What is Data Observability?
Databricks offers powerful capabilities for data engineering and AI—but without visibility into pipeline health and data quality, even the best workflows can silently fail. That’s where data observability comes in.
This practical eBook breaks down what observability means in the context of Databricks and why it’s essential for ensuring trusted, high-quality data. You’ll learn:
Core Observability Metrics
Discover the five key pillars to monitor—freshness, volume, distribution, schema, and lineage.
Databricks-Specific Challenges
Understand how complex Spark jobs, schema drift, and inconsistent quality practices impact reliability.
Step-by-Step Implementation
Follow a clear roadmap for embedding agentic AI-powered data observability into your Databricks environment.
How DQLabs
Helps
DQLabs integrates seamlessly with Databricks, enabling automated monitoring, AI-driven alerts, and visual lineage.
Comprehensive Data Observability for Databricks
Whether you're managing critical analytics pipelines or ML workflows, this guide will help you proactively detect issues, reduce downtime, and improve trust in your Databricks data.
Download the eBook
