lower number of concurrent users

Detailed vs. aggregated

The data in BI systems are aggregated because of their analytical nature. We need to compute maximum values or average values for example and do not need the detailed instantaneous values that transactional databases need.

Read-write operations vs. read only

The data in transactional database can be modified unlike in data warehouse scenarios where the data is being copied over from the source systems. BI data warehouses tend to have copies of online databases in a format optimized for analytical purposes.

Entity relationship vs. Multi-dimensional

The schema used to store transactional databases is the entity model where data is related in a top-down format. Business intelligence warehouses use the multi-dimensional star schema and SAP BI has it’s own version of the star schema which I will cover in another lesson.

That is it for lesson two in this series. The business intelligence topic is very wide and there is so much information I am discovering daily and it is really very interesting. If you like this series, please leave a comment and let me know what you think.

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