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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

Selecting analytical data for reverse ETL

Now that we know what reverse ETL is, the next step is to understand which types of analytical data are a good use case to load into a Salesforce application, for example.

This recipe continues from the previous one, Applying reverse ETL, intending to illustrate a real scenario of deciding what data will be transferred into a Salesforce application.

Getting ready

This recipe has no technical requirements, but you can use a whiteboard or a notepad for annotations.

Still using the example of a scenario where the marketing department requested data to be loaded into their Salesforce account, we will now go a little deeper to see what information is relevant for their analysis.

We received a request from the marketing team to understand the user journey in the e-learning platform. They want to understand which courses are watched most and whether some need improvement. Currently, they don’t know what information we have in...

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