Summary: Data warehouses have played a very important role in organizational settings in the recent times. These can be used for sophisticated enterprise intelligence systems that process queries required to discover trends and analyze critical factors in the marketplace. These systems are known as online analytical processing (OLAP) systems. OLAP systems help designers organize data in the warehouse distinctively. The data in data warehouses is organized differently than in traditional transaction processing databases.
OLAP systems are designed in an intention to handle the queries in an organization required to discover trends and critical factors. This type of queries basically requires large amounts of data. OLAP data is always organized into multidimensional cubes. In other words an OLAP structure created from the operational data is called an OLAP cube. The cube is created from a start schema of tables. In this type of schema, the fact table is placed at the center and linked to numerous dimension tables. The fact table contains the core facts, which make up the query. Dimension tables indicate how the aggregations of relational data can be analyzed.
The multidimensional cube structure of data gives better performance for OLAP queries as compared to the structure where data is organized in relational tables. The basic unit of a multidimensional cube is called a measure. Measures are the units of data that are being analyzed. Take the example of a corporation that operates hardware stores. Suppose it wants to analyze revenue and discounts for the different products it sells. In this case, the measures would be the number of units sold, revenue and the sum of any discounts. These measures are organized along dimensions. A three dimensional cube in this example would have time, store and products as the three dimensions.
Further, each dimension is divided into units called members and the members of a dimension are typically organized into a hierarchy. Similar members are then grouped together as a level of the hierarchy. For example, the top hierarchy level of a time dimension can be years, with months at the next level, then weeks, days and finally hours at the bottom level of the hierarchy. At each intersection of the three dimensions, the values for the measures that match those three dimension values are recorded.
When it comes to the specific dimensions and measures for the cubes in an OLAP system, the kinds of analysis come across as an important aspect. An OLAP system operates on OLAP data in data warehouses. The reason behind using OLAP in data warehousing is speed. OLAP systems provide rapid access to large amounts of performance data from different viewpoints in order to assist business analysts and managers throughout an enterprise.
There are three types of OLAP- Multidimensional OLAP (MOLAP), Relational OLAP (ROLAP) and Hybrid OLAP (HOLAP), each with certain benefits. MOLAP uses a summary database and creates the required schema as a dimensional set of both base data and aggregations. ROLAP utilizes relational databases. Here the base data and the dimension tables are stored as relational tables and new tables are created to hold the aggregation information. Hybrid OLAP uses relational tables to hold base data and multi-dimensional tables to hold the speculative aggregations.
This article was written by Brian May who has worked with companies that offer data warehousing design. He truly understands the value that a data warehouse consulting can offer.
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