Data Quality Fundamentals: A Practitioner's Guide to Building Trustworthy Data Pipelines

The book "Data Quality and Trust at Scale" discusses how to tackle data quality and trust issues in data engineering teams by leveraging best practices and technologies. It covers topics such as building trustworthy and reliable data pipelines, writing scripts for data checks, developing data quality monitors, and automating data lineage graphs.

Format: Paperback / softback
Length: 300 pages
Publication date: 30 September 2022
Publisher: O'Reilly Media

Are your product dashboards looking funky? Are your quarterly reports stale? Is the dataset you're using broken or just plain wrong? These problems affect almost every team, yet they're usually addressed on an ad hoc basis and in a reactive manner. If you answered yes to any of the questions above, this book is for you.

Many data engineering teams today face the good pipelines, bad data problem. It doesn't matter how advanced your data infrastructure is if the data you're piping is bad. In this book, Barr Moses, Lior Gavish, and Molly Vorwerck from the data reliability company Monte Carlo explain how to tackle data quality and trust at scale by leveraging best practices and technologies used by some of the world's most innovative companies.

Build more trustworthy and reliable data pipelines.

Write scripts to make data checks and identify broken pipelines with data observability.

Program your own data quality monitors from scratch.

Develop and lead data quality initiatives at your company.

Generate a dashboard to highlight your company's key data assets.

Automate data lineage graphs across your data ecosystem.

Build anomaly detectors for your critical data assets.

Weight: 552g
Dimension: 178 x 232 x 20 (mm)
ISBN-13: 9781098112042