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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

Ingesting unstructured data with a well-defined schema and format

In the previous recipe, Importing unstructured data without schema, we read a JSON file without any schema or formatting application. This led us to an odd output, which could bring confusion and require additional work later in the data pipeline. While this example pertains specifically to a JSON file, it also applies to all other NoSQL or unstructured data that needs to be converted into analytical data.

The objective is to continue the last recipe and apply a schema and standard to our data, making it more legible and easy to process in the subsequent phases of ETL.

Getting ready

This recipe has the exact same requirements as the Importing unstructured data without a schema recipe.

How to do it…

We will perform the following steps to perform this recipe:

  1. Importing data types: As usual, let’s start by importing our data types from the PySpark library:
    from pyspark.sql.types...

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