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PROJECT


Build a News ETL Data Pipeline Using Python and SQLite

In this project, we’ll learn how to build an extract, transform, and load (ETL) data pipeline in Python to extract data from News API, transform it, and then load it into an SQLite database. We’ll also learn how to automate the pipeline using Airflow in Python.

Build a News ETL Data Pipeline Using Python and SQLite

You will learn to:

Create an ETL news data pipeline.

Extract data from News API.

Load the data into an SQLite database.

Automate the entire ETL pipeline using Apache Airflow.

Skills

Data Pipeline Engineering

Data Extraction

Data Manipulation

Data Cleaning

Data Engineering

Prerequisites

Intermediate knowledge of Python programming language

Understanding of data wrangling using pandas

Basic knowledge of database management

Basic knowledge of Apache Airflow

Technologies

Pandas

sqlite logo

SQLite

Project Description

Extract, transform, and load (ETL) is a process in data warehousing and data integration where data is extracted from different source systems, transformed into a more suitable format, and then loaded into a target database or data warehouse. The ETL process is a fundamental step in data integration and plays a vital role in ensuring that data is accurate, consistent, and ready for analysis.

SQLite is a lightweight, serverless, and self-contained relational database management system (RDBMS). It’s known for its simplicity and ease of use. It’s used as an embedded system in smart TVs and IoT devices. It’s also used to power web browsers like Google Chrome and Mozilla Firefox to manage and store data, such as bookmarks, history, etc.

In this hands-on project, we’ll delve into the world of data engineering by building an ETL pipeline for news data. The primary goal is to extract news data from News API, which is in a semi-structured format (JSON), transform it into a structured format, and load it into an SQLite database. Furthermore, we’ll explore the automation of this pipeline using Apache Airflow.

The final implementation of the project will transform data from an unstructured format to a structured one, as illustrated below.

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Project Tasks

1

Get Started

Task 0: Introduction

Task 1: Import Libraries and Connect to News API

Task 2: Retrieve and Print News Articles

2

Data Transformation

Task 3: Clean Author Column

Task 4: Transform News Data

3

Data Loading

Task 5: Load the Data into SQLite Database

4

Automate News ETL with Airflow

Task 6: Initialize the DAG object

Task 7: Transfer Data Using XComs

Task 8: Create DAG Operators

Task 9: Error Handling and Best Practices

Task 10: Congratulations!