Great question! Data observability really took off in 2021 because it became clear that reliable data is critical for driving business decisions. As data pipelines became more complex, teams needed better ways to monitor data quality, freshness, and lineage. That’s where data observability platforms came in, helping companies ensure trust in their data by making it fully observable end-to-end.

We’ve rolled out several powerful updates to help you monitor data quality more effectively. One highlight is our new referential integrity monitor, which ensures logical consistency between tables, like verifying that every order has a valid customer ID. We’ve also enhanced our Data Quality as Code framework, making it easier to scale monitor creation with templates and for-loops.

Sifflet uses AI not just for buzzwords, but to genuinely improve your workflows. From AI-powered metadata generation to dynamic thresholding and intelligent anomaly detection, Sifflet helps teams automate data quality monitoring and make faster, smarter decisions based on real-time insights.

Great question! Data volume is about how much data you're receiving, while data variety refers to the different types and formats of data sources. For example, a sudden drop in appointment data is a volume issue, while a new file format causing schema mismatches is a variety issue. Observability tools help you monitor both dimensions to maintain healthy pipelines.

Absolutely! MCP allows LLMs to remember past interactions and call diagnostic tools, which is a game-changer for data pipeline monitoring. It supports multi-turn conversations and structured tool use, making incident response faster and more contextual. This means less time spent digging through logs and more time resolving issues efficiently.

Combining data catalogs with data observability tools creates a holistic approach to managing data assets. While catalogs help users discover and understand data, observability tools ensure that data is accurate, timely, and reliable. This integration supports better decision-making, improves data reliability, and strengthens overall data governance.

Modern platforms like Snowflake and Amazon S3 expose rich metadata and access patterns that observability tools can monitor. For example, Sifflet integrates with Snowflake to track schema changes, data freshness, and query patterns, while S3 integration enables us to monitor ingestion latency and file structure changes. These capabilities are key for real-time metrics and data quality monitoring.

Sifflet centralizes documentation for all your data assets, including DAGs, models, and dashboards. This makes it easier for teams to search, explore dependencies, and maintain strong data governance practices.