Transcending Data Warehouses via SAP HANA (Part 1)

This is the first in a series of blogs explaining the evolution of analytics based on traditional data warehouses to the modern day, in-memory computing platform. My focus in the following succession of compositions is on SAP HANA as the fuel for driving business intelligence and enabling accelerated optimization of business processes. This blog provides an overview of how the need for data warehousing developed and evolved into in-memory analytics.
The original concept of data warehousing, based on a three-tier architecture, was intended to provide a model for the flow of data from operational systems such as sales, finance and manufacturing to decision support systems. Typically, data warehouses were based on Extract, Transform, Load (ETL) concept that used staging, data integration, and access layers to house its key functions.
OnLine Analytical Processing (OLAP) requirements optimized for point in time
and historical analysis reporting demanded high volume data aggregation and redundancy. Moreover, OLAP led to formation of complex data warehousing models and staging scenarios. For customers, this often culminated into high cost maintenance ordeals.
As data volumes grew exponentially and information categories expanded to massive generation of unstructured data, disk based database management systems operated by SQL became inadequate for meeting Business Intelligence (BI) needs. Growing demands of high performance analytics and architectural simplification necessitated development of more innovative technologies, hence
the birth of in-memory processing platforms such as SAP HANA.
SAP HANA is a column oriented, in-memory database developed by SAP SE with capabilities of combining operational and analytical data processing into a single system. It achieves significantly faster querying by storing data in the main memory (RAM) rather than on disk. Additionally, HANA provides an application development environment to create information models exposing various types of data to end users through SAP Analytics suite of products.
HANA database infuses greater flexibility into the traditional data warehouse
architecture by relying heavily on virtualization rather than saving data anew.
Such versatility is introduced by higher inmemory processing speeds, pushing
business logic down to the database level, and hence simplifying maintenance.
The next part of this series will provide details on SAP HANA features as well as the
roadmap for simplification via BW/4HANA.
Farhan Bhaba SAP BI/Analytics Consultant @ Yash Technologies

• Data Warehouses
• In-Memory Analytics
• SAP
• SAP HANA