Data Quality
Data Quality is the discipline of ensuring that data is accurate, complete, consistent, timely, and fit for its intended purpose. It encompasses processes, tools, and frameworks for profiling, validating, monitoring, and remediating data across pipelines, warehouses, and ML systems. Practitioners work at the intersection of data engineering, governance, and analytics to prevent bad data from corrupting dashboards, models, and business decisions.
As AI adoption accelerates in 2026, the quality of training and inference data has become a first-order concern: flawed data produces flawed models regardless of architecture sophistication. Companies hiring for ML engineering, analytics engineering, and data platform roles increasingly expect candidates to own data quality end-to-end — from writing dbt tests and Great Expectations suites to designing automated monitoring pipelines. Regulatory pressure around AI transparency (EU AI Act, GDPR) also requires auditability of data provenance and quality gates before high-risk AI systems go live.
🎓 Courses
Data Quality: Analytics and Serving
by Mark Freeman
Hands-on course in a GitHub Codespaces sandbox covering root cause analysis, chaos engineering for data pipelines, SQL-based quality checks, and dbt tests. Practical focus makes it ideal for working data engineers.
Data Quality Masterclass — The Complete Course
Covers the full spectrum from DQ dimensions and rules to governance frameworks, AI-based quality methods, and industry tooling. Good starting point for those new to the domain.
CDO and Data Quality Accelerator: Strategy to Implementation
Updated in 2024, this course connects data quality management to enterprise data strategy, data ownership, stewardship, and the Chief Data Office structure — essential context for practitioners in larger organisations.
DeepLearning.AI Data Engineering Professional Certificate
by Joe Reis
Four-course certificate by Joe Reis (co-author of Fundamentals of Data Engineering) covering data quality monitoring with AWS and open-source tools, batch and streaming pipelines, and orchestration. Directly applicable to production data quality work.
GX Core + dbt Integration Tutorial
Official hands-on tutorial combining PostgreSQL, dbt, Great Expectations, and Airflow in Docker Compose. Teaches the open-source toolchain most commonly used for data quality in modern data stacks.
📖 Books
Automating Data Quality Monitoring: Scaling Beyond Rules with Machine Learning
Jeremy Stanley, Paige Schwartz · 2024
Published by O'Reilly in February 2024, this practical book explains why rules-based testing fails at scale and shows how to apply ML to detect data anomalies automatically. Preface by former US Chief Data Scientist DJ Patil. Directly relevant to ML-era data stacks.
Data Quality: Empowering Businesses with Analytics and AI
Prashanth Southekal · 2023
Wiley, 2023. Structured around the D-A-R-S (Define–Assess–Realize–Sustain) lifecycle, this book gives a practitioner's framework for embedding data quality into analytics and AI programs. The author has consulted for 80+ organisations including Apple, GE, and SAP.
Data Quality Management in the Data Age
Editors: Springer Nature (multiple contributors) · 2024
Springer, October 2024. Covers data quality for data markets and modern data science systems, including challenges from big data and ML contexts. Useful for readers who want academic rigour alongside practical coverage.
🛠️ Tutorials & Guides
Implement dbt data quality checks with dbt-expectations
Step-by-step guide to using dbt-expectations (the Great Expectations port for dbt) to add rich assertions — regex checks, column pair comparisons, distribution checks — directly into dbt YAML model definitions.
Data Quality with Great Expectations — Astrafy
Practical walkthrough of setting up Great Expectations in a cloud data stack, defining Expectation Suites, and integrating validation into orchestrated pipelines. Clear entry point for GX beginners.
GX Core Open Source Platform Documentation
The official home of GX Core (Apache 2.0), the most widely used open-source data quality framework. Documentation covers Expectations, Checkpoints, Actions, and integrations with Spark, Pandas, and SQL backends.
🏅 Certifications
DeepLearning.AI Data Engineering Professional Certificate
DeepLearning.AI / Coursera · Included in Coursera Plus (~$49/month) or ~$49/month standalone
While broader than pure DQ, this certificate explicitly covers data quality monitoring tools and practices in production AWS environments, and carries recognisable brand weight with hiring managers.
Learning resources last updated: June 18, 2026