ETL; Extraction, Transformation, and Loading is a scheduled data integration process. The major activities that take place during this process include the extraction of data from either data sources or from an operational source where the data is then subjected to a transformation process to an appropriate format. Loading comes at the end of the entire process where data is then placed or stored in the relevant data repositories. ETL is all about the physical movement of data from a specific source to the final data repository. It basically entails three crucial steps which make up the entire process; extraction, transformation, and loading.
Steps of the ETL Process
Extraction is a critical step in the ETL process. It is the first step which is concerned with the connection of the source systems so that all the necessary data can be collected and subjected to analytical processes in the target data warehouse. The second step, Transformation encompasses an execution process guided by stipulated rules or functions. The subject of this process is the data obtained during extraction. During this step, data is converted into a standardized format. The last step has a direct connection to the data warehouse. The processed data from the previous step, transformation, is loaded to the target data warehouse or a database.
Categories of ETL Technologies
There are four categories of ETL technologies (Dembczynski, 2015). The first category is batch/bulk, which acts as a support mechanism to aid the ETL process through consolidating data from any primary database. The second category is data virtualization which grants users the opportunity to piece up databases or data warehouses through a created virtual abstract layer. The third category is the message-oriented movement which allows the grouping of data into messages thus allowing the application to read the data which can then be exchanged in real time. The last category is data replication which works through data synchronization to allow users to share the same level of information from one database to another.
Importance of ETL Process
The ETL process enhances business intelligence. The insight which is gained from data warehousing guarantees improved access to information. Those in managerial positions, especially from the business domain, do not have to make decisions based on inconclusive information. ETL process comes in handy to aid in making the right decisions in real time which would eventually be in line with the set strategies of the business (Kimball et al., 2015). Additionally, business managers get the chance to be well informed about crucial data queries aligned to specific needs.
Another importance that comes with the ETL process is timely access to data. Data warehousing and the ETL process combined ensure that any user or those in leadership positions can access data from various sources at any given time. ETL process guarantees access to processed data in a very short time considering its ability to consolidate data regardless of the source then transforming it into a useful format.
The ETL process also boasts of enhancing consistency and quality of data. The fact that it handles data from different sources ensures that they can all be transformed into common formats. A good scenario is that of a business company which deals with departments and various business processes. Standardized processes are enforced through the ETL so that the same data can be used across departments to run different operations. Basically, the ETL process produces results that have been aligned to suit the needs of specific departments.
ETL technologies have been adopted across the globe leading to the emergence of a wide variety. Examples of ETL technologies include IBM Infosphere Information Server, PowerCenter Informatica, SAP Data Services among others. ETL if implemented in data warehousing triggers numerous benefits.
References
Dembczynski, K. (2015). Data Integration and ETL Process.
Kimball, R., Ross, M., Becker, B., Mundy, J., & Thornthwaite, W. (2015). The kimball group reader: Relentlessly practical tools for data warehousing and business intelligence remastered collection. John Wiley & Sons.