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

Technical requirements

Like Chapter 6, in this chapter too, some recipes will need SparkSession initialized, and you can use the same session for all of them. You can use the following code to create your session:

from pyspark.sql import SparkSession
spark = SparkSession.builder \
      .master("local[1]") \
      .appName("chapter7_analytical_data") \
      .config("spark.executor.memory", '3g') \
      .config("spark.executor.cores", '2') \
      .config("spark.cores.max", '2') \
      .getOrCreate()

Note

A WARN message as output is expected in some cases, especially if you are using WSL on Windows, so you don’t need to worry if you receive one.

You can also find the code from this chapter in its GitHub repository...