Many of you may be curious about ETL Tools and the use of the ETL process in the world of data hubs where data plays a significant role. Today, we will examine this more closely.
Many of you may be curious about *ETL Tools *and the use of the ETL process in the world of data hubs where data plays a significant role. Today, we will examine this more closely.
ETL (which stands for Extraction, _Transform _and _Load) _is the generic process of extracting data from one or more systems and loading it into a data warehouse or databases after performing some intermediate transformations.
There are many ETL tools available in the market that can carry out this process.
A standard ETL tool like PySpark, supports all basic data transformation features like sorting, mapping, joins, operations, etc. PySpark’s ability to rapidly process massive amounts of data is a key advantage.
Some tools perform a complete ETL implementation while some tools help us create a custom ETL process from scratch, and there are a few those fall somewhere in between. Before going into the detail of PySpark, let’s first understand some important features that an ETL tool should have.
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