Book Image

Data Ingestion with Python Cookbook

By : Gláucia Esppenchutz
Book Image

Data Ingestion with Python Cookbook

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)
1
Part 1: Fundamentals of Data Ingestion
9
Part 2: Structuring the Ingestion Pipeline

Scheduling historical data ingestion

Historical data is vital for data-driven decisions, providing valuable insights and supporting decision-making processes. It can also refer to data that has been accumulated over a period of time. For example, a sales company can use historical data from previous marketing campaigns to see how they have influenced the sales of a specific product over the years.

This exercise will show how to create a scheduler in Airflow to ingest historical data using the best practices and common concerns related to this process.

Getting ready

Please refer to the Technical requirements section for this recipe since we will handle it with the same technology mentioned here.

In this exercise, we will create a simple DAG inside our DAGs folder. The structure of your Airflow folder should look like the following:

Figure 11.6 – historical_data_dag folder structure in your local Airflow directory

Figure 11.6 – historical_data_dag folder structure in your local Airflow directory

How to do it…

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