OLAP stands for Online Analytical Processing. This technology is designed to support multidimensional data analysis in a more interactive and faster way.
It is known that OLAP is an online analytical process often used in the business world, particularly in storing, manipulating, and processing multidimensional data for analytical purposes. The data processed to produce specific information undergoes several processes such as Extraction, Transformation, and Loading (ETL). OLAP functions in decision-making based on transactional data.
What is OLAP
OLAP is an acronym for Online Analytical Processing. OLAP is a technology that supports multidimensional data analysis to be more interactive and faster. OLAP allows users to perform complex data analysis efficiently, ensuring that decision-making is always based on data.
Characteristics of OLAP
OLAP has various characteristics that enable it to provide in-depth and interactive analysis. Here are some characteristics of OLAP that distinguish it from other data analysis technologies:
- Multidimensional Analysis: This allows users to view data from various perspectives. Data can be analyzed based on several dimensions, such as product, time, location, and others, providing deeper data insights.
- High Performance: It can provide quick responses to analytical queries. This is achieved using optimized data structures, such as data cubes, which allow fast access to the required information.
- Interactivity: Users can perform interactive analysis, such as roll-up (aggregating data to see summaries), drill-down (exploring data in more depth), slicing (selecting a subset of data), and dicing (cutting data for more specific analysis).
- Support for Decision Making: Sometimes top-level managers often need easily understandable reporting. OLAP provides tools and techniques that assist managers and analysts in making better decisions based on analyzed data. This is particularly useful in business planning, performance analysis, and resource management.
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How It Works
The Online Analytical Processing (OLAP) system has a structured way of working. The sequence involves collecting, organizing, aggregating, and analyzing data through the following steps:
- Data Collection: OLAP servers collect data from various data sources, including relational databases and data warehouses.
- ETL Process: The next step involves using extract, transform, and load (ETL) tools to clean, aggregate, pre-calculate, and store data in OLAP cubes. The data storage is done according to the specified number of dimensions.
- Business Analysis: The next step is to conduct business analysis using OLAP tools to measure and generate reports from multidimensional data in the OLAP cube. In the world of Big Data, you may also often hear the term OLTP. So, what is the difference between OLTP and OLAP? Both have several differences in terms of data sources, database design, users, and even the number of users.
It is known that OLAP is a system that utilizes Multidimensional Expressions (MDX) for querying OLAP cubes. MDX itself is a query language, like SQL, that provides a set of instructions for manipulating databases.
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Types of OLAP
After understanding the definition and workings above, it is now time to learn about Online Analytical Processing (OLAP) systems. This system operates in three main ways as follows:
- MOLAP: Multidimensional Online Analytical Processing (MOLAP) involves creating data cubes that represent multidimensional data from data warehouses. This system stores pre-calculated data in hypercubes, making it widely used by data engineers due to its fast analysis capabilities.
- ROLAP: ROLAP systems are often used as an alternative to data cubes because they allow data designers to perform multidimensional data analysis in relational databases. Engineers typically utilize SQL queries to obtain specific information based on the required dimensions. Although ROLAP query performance is slower compared to MOLAP, it is highly recommended for analyzing extensive and detailed data.
- HOLAP: Hybrid Online Analytical Processing (HOLAP) is a combination of MOLAP and ROLAP. Its hybrid function aims to provide the best of both architectures, allowing data engineers to act quickly, especially when retrieving analysis results from data cubes and extracting detailed information from relational databases. From the description above, it can be concluded that OLAP is a modern system that greatly assists data engineers in their work.
Sources
Melanda, D., Surahman, A., & Yulianti, T. (2023). Development of Web-Based Science Learning Media for Fourth Grade (Case Study: SDN 02 Sumberejo). Journal of Technology and Information Systems, 4(1), 28-33.
Author: Meilina Eka A
