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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

Creating DAGs

The core concept of Airflow is based on DAGs, which collect, group, and organize tasks to be executed in a specific order. A DAG is also responsible for managing the dependencies between its tasks. Simply put, it is not concerned about what a task is doing but just how to execute it. Typically, a DAG starts at a scheduled time, but we can also define dependencies between other DAGs so that they will start based on their execution statuses.

We will create our first DAG in this recipe and set it to run based on a specific schedule. With this first step, we enter into practically designing our first workflow.

Getting ready

Please refer to the Getting ready section in the Configuring Airflow recipe for this recipe since we will handle it with the same technology.

Also, let’s create a directory called ids_ingest inside our dags folder. Inside the ids_ingest folder, we will create two files: __init__.py and ids_ingest_dag.py. The final structure will look...

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