Absolutely! In fact, data monitoring is often a key feature within a broader data observability solution. At Sifflet, we combine traditional monitoring with advanced capabilities like data profiling, pipeline health dashboards, and data drift detection so you get both alerts and insights in one place.

You’ll want a solution that goes beyond basic checks like null values and schema validation. The best data quality monitoring tools use intelligent anomaly detection, dynamic thresholding, and auto-generated rules based on data profiling. They adapt as your data evolves and scale effortlessly across thousands of tables. This way, your team can confidently trust the data without spending hours writing manual validation rules.

When data distribution shifts unexpectedly, it can throw off the assumptions your ML models are trained on. For example, if a new payment processor causes 70% of transactions to fall under $5, a fraud detection model might start flagging legitimate behavior as suspicious. That's why real-time metrics and anomaly detection are so crucial for ML model monitoring within a good data observability framework.

A user-friendly interface boosts adoption across teams and makes it easier to navigate complex datasets. For observability tools, especially those focused on data cataloging and data discovery, a clean UI enables faster insights and more efficient collaboration.

Sifflet is ISO 27001 certified and SOC 2 compliant, and we use a separate secret manager to handle credentials securely. This setup ensures a strong audit trail and tight access control, making compliance monitoring and audit logging seamless for your data teams.

It's a mix of both, but culture plays a big role. As Dan Power puts it, 'culture eats strategy for breakfast.' Even the best observability tools won't succeed without enterprise-wide data literacy and buy-in. That’s why training, user-friendly platforms, and fostering collaboration are just as important as the technology stack you choose.

Data lineage tracking is essential for understanding how data flows from storage to dashboards. When something breaks, Sifflet helps you trace it back to the storage layer, whether it's a corrupted file in S3 or a schema drift in MongoDB. This visibility is critical for root cause analysis and ensuring data reliability across your pipelines.

While a data catalog helps you find and understand your data, data observability ensures that the data you find is actually reliable. Observability tools like Sifflet monitor the health of your data pipelines in real time, using features like data freshness checks, anomaly detection, and data quality monitoring. Together, they give you both visibility and trust in your data.