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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

Configuring sensors

Under the operator’s umbrella, we have sensors. Sensors are designed to wait to execute a task until something happens. For example, a sensor triggers a pipeline (or task) when a file lands in an HDFS folder, as shown here: https://airflow.apache.org/docs/apache-airflow-providers-apache-hdfs/stable/_api/airflow/providers/apache/hdfs/sensors/hdfs/index.html. As you might be wondering, there are also sensors for specific schedules or time deltas.

Sensors are a fundamental part of creating an automated and event-driven pipeline. In this recipe, we will configure a weekday sensor, which executes our data pipeline on a specific day of the week.

Getting ready

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

Besides that, let’s put a JSON file to the following path inside the Airflow folder: files_to_test/sensors_files/.

In my case, I will use the github_events...

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