Absolutely! Sifflet is designed to integrate seamlessly with your current stack. We support a wide range of tools including Airflow, Snowflake, AWS Glue, and more. Our goal is to provide complete pipeline orchestration visibility and data freshness checks, all from one intuitive interface.

We’re just getting started! Soon, you’ll be able to monitor dbt run performance and resource utilization, define monitors in your dbt YAML files, and use custom metadata even more dynamically. These updates will further enhance your cloud data observability and make your workflows even more efficient.

Sifflet offers detailed data lineage tracking at both the table and field level. You can easily trace data upstream and downstream, which helps avoid unexpected issues when making changes. This transparency is key for data governance and ensuring trust in your analytics pipeline.

ETL (Extract, Transform, Load) transforms data before storing it, while ELT (Extract, Load, Transform) loads raw data first, then transforms it. With modern cloud storage, ELT is often preferred for its flexibility and scalability. Whichever method you choose, pairing it with strong data pipeline monitoring ensures smooth operations.

Great question! Data distribution gives you insight into the shape and spread of your data values, which traditional monitoring tools often miss. While volume, schema, and freshness checks tell you if the data is present and structured correctly, distribution monitoring helps you catch hidden issues like skewed categories or outlier spikes. It's a key component of any modern observability platform focused on data reliability.

The earlier, the better. Building good habits like CI/CD, code reviews, and clear documentation from the start helps prevent data issues down the line. Implementing telemetry instrumentation and automated data validation rules early on can significantly improve data pipeline monitoring and support long-term SLA compliance.

Great question! Data monitoring is like your car's dashboard—it alerts you when something goes wrong, like a failed pipeline or a missing dataset. Data observability, on the other hand, is like being the driver. It gives you a full understanding of how your data behaves, where it comes from, and how issues impact downstream systems. At Sifflet, we believe in going beyond alerts to deliver true data observability across your entire stack.

Using an observability platform like Sifflet brings several benefits: real-time anomaly detection, proactive incident management, improved SLA compliance, and better data governance. By combining metrics, metadata, and lineage, we help teams move from reactive data quality monitoring to proactive, scalable observability that supports reliable, data-driven decisions.