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Data Ingestion with Python Cookbook

Data Ingestion with Python Cookbook

By : Gláucia Esppenchutz
4.5 (4)
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Data Ingestion with Python Cookbook

Data Ingestion with Python Cookbook

4.5 (4)
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)
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1
Part 1: Fundamentals of Data Ingestion
9
Part 2: Structuring the Ingestion Pipeline

Logging and Monitoring Your Data Ingest in Airflow

We already know how vital logging and monitoring are to manage applications and systems, and Airflow is no different. In fact, Apache Airflow already has built-in modules to create logs and export them. But what about improving them?

In the previous chapter, Putting Everything Together with Airflow, we covered the fundamental aspects of Airflow, how to start our data ingestion, and how to orchestrate a pipeline and use the best data development practices. Now, let’s put into practice the best techniques to enhance logging and monitor Airflow pipelines.

In this chapter, you will learn the following recipes:

  • Creating basic logs in Airflow
  • Storing log files in a remote location
  • Configuring logs in airflow.cfg
  • Designing advanced monitoring
  • Using notification operators
  • Using SQL operators for data quality
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