Why Does A Data Warehouse Need An ETL (Extract, Transform, Load) Process?

The purpose of a data warehouse, which stores massive amounts of data for analysis by Business Intelligence software, is well-known.

In order to achieve this goal, DW should be loaded periodically. The information used by the system comes from several sources, including active databases, flat files, etc. The term “ETL Process” refers to the procedure that actually gets the data into DW. According to data management and analytics companies, ETL consists of the processes of Extraction, Transformation, and Loading.

1) Extraction: All desired information is culled from its original databases, programs, and flat files. Jobs can be conducted outside of normal business hours to get the data extraction done.

Successful DW system design relies heavily on the extraction of relevant data. Data from several sources may have varying qualities, but the ETL procedure can handle these variations with ease.

2) Transformation: Transformation is required since much of the data that is collected cannot be used as-is in the destination system. Before importing the data, it might undergo certain changes based on the business rules.

The data is transformed by applying a predetermined set of rules to it before being loaded into the target system. The information after extraction is called “raw data.”

All the disparate data from the various source systems is unified and made useful in the DW system using a standardized transformation procedure. The goal of data transformation is to enhance the accuracy of the data. All the rules for the logical transformation are described in the data mapping document.

If any of the source data is not conforming to the transformation rules, it is not loaded into the target DW system and instead is stored in a reject file or reject table.

To understand this, we can take an example of concatenated data from two source columns may be expected by a target column. Similarly, data transformation may include complicated logic that must be handled by trained professionals. Certain information may be transferred to the new system without any further processing.

Before the data is loaded, it is corrected, any wrong data is removed, and any problems are fixed thanks to the transformation process.

3) Load: We put all the data into the desired Data Warehouse tables. During the Load step of ETL, data that has been extracted and converted is inserted into the desired DW tables. The organization chooses the loading procedure for each table.

Methodology for Extracting Information from Data

The following are the stages that make up a typical ETL cycle:

  • Get the ETL cycle going so that tasks may be completed in order.
  • Check that all of the metadata is prepared.
  • The ETL process is useful for gathering information from several sources.
  • Verify the accuracy of the data you’ve culled.
  • If staging tables are employed, data is loaded during the ETL cycle.
  • Applying business rules, generating aggregates, etc. are all examples of transformations that ETL may execute.
  • The ETL cycle will generate reports to draw attention to any problems.
  • After that, data is loaded into the target tables using an ETL cycle.
  • Information from the past that must be kept for posterity’s sake is archived.
  • Any other unnecessary information is removed.

If you are also interested in the ETL process to grow your business then you must go for top ETL companies in India.


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