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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

Reading CSV and JSON Files and Solving Problems

When working with data, we come across several different types of data, such as structured, semi-structured, and non-structured, and some specifics from other systems’ outputs. Yet two widespread file types are ingested, comma-separated values (CSV) and JavaScript Object Notation (JSON). There are many applications for these two files, which are widely used for data ingestion due to their versatility.

In this chapter, you will learn more about these file formats and how to ingest them using Python and PySpark, apply the best practices, and solve ingestion and transformation-related problems.

In this chapter, we will cover the following recipes:

  • Reading a CSV file
  • Reading a JSON file
  • Creating a SparkSession for PySpark
  • Using PySpark to read CSV files
  • Using PySpark to read JSON files
CONTINUE READING
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Tech Concepts
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Programming languages
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