This ensures that the BI reports and dashboards display the most current information, enabling agile and responsive decision-making. ETL can be designed to accommodate real-time data streaming, where data is extracted, transformed, and loaded into the BI system continuously or at regular intervals.

Real-time Data Streaming: In certain BI scenarios, real-time data is essential for timely decision-making.

ETL allows the loading of historical data into data warehouses or data marts, enabling BI tools to access this information for retrospective analysis and trend identification.

Historical Data Loading: BI often requires analyzing historical data to identify trends, patterns, and long-term performance indicators.

By ensuring data quality, ETL helps maintain the integrity of BI reports and dashboards, leading to more trustworthy insights and analysis. ETL processes include data cleansing and validation steps, where data inconsistencies, errors, and duplicates are identified and rectified.

Data Cleansing and Validation: BI heavily relies on accurate and reliable data.

Data integration ensures that all relevant information is gathered in a central data repository, allowing business analysts and decision-makers to access a comprehensive and unified view of the organization’s data.

Data Integration: ETL serves as the backbone of BI by integrating data from various sources, including databases, spreadsheets, applications, and cloud services.

Below are some key applications of ETL in Business Intelligence: ETL plays a pivotal role in enabling effective BI by providing the necessary data integration, transformation, and loading capabilities. How is ETL used in business intelligence applications?īusiness Intelligence (BI) is the process of transforming raw data into meaningful insights to support strategic decision-making within an organization. In this way, the data can be compared and the obsolete information can be specifically deleted. This is often done using a unique ID or by entering the time at which the information was saved. If there is already older information of the same type there, this must be supplemented accordingly or even exchanged. The data we prepared in the previous steps can now be loaded into the data warehouse or the target database. This could include, for example, that the turnover is already aggregated on a daily basis and not each order is saved individually if this is required. In addition, basic calculations are already made here with which the data can be summarized or prepared.

This includes, for example, filling in missing values or correcting errors. If there are still data quality problems, these are now processed. In this stage, all data is transformed into a structure that matches the data model of the data warehouse or the application. The Extract step involves loading information from a wide variety of sources.

If there are no or only a few deficiencies, the data is passed to the next stage, where the necessary changes are made.

If the data has gross quality defects, it can also be rejected at this stage.

For example, checks could be made to see if all items that represent a price are also marked in USD. These checks can include, for example, matching the data type or looking for missing values. In this step, among other things, data quality checks are performed to ensure a clean state in the data warehouse. To better understand the Extract, Transform, Load process, it is worthwhile to look at the individual phases in detail: ETL ExtractĮxtraction is the process step in which data is retrieved from various sources and stored centrally. Extract, Transform, Load Process | Source: Author What are the ETL Process Steps?

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