In Data Warehouse, when any data has to be transferred from one database to some other database, then data has to be picked up from the first database, do certain changes and operations so as to fit up in the other database, and then load to the destination database.
This whole procedure is termed the ETL process i.e. the extract, transform, and then load. It is not a component but a process of development efforts in data warehousing technologies. It is not just a process that can be done by anyone. But it needs proper ETL skills to deploy.
ETL is not a short-term process but it takes days, months and years to get done. It occurs very frequently and if at the very first time it is defined well, it runs automatically without any problem.
Once the data is put under the ETL process, then the data in the text file or in the spreadsheet becomes efficient and well-readable. The ETL process cannot be done on the data warehouse or source database but it should be done on a different kind of database server.
Since data has to be transferred periodically, so studying the time interval, the developers schedule the ETL process so that it runs automatically according to the scheduled time. If the ETL process is not automated, then the developer’s work will be increased because they have to run the ETL process, again and again, every time there is a need for data transfer.
Extraction in the ETL Process
When we extract data from different data sources, they need to be incorporated into a single system. If all the extracted data are on different systems then it is very difficult to pass on to the data warehouse.
Without ETL, extraction seems to be really complex. ETL process very simply integrates all the data coming from different data sources. There are certain rules and regulations that have to be followed while extracting data from different data sources using ETL. ETL integrates different systems and hardware in the extraction of data.
The mapping of data is very important. Making a flow chart from the source till the data reaches the data warehouse is essential. Once the flow chart is prepared, then only the full flow of the process will be cleared.
The data extraction process is split into two more phases:
• Data discovery phase
• Second anomaly detection phase
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Data Transformation in the ETL Process
Transformation is the most important phase of the ETL process. For transforming of the data, the data is checked on behave of its quality. Depending on the quality, the data is put under cleaning stage. If errors are found then the transformation is stopped. If there is no error, then the data proceeds to the next stage of ETL process that is the Load stage.
Loading in the ETL Process
Once the data is extracted, it is cleaned and then made ready to be uploaded in the data warehouse. Once cleaned, it is sent to the landing area and then send to the data warehouse.
We have to make sure that during the loading procedure we use as fewer assets as we can. We should immobilize all indexes and limitations before loading and then again enable them once it is really done.
Conclusion
ETL is an awesome process for data warehousing projects. It is a costly procedure so we need to make sure that it is done properly or else huge loss of capital is confirmed. There are various ETL tools making this process simpler like the Elixir Repertoire for Data ETL, Pervasive Data Integrator, Oracle Warehouse Builder (OWB) , IBM InfoSphere Warehouse Edition, etc.