Explore more about: OLAP Cube Designing


olap cube designing

Business intelligence is the process simplification of unstructured data and providing access of data to the organisation users for making better & right business decisions. The OLAP cube is an integral part of the BI project.

OLAP (Online Analytical Processing) cubes are used predominantly to view and analyze multiple dimensions of business data to gather insights that will help them define a business strategy. As the data grows in volume and velocity, building and managing these cubes require BI Solution.

This technique for quickly analyzing a measure, e.g. profit margin, by multiple categories or dimensions e.g. customer, region, fiscal period and product line. Typically some tool has capabilities to drag and drop in rows and columns and aggregate the measure at each intersection of a row and column. The real power of OLAP is the ability to drill down on a category to see more details.  For example, you might drill down on a state to see details by the city.

Cube stores data in an optimized way to provide quick response to queries by dimension and measure.  Most cubes pre-aggregate the measures by the different levels of categories in the dimensions to enable the quick response time.  End user software will make querying a cube very easy, in MDX (Multi-Dimensional Expressions) – query language.

The basic concepts of OLAP are:

  1. Data cubes – multi-dimensional approach to data
  2. Fast aggregation or pre-aggregation

1. Multi-dimensional databases: – data is stored in alternative manner of faster structures.

2. Pre-aggregation:– data is aggregated at different levels of granularity which means monthly, quarterly, semester and year wise data is stored.

Questions like “what was the sales in January 2017” would not require any computation, just direct fetch of the answer. The data might be pre-aggregated at all levels and combinations of their properties (dimensions).

This applications store more detailed data. They require efficiency on transaction (operation) level and integrity. For example, the data might be stored in different places, depending on how they are used by the systems. On the other hand, decision makers want to see the data at one place and in the form that reflects their view in the world. They don’t care how long it takes to store & fetch a millions of transactions, they just want to know when those millions of transactions happened.

A relational database is a standard input for this type of analysis is called a star schema.  A fact table is related to multiple dimensions and this can be represented graphically in a form of a star schema. This type of analysis will support reporting and data forecasting by dimensions for measures in the graphical formats with the help of OLAP cube.

There are three reasons for adding a cube to your solution:

  1. Performance: – Cube’s structure and pre and post aggregation allow it to provide very fast responses to queries that would have required reading, grouping and summarizing millions of rows of relational star schema data.  The drilling, slicing and dicing that an analyst perform for exploring the data would be immediate using a cube and could have took long pauses when using a relational data source.
  2. Drill down functionality: –A cube that automatically allows drilling down and up on dimensions with the input data source is an OLAP cube.
  3. Availability of software tools: –Some client software reporting tools will only use an OLAP data source for reporting.  These tools are designed for multi-dimensional analysis and use MDX query.

OLAP cube technology take more costing terms for development, learning and project time but will return benefits in fast response time to analyze large amounts of data.  This capability can result in insights that drive actions and decisions that enable very large organizational productivity, cost saving or revenue increasing gains.

You Might Need a Cube If…

  1. Queries that summarize lots of data and join several tables run too slowly. Operational systems (sometimes called Online Transaction Processing or OLTP systems) are usually designed for optimal performance inserts, updates, and lookups. They often don’t respond well to queries that aggregate data across many months or years when millions or billions of records are involved.
  2. Reporting against operational databases cause resource contention. When the database engine is grinding away at summarized reports, it responds even slowly to other requests like important business transactions to insert. Everybody has to pay for these types of queries.
  3. People spend more time in meetings trying to figure out why the numbers in their spreadsheets don’t match up than discussing whatever it was they were supposed to be discussing at the meeting. When everyone can get raw data from the OLTP system through their own ad hoc reports (usually a good thing), they may not consistently apply correct business rules to the data (which is definitely not a good thing).  You’d be surprised (or maybe not) how many different ways people at the same company can calculate the same metric.
  4. Commonly requested queries that compute growth analysis or calculate cumulative values are difficult to write. Sure, writing complex T-SQL builds character, but is that really how you want to spend your precious time.
  5. People don’t know what data they want until they see it, so you wind up cycling through a lot of iterations of reports to give them what they need.

OLAP cubes are going to solve a lot of companies’ “problems”, but knowing more about the solution will help you to develop the strongest strategy for BI data storage as well as BI data analytics process improvement. This is available for improving your business right now! By historical data analysis, it is possible to plan and forecast, define gaps and a new strategy that can help to increase sales or production of business. The business intelligence consultants can help to build low-cost analytical systems based on data warehouse and BI standards.

 

Explore more about: OLAP Cube Designing was last modified: November 13th, 2017 by Sachin Shelke

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