Introduction to OLAP
OLAP is online analytical processing as the name itself indicating the OLAP is for the data analytic purpose; hence it enables us to analyze information from multiple database systems at the same time. In other words, we can tell that it is a computing method that allows users to easily extract required data and query data in order to analyze it from different points of view. It is basically based on the huge data that is called data warehouse; it collects the required data from the data warehouse and perform the business required analysis to take some decision in the business to improve in profit, to improve sale, to improve brand, to improve marketing and so all. Therefore it uses in business intelligence to queries aid in trends analysis, sales forecasting, financial reporting, planning purposes, budgeting, and so other things.
What is OLAP?
The OLAP is OLAP (Online Analytical Processing) is a powerful technology behind many Business Intelligence (BI) applications that discovers data, report viewing capabilities, complex analytical calculations, and predictive “what if” scenario, budget planning, forecast planning.
For example, a user can request that data be analyzed to display a spreadsheet showing all the movie’s release in Mumbai in the month of August, compare revenue figures with those for the same movie in December and then see a comparison of other movies to check whether achieved higher success and become a profitable or not, in the same time period. So by this analysis, will be able to take the decision that where the movie should be released and by which they get more profit and even this kind of data analysis help to plan marketing strategy like where to do marketing, how to do, through which channel to do and so on.
Now we will see how OLAP works – The data is first collected from multiple data sources (like a spreadsheet, video, XML, etc.) and stored in data warehouses, which then cleansed and organized into data cubes. The term cube is using cube because it is categorized by three dimensions which can even be categorized by multi-dimensions. So each it cube contains data categorized by some dimensions (such as customers, time period, geographic sales region and product) derived by multidimensional tables in the data warehouses. The dimensions can be populated by members or for dimensions that can take the value such as customer names, countries and months that are organized hierarchically and want to perform the analysis on the specific values. The OLAP cubes are pre-summarized on the frequent queries across dimensions which improve query execution time over relational databases. So like this, it works to facilitate a different kind of analysis within a time.
Like OLAP, the other term we are having is OLTP that is online transactional processing; both are online processing systems; the OLTP is transactional processing mainly concerned task on the transaction task while OLAP is an analytical processing system which is mainly concerned with the analysis and reporting and gives them valuable insight to improve the business.
The OLAP makes working so easy in business reporting for sales, management reporting, marketing, business process management, financial reporting, budgeting and forecasting and more.
It can be used to perform five types of analytical operations against the multidimensional databases:
- Roll-up – Also known as drill-up or consolidation, use to summarize operation data along with the dimension.
- Drill-down – To perform the analysis in deeper among the dimensions of data. For example, drilling down from “time period” to “years” and “months” and to “days” and so on to plot sales growth for a product.
- Slice – To perform the analysis to take one level of information for display, such as “sales in 2019.”
- Dice – To perform the analysis to select data from multiple dimensions to analyze, such as “sales of green apple in Bangalore in 2019.”
- Pivot – To perform the analysis that can gain a new view of data by rotating the data axes of the cube.
As the OLAP gives the cube which is of dimensions then find the intersection of dimensions, for example, all movie is profitable in Mumbai during a particular time period and displays the result. Each OLAP cube covers hundreds of measures that have at least one possible, which are actually derived from information stored in the data warehouse’s fact tables.
Architecture
As in the figure, It starts working by data collection from multiple sources and stored in a data warehouse. Further, the OLAP cubes are created on cleansed data of the warehouse, against which users can run the queries.
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There are basically three types of OLAP (Online Analysis processing):
MOLAP (Multidimensional OLAP ) – MOLAP is an OLAP for multidimensional database indexes based.
ROLAP (Relational OLAP ) – ROLAP is an OLAP that performs dynamic multidimensional analysis on a relational database stored data.
HOLAP (Hybrid OLAP ) – HOLAP is a various integration of ROLAP and MOLAP. It is used to developed ROLAP data capacity with MOLAP, the superior processing capability to fulfill the processing requirements.
Uses and Advantages
It can be used for data extracting or mining, data analysis, reporting to find the relationships between data items. To import data from an existing relational, we can use ODBC (Open Database Connectivity) to create an OLAP multidimensional database. All transactional data is not required for trend analysis, so an OLAP database does not need to be as large as a data warehouse.
Disadvantages
Some of the disadvantages of OLAP are pre-modeling, which is a must, great dependence on IT, poor computation capability, slow in reacting, short of Interactive analysis ability, abstract model, great potential risk.
Some of the analytic tools (OLAP) are IBM Cognos, Micro Strategy, Palo OLAP Server, Apache Kylin, Oracle OLAP, icCube, Pentaho BI, JsHypercube, etc.
Conclusion
- OLAP (Online Analytical Processing) is powerful technology behind many Business Intelligence (BI) applications which discovers data, report viewing capabilities, complex analytical calculations, and predictive scenario, budget planning, forecast planning.
- It works as it first collected the data from multiple data sources (like a spreadsheet, video, XML, etc.) and stored in data warehouses, which then cleansed and organized into data cubes on which can run the users queries.
- The five types of analytical operations against the multidimensional databases that can perform are Roll-up, Drill-down, Slice, Dice, and Pivot.
- There are three types of OLAP which are MOLAP, ROLAP, HOLAP.
- Some of the analytic tools (OLAP) are IBM Cognos, Micro Strategy, Palo OLAP Server, Apache Kylin, Oracle OLAP, icCube, Pentaho BI, JsHypercube, etc.
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