If you're just starting out, look for someone with 'Full Data Stack' capabilities, like a Data Analyst with strong SQL and business acumen or a Data Engineer with analytics skills. This person can work closely with other teams to build initial pipelines and help shape your data platform. As your needs evolve, you can grow your team with more specialized roles.

Sifflet’s observability platform offers powerful features like anomaly detection, pipeline error alerting, and data freshness checks. We help teams stay on top of their data workflows and ensure SLA compliance with minimal friction. Come chat with us at Booth Y640 to learn more!

Data quality monitoring ensures your data is accurate, complete, and consistent. A good data catalog should include profiling and validation tools that help teams assess data quality, which is crucial for maintaining SLA compliance and enabling proactive monitoring.

Great question! This partnership allows Google Cloud users to integrate Sifflet’s data observability platform directly within their private cloud environment. That means better visibility, reliability, and trust in your data from ingestion all the way to analytics.

The 'Metadata Ceiling' refers to the limitations of infrastructure-first observability tools like Datadog when it comes to understanding the actual content and business impact of data. While Datadog excels at monitoring pipeline health and system performance, it lacks the deep data observability features required to catch issues like null values in critical reports or corrupted inputs in AI models. For full visibility into data quality and business relevance, a specialized observability platform like Sifflet is often a better fit.

This checklist is for anyone who relies on trustworthy data—from CDOs and analysts to DataOps teams and engineers. Whether you're focused on data governance, anomaly detection, or building resilient pipelines, the checklist gives you a clear path to choosing the right observability tools.

Sifflet supports SLA compliance by offering intelligent alerting, dynamic thresholding, and real-time dashboards that track incident metrics and resolution times. Its data reliability dashboard gives teams visibility into SLA adherence and helps prioritize issues based on business impact, streamlining incident management workflows and reducing mean time to resolution.

By surfacing impacted tables, dashboards, and other assets directly in GitHub or GitLab, Sifflet’s dbt Impact Analysis gives teams real-time visibility into how changes affect the broader data pipeline. This supports better data pipeline monitoring and helps maintain data reliability.