Absolutely! With Sifflet’s declarative framework, you can programmatically declare assets from tools like Salesforce, SAP, or HubSpot, even if they aren’t natively integrated. This helps you maintain a complete and unified view of your data ecosystem for better data governance.

Metaplane is great for small teams and dbt-centric workflows, but it lacks depth in areas like infrastructure observability, field-level lineage, and ML model monitoring. As your stack grows to include streaming data, hybrid cloud, or multiple orchestration tools, you’ll need a more robust observability platform to maintain data quality and SLA compliance.

At Sifflet, data security is built into the foundation of our data observability platform. We follow three core principles: least privilege, no storage, and single tenancy. This means we only use read-only access, never store your data, and isolate each customer’s environment to prevent cross-tenant access.

Sifflet's integration with Apache Airflow brings powerful data observability features directly into your orchestration workflows. It helps data teams monitor DAG run statuses, understand downstream dependencies, and apply data quality monitoring to catch issues early, ensuring data reliability across the stack.

Great question! At Sifflet, we believe that bringing together data cataloging, data quality monitoring, and lineage tracking into a single observability platform helps reduce Data Entropy and streamline how teams manage and trust their data. By centralizing these capabilities, users can quickly discover assets, monitor their health, and troubleshoot issues without switching tools.

We've been exploring and integrating a variety of cutting-edge technologies, including dynamic thresholding for anomaly detection, data profiling tools, and telemetry instrumentation. These tools help enhance our pipeline health dashboard and improve transparency in data pipelines.

Carrefour’s approach combines no-code self-service tools with as-code automation, making it easy for both technical and non-technical users to adopt. This balance, along with incremental implementation and cultural emphasis on data quality, supports scalable observability across the organization.

Absolutely! By clearly mapping upstream and downstream table relationships, SQL Table Tracer helps teams quickly trace issues back to their source. This accelerates root cause analysis and supports faster, more effective incident response workflows in any observability platform.