Book Image

Data Observability for Data Engineering

By : Michele Pinto, Sammy El Khammal
Book Image

Data Observability for Data Engineering

By: Michele Pinto, Sammy El Khammal

Overview of this book

In the age of information, strategic management of data is critical to organizational success. The constant challenge lies in maintaining data accuracy and preventing data pipelines from breaking. Data Observability for Data Engineering is your definitive guide to implementing data observability successfully in your organization. This book unveils the power of data observability, a fusion of techniques and methods that allow you to monitor and validate the health of your data. You’ll see how it builds on data quality monitoring and understand its significance from the data engineering perspective. Once you're familiar with the techniques and elements of data observability, you'll get hands-on with a practical Python project to reinforce what you've learned. Toward the end of the book, you’ll apply your expertise to explore diverse use cases and experiment with projects to seamlessly implement data observability in your organization. Equipped with the mastery of data observability intricacies, you’ll be able to make your organization future-ready and resilient and never worry about the quality of your data pipelines again.
Table of Contents (17 chapters)
1
Part 1: Introduction to Data Observability
4
Part 2: Implementing Data Observability
8
Part 3: How to adopt Data Observability in your organization
12
Part 4: Appendix

Challenges of implementing data observability

In this section, we will describe the common pitfalls and challenges of the implementation of data observability and how we can overcome them. The concerns we will cover are the following:

  • Costs
  • Overhead
  • Security
  • Complexity increase
  • Legacy system
  • Information overload

Let’s start with the bottom line: the costs.

Costs

Foremost among the concerns surrounding data observability are its associated costs, which can pose a significant financial burden on data projects. These expenses typically encompass the following:

  • The acquisition or development costs of a data observability solution, including the investment in research and development and the requisite team training
  • Expenses related to the storage and computation of data observations, which can also introduce overhead, as we will elaborate on later in this chapter
  • The marginal cost incurred when integrating observability into...