ETL stand for extract, transform and load. ETL is a strategy with which database functions are collectively used to fetch the data. With ETL, collection and transfer of the data are a lot easier. ETL model is a concept that provides reliability with a realistic approach. The database is like a lifeline that is to be protected and secured at any cost. Failing to keep the database intact can turn out to be a disaster.

In that case, ETL is a sophisticated program that can transfer the data from one database to another. In ETL format, the data is fetched from multiple sources. This data is then downloaded to a data warehouse. Data warehouse is a place where the data is consolidated and complied. ETL is a technique that can change the format of the data in data warehouse. Once the data is compiled, it is then transferred to the actual database.

ETL is a continuous phase. First step of ETL is extraction. As the name suggest, the data is extracted using multiple tools and techniques. The second step is the transformation of the data. There are set of rules defined for the extraction process. As per the requirement, there are multiple parameters used in order to shape up the data. There are lookup tables predefined for the extraction process. Last step of ETL is the loading process. The target of the loading process is to make sure that data is transferred to the required location in the desired format.

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    A>Airflow 1. Take any small source CSV file and copy it into target S3 using Airflow 2. Take any CSV in S3 and copy that into target local server using airflow 3. On local server, airflow calls a simple python file , that reads say a json and pushes data into output say CSV schedule above one after anotther to run every ten minutes ( since the files are small, we can run them back to back)...

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