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An Overview of the ETL Process

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An Overview of the ETL Process

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  1. An Overview of the ETL Process  The ETL process(Extract, Transform, Load) is an optimized solution that combines data from various data sources into one consistent data store that is loaded into a data warehouse or other target repository. The ETL process is the foundation for work streams like machine learning and data analytics.

  2. ETL cleans and organizes data to address business analytics requirements like monthly reporting. However, it can also handle advanced analytics to improve backend processes thereby enhancing user experience. The complete ETL process consists of extracting data from legacy systems, cleaning that data to improve data quality, and finally loading the formatted and processed data into a target database. • Here is the ETL process in some detail.•Extract: During extraction, the raw data is exported from source locations to a staging area. This data can then be extracted from a range of sources in their native format – unstructured, semi-structured, or structured. The sources of data extraction can be among others SQL or NoSQL servers, CRM, and ERP systems, web pages, email, and flat files. 

  3. •Transform: This is the second part of the ETL process. The raw data is processed, transformed, and consolidated for analytics. After the data is cleaned, de-duplicated, validated, and authenticated, it is formatted into tables or joined tables to match the schema of the target database.

  4. •Load: In the final step, the transformed data is moved from the staging area into the target data warehouse. This involves initially loading all data, followed by loading changes and incremental data, and sometimes even carrying out full refreshes to delete or replace data in a data warehouse. The ETL process is conducted during lean hours when traffic on the source system is at its lowest.Click to know more.

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