Great question! Sifflet acts as a unified observability platform that consolidates metadata from tools like Snowflake and BigQuery into one centralized Data Catalog. By surfacing tags, labels, and schema details, it makes data discovery and governance much easier for all stakeholders.

Smart alerting helps your team focus on what really matters. Instead of flooding your Slack with every minor issue, a good observability tool prioritizes alerts based on business impact and data asset importance. This reduces alert fatigue and ensures the right people get notified at the right time. Look for platforms that offer customizable severity levels, real-time alerts, and integrations with your incident management tools like PagerDuty or email alerts.

That's a smart consideration! While 100% accuracy sounds ideal, it's often costly and unrealistic. A better approach is to define acceptable thresholds through data validation rules and data profiling. By using observability platforms that support threshold-based alerts and dynamic thresholding, teams can focus on what matters most without over-investing in perfection.

By combining real-time metrics monitoring with proactive alerts and incident management workflows, Sifflet helps teams stay on top of SLA compliance. Users can track metrics freshness, detect anomalies, and take action before SLA breaches occur.

Data lineage is the process of tracing data as it moves from source to destination, including all transformations along the way. It's a critical component of data observability because it helps teams understand dependencies, troubleshoot issues faster, and maintain data reliability across the entire pipeline.

Successful data governance is measured through KPIs that tie directly to business outcomes. This includes metrics like how quickly teams can find data, how often data quality issues are caught before reaching production, and how well teams follow access protocols. Observability tools help track these indicators by providing real-time metrics and alerting on governance-related issues.

Silent failures are tricky, but with the right data observability tools, you can catch them early. Look for platforms that support real-time alerts, schema registry integration, and dynamic thresholding. These features help you monitor for unexpected changes, missing data, or drift in your pipelines. Sifflet, for example, offers anomaly detection and root cause analysis that help you uncover and fix issues before they impact your business.

Absolutely! We know that data patterns can shift during holidays or unique business dates. That’s why Sifflet now lets you exclude these dates from alerts by selecting from common calendars or customizing your own. This helps reduce alert fatigue and improves the accuracy of anomaly detection across your data pipelines.