Redshift
Integrate Sifflet with Redshift to access end-to-end lineage, monitor assets like Spectrum tables, enrich metadata, and gain insights for optimized data observability.




Exhaustive metadata
Sifflet leverages Redshift's internal metadata tables to retrieve information about your assets and enhance it with Sifflet-generated insights.


End-to-end lineage
Have a complete understanding of how data flows through your platform via end-to-end lineage for Redshift.
Redshift Spectrum support
Sifflet can monitor external tables via Redshift Spectrum, allowing you to ensure the quality of data stored in other systems like S3.


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Frequently asked questions
How does data observability differ from traditional data quality monitoring?
Great question! Traditional data quality monitoring focuses on pre-defined rules and tests, but it often falls short when unexpected issues arise. Data observability, on the other hand, provides end-to-end visibility using telemetry instrumentation like metrics, metadata, and lineage. This makes it possible to detect anomalies in real time and troubleshoot issues faster, even in complex data environments.
How does Sifflet help with SLA compliance and incident response?
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.
What makes SQL Table Tracer suitable for real-world data observability use cases?
STT is designed to be lightweight, extensible, and accurate. It supports complex SQL features like CTEs and subqueries using a composable, monoid-based design. This makes it ideal for integrating into larger observability tools, ensuring reliable data lineage tracking and SLA compliance.
What role does Sifflet’s data catalog play in observability?
Sifflet’s data catalog acts as the central hub for your data ecosystem, enriched with metadata and classification tags. This foundation supports cloud data observability by giving teams full visibility into their assets, enabling better data lineage tracking, telemetry instrumentation, and overall observability platform performance.
Where can I find Sifflet at Big Data LDN 2024?
You can find the Sifflet team at Booth Y640 during Big Data LDN on September 18-19. Stop by to learn more about our data observability platform and how we’re helping organizations like the BBC and Penguin Random House improve their data reliability.
How does this integration help with root cause analysis?
By including Fivetran connectors and source assets in the lineage graph, Sifflet gives you full visibility into where data issues originate. This makes it much easier to perform root cause analysis and resolve incidents faster, improving overall data reliability.
How can data observability support better hiring decisions for data teams?
When you prioritize data observability, you're not just investing in tools, you're building a culture of transparency and accountability. This helps attract top-tier Data Engineers and Analysts who value high-quality pipelines and proactive monitoring. Embedding observability into your workflows also empowers your team with root cause analysis and pipeline health dashboards, helping them work more efficiently and effectively.
Can Sifflet Insights help with data pipeline monitoring?
Absolutely! Sifflet Insights connects to your broader observability platform, giving you visibility into data pipeline health right from your BI dashboards. It helps track incidents, monitor data freshness, and detect anomalies before they impact your business decisions.