Developing Data ETL (Extract, Transform, Load) pipelines is one of the most valuable skills for Data Engineers. ETL processes involve extracting data from a source, transforming it through various processes, and loading it into a database. This README will guide you through the steps to develop a Data ETL pipeline using Python.
The Data ETL process is crucial for data management and involves three main stages:
- Extract: Gathering data from a source system.
- Transform: Processing or modifying the data to meet specific requirements.
- Load: Storing the transformed data into a database for future use.
If you are interested in learning how to develop a Data ETL pipeline, this guide will provide you with the essential steps and an example implementation in Python.
The Above Image contains the datathat we have Extracted, transformed and Loaded into SQLite3.
You can Download your final file after ETL Process and can view it on the following website 👉 : https://sqliteviewer.app/#/
In this article, you will learn how to:
- Extract data from a source.
- Transform the data through various operations.
- Load the data into a database for analysis or future use.
ETL stands for Extracting, Transforming, and Loading the data, which are the core components of the ETL process.
Make sure you have Python installed and the necessary libraries. You can install the required libraries using pip:
pip install sqlite3 tensorflow