Data Quality in Databricks: Validation vs Quality (DLT Expectations, DQX, Lakehouse Monitoring)

Описание: 🚀 Full Databricks Lakeflow Masterclass (32+ Episodes)

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In modern data platforms, data quality is not just about rules — it’s about trust.

In this video, I explain the fundamental difference between Data Validation and Data Quality in Databricks, and why both are essential for building reliable Lakehouse data pipelines.

Many teams confuse validation with quality.
But they solve very different problems.

Validation protects pipelines.
Data Quality protects decisions.

In this session we cover:

• Data Validation vs Data Quality explained
• Why validation alone is not enough
• Databricks-native data quality tools
• When to use DLT Expectations
• When to use DQX
• What Lakehouse Monitoring actually does
• Comparison with Great Expectations and Deequ
• A recommended modern Lakehouse data quality architecture

The video also explains a practical pattern for implementing data quality in Databricks Lakehouse platforms, combining validation, observability, and monitoring.

Recommended architecture covered in this video:

Source
→ DLT Expectations (Validation)
→ Bronze / Silver
→ DQX (Quality Rules)
→ Gold
→ Lakehouse Monitoring (Observability)

This approach enables AI-ready data platforms that are reliable, scalable, and trustworthy.

About the channel

DataMindAI with Ahmed
Principal Data Engineer | AI Data Platforms | Lakehouse Architecture

This channel focuses on:

• Data Engineering
• Databricks Lakehouse
• AI-ready Data Platforms
• Data Governance & Quality
• Enterprise Data Architecture

Chapters

00:00 Introduction
01:00 Data Validation vs Data Quality
03:00 Why most teams misunderstand data quality
05:00 Databricks Data Quality Tools
07:00 DLT Expectations
09:00 DQX
11:00 Lakehouse Monitoring
13:00 Framework Comparison
16:00 Recommended Architecture
18:00 Final Takeaway

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