Data Quality in Databricks: Validation vs Quality (DLT Expectations, DQX, Lakehouse Monitoring) скачать в хорошем качестве

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

🚀 Full Databricks Lakeflow Masterclass (32+ Episodes)    • Databricks Lakeflow Masterclass   📚 Start the course here: 1️⃣ Lakeflow Architecture    • Databricks Lakeflow Explained (2026) | Arc...   2️⃣ Lakeflow Connect    • 1️⃣ Lakeflow Connect Explained (2026) | Da...   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 ▶ Previous Episode Advanced Lakeflow Engineering | Schema Evolution & Data Quality (Section 5)    • Advanced Lakeflow Engineering | Schema Evo...   ▶ Next Episode Data Contracts in Lakeflow    • Databricks Lakeflow Data Contracts Explain...