3 Stages of the ETL Process
In the context of Data Warehouse and Business Intelligence comes the process of ETL (extract, transform, load) that passes through the following three important steps for the success and transition of data from the source systems to the Data Warehouse:
Data Collection
This step can be understood as the phase in which the data is extracted from the source systems and transported to a secondary area where it is converted into a single format.
Its conversion is fundamental considering the heterogeneity of the information extracted from the source systems. Therefore, it is fundamental to do a previous structuring for later, the adequate treatment to be done.
Processing and Analysis
Once the data has been collected, it is transformed and 'cleaned'. In this phase the data is corrected, standardized, and treated according to the needs of each business.
Data mapping is a part of the transformation process that provides detailed instructions to an application on how to obtain the data needed to be processed.
The loading step follows the transformation. Once all the necessary data processing is done, the loading of the Data Warehouse begins - it contains four key properties to support decision-making that define the DW as a data set:
- Organized by subject, providing a simpler view on a given subject and allowing a better analysis;
- Integrated, in order to solve the conflicts and inconsistencies of the various data formats;
- Time-structured, allowing for the detection of long-term patterns and relationships;
- Non-volatile, in the sense that records may undergo changes or updates after being entered.
Visualization and Monitoring
This step is a normal challenge for businesses because it allows the various intervenients to monitor the evolution of results according to the defined goals and objectives.
A solution in this sense is the creation of dashboards - panels that present metrics and visual indicators for achieving goals and objectives that facilitate the understanding of the information obtained and allow the monitoring of results on a daily, weekly or real-time basis.
Large amounts of data have a lot of potential but they don't mean much if you can't operationalize their use because it is necessary to use complete and dynamic integration systems that are able to generate, group, cross-reference and compare raw data from various sources, transforming them into relevant information for businesses in order to facilitate management, information search and decision-making.