From Kafka to Snowflake: Detect and Debug Job and Quality Issues at Every Stage of The Data Pipeline

As data pipelines grow increasingly distributed and complex across multiple systems and teams—spanning Kafka topics, Spark jobs, and Snowflake queries—it becomes harder to know when data is delayed, missing, or just incorrect.

In this session, you’ll learn how you can proactively detect data quality issues stemming from schema changes, null values, or freshness gaps before they affect users or dashboards, and trace that back to a root cause. Gone are the days of data jobs running blind and over budget. Datadog provides deep visibility into the performance and cost of your pipelines. With Data Jobs Monitoring, you can track execution metrics and failure patterns for Airflow, Spark, and Databricks jobs, tying them to downstream data quality and spend. Data Streams Monitoring delivers real-time insight into Kafka throughput and latency, helping you pinpoint and resolve bottlenecks as they emerge.

Whether you're building analytics, AI-powered features, or automation, this session will show you how to keep your data pipelines reliable and your data trustworthy—from source to storage.