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.
Used by




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.


Frequently asked questions
Why are traditional data catalogs no longer enough for modern data teams?
Traditional data catalogs focus mainly on metadata management, but they don't actively assess data quality or track changes in real time. As data environments grow more complex, teams need more than just an inventory. They need data observability tools that provide real-time metrics, anomaly detection, and data quality monitoring to ensure reliable decision-making.
What role does reverse ETL play in operational analytics?
Reverse ETL bridges the gap between data teams and business users by moving data from the warehouse into tools like CRMs and marketing platforms. This enables operational analytics, where business teams can act on real-time data. To ensure this process runs smoothly, data observability dashboards can monitor for pipeline errors and enforce data validation rules.
How does Sifflet help with compliance monitoring and audit logging?
Sifflet is ISO 27001 certified and SOC 2 compliant, and we use a separate secret manager to handle credentials securely. This setup ensures a strong audit trail and tight access control, making compliance monitoring and audit logging seamless for your data teams.
What does 'observability culture' mean at Adaptavist?
For Adaptavist, observability culture means going beyond tools. It's about clear ownership of alerts, integrating data quality monitoring into sprints, and giving stakeholders ways to provide feedback directly in dashboards. They even track observability metrics to continuously improve their own observability practices.
How do Service Level Indicators (SLIs) help improve data product reliability?
SLIs are a fantastic way to measure the health and performance of your data products. By tracking metrics like data freshness, anomaly detection, and real-time alerts, you can ensure your data meets expectations and stays aligned with your team’s SLA compliance goals.
What does Full Data Stack Observability mean?
Full Data Stack Observability means having complete visibility into every layer of your data pipeline, from ingestion to business intelligence tools. At Sifflet, our observability platform collects signals across your entire stack, enabling anomaly detection, data lineage tracking, and real-time metrics collection. This approach helps teams ensure data reliability and reduce time spent firefighting issues.
Why is using WHERE instead of HAVING so important for performance?
Using WHERE instead of HAVING when not working with GROUP BY clauses is crucial because WHERE filters data earlier in the query execution. This reduces the amount of data processed, which improves query speed and supports better metrics collection in your observability platform.
How does data observability support MLOps and AI initiatives at Hypebeast?
Data observability plays a key role in Hypebeast’s MLOps strategy by monitoring data quality from ML models before it reaches dashboards or decision systems. This ensures that AI-driven insights are trustworthy and aligned with business goals.
Want to try Sifflet on your Redshift Stack
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