The above subtotal operation could be achieved using 4 вЂSELECTвЂ™ statements with UNION ALL. Then, it creates progressively higher-level subtotals, moving from right to left through the list of grouping columns. The Query calculates the standard aggregate values specified in theВ GROUPВ BYВ clause. Query Syntax: SELECT вЂ¦GROUP BY ROLLUP ( grouping_Column_reference_list) Д®xample: SELECT Time, Location, product ,sum(revenue) AS Profit FROM sales GROUP BY ROLLUP(Time, Location, product)

The Below ROLL UP operation example would return the total revenue across all products at increasing aggregation levels of location: from state to country to region for different Quarters. With some guidance, you can craft a data platform that is right for your organizationвЂ™s needs and gets the most return from your data capital. When a roll-up is performed, one or more dimensions from the data cube are removed because the output would display blank for certain rows. Example : A Query could involve aВ ROLLUPВ of year>month>day or country>state>city. climbing up a concept hierarchy for the dimensionВ such as time or geography. It creates subtotals at any level of aggregation needed, from the most detailed up to a grand total i.e. ROLLUP is used in tasks involving subtotals. The OLAP operations with the SQL queries in real time are explained below:ДЇigВ 1 вЂ“ Data cube :Graph representation Roll up (drill-up): There are three components associated with any Data cube: Measures, Dimensions and Hierarchies. The data source for all Service Manager OLAP cubes is the data marts, which includes the data marts for both the Operations Manager and Configuration Manager. OLAP cubes can display and sum large amounts of data while also providing users with searchable access to any data points so that the data can be rolled up, sliced, and diced as needed to handle the widest variety of questions that are relevant to a userвЂ™s area of interest.ДЄn OLAP cube connects to a data source to read and process raw data to perform aggregations and calculations for its associated measures. It is a Multidimensional cube that is built using OLAP databases. As event logs grow, data processing techniques need to become more efficient and highly scalable.ДЄn OLAP cube is a data structure that overcomes the limitations of relational databases by providing rapid analysis of data.

The incredible growth of event data poses new challenges. They realize the need for utilizing increasing amounts of вЂњBig DataвЂќ in order to compete with other organizations in terms of efficiency, speed and service. Organizations are usually posed with the challenge of turning data into valuable insights.

4 level data slicer