You can prevent schema-related breakages by using data observability tools that offer real-time schema drift detection and alerting. These tools help you catch changes early, validate against data contracts, and maintain SLA compliance across your data pipelines.

The MCP (Model Context Protocol) Server is a new interface that lets you interact with Sifflet directly from your development environment. It's designed to make data observability more seamless by allowing you to query assets, review incidents, and trace data lineage without leaving your IDE or notebook. This helps streamline your workflow and gives you real-time visibility into pipeline health and data quality.

Honestly, anyone who relies on data to make decisions—so pretty much everyone. Data engineers, BI teams, data scientists, RevOps, finance, and even executives all benefit. With Sifflet, teams get proactive alerts, root cause analysis, and cross-functional visibility. That means fewer surprises, faster resolutions, and more trust in the data that powers your business.

Great question! Sifflet makes data accessibility a breeze by offering intuitive search features and AI-generated metadata, so both technical and non-technical users can easily find and understand the data they need. This helps break down silos and supports better collaboration, which is a key component of effective data observability.

Yes, we're excited to share that a native Airbyte connector is in the works! This will make it even easier to integrate and monitor Airbyte pipelines within our observability platform. Stay tuned as we continue to enhance our capabilities around data lineage, automated root cause analysis, and pipeline resilience.

Sifflet's dbt runs tab offers deep insights into model execution, cost, and runtime, making it easy to spot inefficiencies. You can also use historical performance data to set up custom dashboards and proactive monitors. This helps with capacity planning and ensures your data pipelines stay optimized and cost-effective.

Choosing the right observability tools depends on your data maturity and stack complexity. Look for platforms that offer comprehensive data quality monitoring, support for both batch and streaming data, and features like data lineage tracking and alert correlation. Platforms like Sifflet provide end-to-end visibility, making it easier to maintain SLA compliance and reduce incident response times.