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

Setting up StatsD for monitoring

As introduced in Chapter 10, StatsD is an open source daemon that gathers and aggregates metrics about application behaviors. Due to its flexibility and lightweight, StatsD is used on several monitoring and observability tools, such as Grafana, Prometheus, and ElasticSearch, to visualize and analyze the collected metrics.

In this recipe, we will configure StatsD using a Docker image as the first step in building a monitoring pipeline. Here, StatsD will collect and aggregate Airflow information and make it available to Prometheus, our monitoring database, in the Setting up Prometheus for storing metrics recipe.

Getting ready

Refer to the Technical requirements section for this recipe since we will handle it with the same technology.

How to do it…

Here are the steps to perform this recipe:

  1. Let’s start by defining our Docker configurations for StatsD. These lines will be added under the services section inside the docker...