In analyzing Big Data, you need to know the overview of the OLAP process to produce information and how the OLAP work in Data Analysis
Understanding OLAP
When it comes to data analysis, we are likely familiar with OLAP technology. Online Analytical Processing or OLAP is a Multidimensional Analysis or Online Analytical Processing technology that manages large-scale business databases and supports complex analytics. OLAP is used to perform complex analytics queries without compromising the performance of transactional systems. The OLAP system is designed to help extract business intelligence information from data at high performance. To learn more, read about the OLAP process in the following post!
What is Business Intelligence?
Before we delve into the main discussion, it is important to first understand the field of business intelligence. Do you know what Business Intelligence is?
Business Intelligence is the application and technology used to consolidate, analyze, and provide access to a large amount of data to help users make better business and strategic decisions. Business Intelligence is related to Big Data in its process. There are many uses for Business Intelligence.
Difference Between Business Intelligence and OLAP
BI (Business Intelligence) and OLAP (Online Analytical Processing) are two related concepts used together in the processing and analysis of business data.
OLAP is a data processing technology focused on analyzing more complex business data. In OLAP, data is stored in a multidimensional cube that allows users to view data in various dimensions, such as time, product, region, and others. Users can manipulate and analyze data in various ways, including creating reports, drilling down or rolling up data, and creating pivot tables.
Meanwhile, Business Intelligence is a broader concept that encompasses the entire process of collecting, integrating, analyzing, and presenting relevant business information to support better business decision making. OLAP is one of the technologies in Business Intelligence along with other technologies such as data warehousing, data mining, data visualization, and predictive analytics.
In Business Intelligence, OLAP is used as one of the main technologies for analyzing business data. OLAP allows users to access and analyze data quickly and easily, enabling users to make better and faster business decisions with multidimensional data processing.
Overview of the OLAP Process
The OLAP (Online Analytical Processing) process is a data processing process used to perform real-time business data analysis. Here are some stages of the OLAP process:
1. Data Warehouse
A Data Warehouse is a database that contains large amounts of business data collected from various sources. The Data Warehouse is designed to support business data analysis by storing data in a structured and organized manner.
Besides Data Warehouse, you may also have heard of Data Lake and Data Mart, right? The difference between a data warehouse, data mart, and data lake lies in the data source, storage structure, and usage purpose.
A Data Warehouse is usually collected from various different sources. The Data Warehouse can be integrated, updated, and stored in a structured form to be used for business analysis. Data warehouses are usually used by large companies that have many data sources and require complex and in-depth data analysis. Examples of data warehouses include:
- Walmart’s data warehouse: Stores sales data from all their stores worldwide. Amazon’s data warehouse: Stores transaction and customer activity data from all their platforms.
- Amazon Data Warehouse: Stores transactional and customer activity data from all their platforms
Data Mart
Data Mart is a part of the data warehouse that is created to meet the business analysis needs of a specific department or division within a company. Data Mart can be more specific and focused on the needs of a particular department or division within a company, such as sales analysis, financial analysis, or production analysis.
Examples of data marts include:
- Marketing Department Data Mart: Stores sales and customer behavior data for marketing analysis and promotional campaigns.
- Finance Department Data Mart: Stores financial transaction and expenditure policy data for financial and accounting analysis.
Data Lake
Data lake is a data storage system that contains raw data or unprocessed data from various sources, such as file storage systems, databases, and others. Data in the data lake is not integrated or stored in a structured form like a data warehouse or data mart, but is stored in raw form.
Examples of data lakes include:
- Facebook Data Lake: Stores all data generated by users on the Facebook platform, including posts, messages, photos, and other activities.
- Google Data Lake: Stores all data generated by users on the Google platform, including searches, emails, and other activities.
2. Data Sources
Data in the data warehouse comes from various business data sources, such as sales systems, procurement systems, inventory systems, and others. Data from these sources is then integrated into the data warehouse so that it can be used for data analysis.
3. Semantic Layer
The semantic layer is a layer that allows users to access the data warehouse and perform data analysis using a more easily understood language. The semantic layer functions as a data filter, so that users can only view and access data that is relevant to their business analysis needs.
4. Analysis
After the data is processed and available in the semantic layer, users can perform business data analysis using techniques such as drill-down, roll-up, pivot table, and others.
5. Reporting
After performing data analysis, users can create reports that display the results of the analysis clearly and easily understood. These reports can be created in various forms, such as tables, charts, and dashboards. To process a data report, you can use several tools such as Microsoft Excel, Tableau, Power BI, Google Data Studio, SAP Data Analytics, and others.
Conclusion
Throughout the OLAP process, data is extracted from different sources and integrated into the data warehouse. This data is then presented to users in a semantic layer that can be used for data analysis. After conducting the analysis, users can create reports to present the results of their analysis in an easily understandable format. That’s all, hope it’s useful.
Writer : Meilina Eka A
