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

Filtering data and handling common issues

Filtering data is a process of excluding or selecting only the necessary information to be used or stored. Even analytical data must be re-filtered to meet a specific need. An excellent example is data marts (we will cover them later in this recipe).

This recipe aims to understand how to create and apply filters to our data using a real-world example.

Getting ready

This recipe requires SparkSession, so ensure yours is up and running. You can use the code provided at the beginning of the chapter or create your own.

The dataset used here will be the same as in the Ingesting Parquet files recipe.

To make this exercise more practical, let’s imagine we want to analyze two scenarios: how many trips each vendor made and what hour of the day there are more pickups. We will create some aggregations and filter our dataset to carry out those analyses.

How to do it…

Here are the steps to perform this recipe:

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