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

Ingesting Analytical Data

Analytical data is a bundle of data that serves various areas (such as finances, marketing, and sales) in a company, university, or any other institution, to facilitate decision-making, especially for strategic matters. When transposing analytical data to a data pipeline or a usual Extract, Transform, and Load (ETL) process, it corresponds to the final step, where data is already ingested, cleaned, aggregated, and has other transformations accordingly to business rules.

There are plenty of scenarios where data engineers must retrieve data from a data warehouse or any other storage containing analytical data. The objective of this chapter is to learn how to read analytical data and its standard formats and cover practical use cases related to the reverse ETL concept.

In this chapter, we will learn about the following topics:

  • Ingesting Parquet files
  • Ingesting Avro files
  • Applying schemas to analytical data
  • Filtering data and handling...