Book Image

Data Ingestion with Python Cookbook

By : Gláucia Esppenchutz
Book Image

Data Ingestion with Python Cookbook

By: Gláucia Esppenchutz

Overview of this book

Data Ingestion with Python Cookbook offers a practical approach to designing and implementing data ingestion pipelines. It presents real-world examples with the most widely recognized open source tools on the market to answer commonly asked questions and overcome challenges. You’ll be introduced to designing and working with or without data schemas, as well as creating monitored pipelines with Airflow and data observability principles, all while following industry best practices. The book also addresses challenges associated with reading different data sources and data formats. As you progress through the book, you’ll gain a broader understanding of error logging best practices, troubleshooting techniques, data orchestration, monitoring, and storing logs for further consultation. By the end of the book, you’ll have a fully automated set that enables you to start ingesting and monitoring your data pipeline effortlessly, facilitating seamless integration with subsequent stages of the ETL process.
Table of Contents (17 chapters)
1
Part 1: Fundamentals of Data Ingestion
9
Part 2: Structuring the Ingestion Pipeline

Automating Your Data Ingestion Pipelines

Data sources are frequently updated, and this requires us to update our data lake. However, with multiple sources or projects, it becomes impossible to trigger data pipelines manually. Data pipeline automation makes ingesting and processing data mechanical, obviating the human actions to trigger it. The importance of automation configuration lies in the ability to streamline data flow and improve data quality, reducing errors and inconsistency.

In this chapter, we will cover how to automate the data ingestion pipelines in Airflow, along with two essential topics in data engineering, data replication and historical data ingestion, as well as best practices.

In this chapter, we will cover the following recipes:

  • Scheduling daily ingestions
  • Scheduling historical data ingestion
  • Scheduling data replication
  • Setting up the schedule_interval parameter
  • Solving scheduling errors