ONLINE ANALYTICAL PROCESSING (OLAP) - V.E.S.I.T.
One of the challenges of information processing in the 1900's is how to process larger and larger databases, containing increasingly complex data, without sacrificing response time. And to ensure that data remains integrated and accessible to a wide number of users. This increased the demand for a revised approach to decision making. The key players now are emergence of data warehousing and new technology solutions including OLAP, multi dimensional databases and data mining.
"What is OLAP ?"
OLAP is primarily involved with reading and aggregating large groups of diverse data involved in complex relationships. OLAP analyses these relationships and looks out for patterns, trends and exception conditions.
An OLAP database consists of sales data by aggregated by region, product type, and sales channel. Atypical OLAP query might access a multi-gigabyte/multi-year sales database in order to find all product sales in each region for each product type. After reviewing the results, an analyst might further refine the query to find the sales volume for each sale channel within region/product classifications. As a last step the analyst might want to perform year-to-year or quarter-to-quarter comparisons for each sales channel. This whole process must be carried out online with rapid response time so that the analysts' process is undisturbed. Fast response is a crucial element in OLAP. Information in OLAP applications must be immediately available so that it can be immediately refined for further analysis.
OLAP servers
OLAP databases support common analytical operations including: consolidation, drill down and "slicing and dicing".
Consolidation
It involves the aggregation of data. These can be simple roll-ups or complex expressions
involving inter-related data. For example, sales offices can be rolled up to districts and
districts rolled-up to regions.
Drill-Down
OLAP data servers can also go in the reverse direction and display detailed data which
comprises consolidated data, which is called drill-down.
Slicing and Dicing
Slicing and dicing refers to the ability to look at the database from different
viewpoints. One slice of the sales database might show all sales of product type within
regions. Another slice might show all sales by sales channel within each product type.
Slicing and dicing is often performed along the time axis in order to analyse trends and
find patterns.
Conclusion
Thus, the technology best suited to serve the role as the analytical engine in the data
warehouse is the OLAP server.