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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

Logging based on data

As mentioned in the Monitoring our data ingest file size recipe, logging our ingest is a good practice in the data field. There are several ways to explore our ingestion logs to increase the process’s reliability and our confidence in it. In this recipe, we will start to get into the data operations field (or DataOps), where the goal is to track the behavior of data from the source until it reaches its final destination.

This recipe will explore other metrics we can track to create a reliable data pipeline.

Getting ready

For this exercise, let’s imagine we have two simple data ingests, one from a database and another from an API. Since this is a straightforward pipeline, let’s visualize it with the following diagram:

Figure 8.16 – Data ingestion phases

Figure 8.16 – Data ingestion phases

With this in mind, let’s explore the instances we can log to make monitoring efficient.

How to do it…

Let’s define...

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