MOLAP Operations-The main operations in MOLAP are roll-up, drill-down, slice, dice, and pivot (or rotate).

MOLAP Operations-

In organisation lots of data are available,That all data saved in operational database. The multi-dimensional OLAP Store large data in cubicle format and with user requirement all data can extract from dimensional cube,for extract data the MOLAP use the following operations for fetching data from cubicle format data warehouse. The operations are as follows-

1.Roll-Up

2.Roll-down

3.Slice and Dice

4.pivot

1.Roll-Up-

It is the simplest process of display data to user. The role of operations represent data with hierarchical tree structure. In that information displayed as summarized format. In roll-up reduce the all size of data cube and hiding the background information with hiding the dimensions of the cube and reduction of information and only E-result is shown to user.

2.Roll-Down –

It is the opposite process of roll up follows the summary information for display to user. In roll-up the all information can hide and only result can show to user but in roll down adding the extra dimensions in to the hierarchical tree and display result as detailed format.

3.Slice and Dice-

Slice and dice break the body of information in smaller part for user shows that information in different view points. In cooking we can slice onion first in small part then rotate that onion and cut in dice so in slice and dice exactly same process is follows in that all information can slice first and then dice the information for fast process.

Slice-

In diagram shows the slicing of cube with user requirement with different dimension divide station.

Dice-

In following diagram shows that slice data is again dies into smaller part for getting a particular information to user with their requirement.

4.Pivot-

It is the operation of rotating cube in various format for dimensions for getting a better output to user. In that the cube is rotated vertically or horizontally with different dimension.

Explanation :

MOLAP (Multidimensional Online Analytical Processing) is a technology used in data warehousing that stores data in a multidimensional cube format rather than traditional relational databases. It allows for rapid analytical queries and complex calculations on pre-aggregated data. MOLAP operations enable users to explore data from different perspectives and dimensions efficiently, supporting strategic business decisions through fast and flexible analysis.

The main operations in MOLAP are roll-up, drill-down, slice, dice, and pivot (or rotate).

1. Roll-Up:
   Roll-up is a data aggregation operation where data is summarized or consolidated along a dimension hierarchy. For example, sales data can be rolled up from the “city” level to the “country” or “region” level. This operation reduces data granularity and provides higher-level summaries for strategic insights.

2. Drill-Down:
   Drill-down is the reverse of roll-up. It moves from a higher level of aggregation to a more detailed view. For instance, analyzing “yearly sales” can be drilled down to “quarterly,” “monthly,” or even “daily” sales. This operation helps in pinpointing trends and detailed analysis of specific data segments.

3. Slice:
   The slice operation selects a single dimension from the cube and examines data for that specific value. For example, slicing the cube for “2024” will display data only for that year across all other dimensions. This is similar to applying a filter to focus on a specific subset of data.

4. Dice:
   Dice operation is used to select data from multiple dimensions by applying conditions. For example, choosing sales data for “Product A” in “India” during “2024” represents a dice operation. It creates a smaller sub-cube for targeted analysis.

5. Pivot (Rotate):
   The pivot operation reorients the data cube to view data from different perspectives. It allows users to swap rows, columns, or axes to analyze data more interactively.

In summary, MOLAP operations empower users to perform multidimensional analysis efficiently. They provide a fast, intuitive, and structured approach to explore data hierarchies and relationships, helping organizations uncover patterns and make informed business decisions.

Read More-

1. What Is Data Warehouse
2. Applications of Data Warehouse, Types Of Data Warehouse
3. Architecture of Data Warehousing
4. Difference Between OLTP And OLAP
5. Python Notes