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.

Talk to our experts Explore at your own pace

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.

Sifflet’s AI Helps Us Focus on What Moves the Business

What impressed us most about Sifflet’s AI-native approach is how seamlessly it adapts to our data landscape — without needing constant tuning. The system learns patterns across our workflows and flags what matters, not just what’s noisy. It’s made our team faster and more focused, especially as we scale analytics across the business.

Simoh-Mohamed Labdoui

Head of Data

"Enabler of Cross Platform Data Storytelling"

"Sifflet has been a game-changer for our organization, providing full visibility of data lineage across multiple repositories and platforms. The ability to connect to various data sources ensures observability regardless of the platform, and the clean, intuitive UI makes setup effortless, even when uploading dbt manifest files via the API. Their documentation is concise and easy to follow, and their team's communication has been outstanding—quickly addressing issues, keeping us informed, and incorporating feedback. "

Callum O'Connor

Senior Analytics Engineer, The Adaptavist

"Building Harmony Between Data and Business With Sifflet"

"Sifflet serves as our key enabler in fostering a harmonious relationship with business teams. By proactively identifying and addressing potential issues before they escalate, we can shift the focus of our interactions from troubleshooting to driving meaningful value. This approach not only enhances collaboration but also ensures that our efforts are aligned with creating impactful outcomes for the organization."

Sophie Gallay

Data & Analytics Director, Etam

" Sifflet empowers our teams through Centralized Data Visibility"

"Having the visibility of our DBT transformations combined with full end-to-end data lineage in one central place in Sifflet is so powerful for giving our data teams confidence in our data, helping to diagnose data quality issues and unlocking an effective data mesh for us at BBC Studios"

Ross Gaskell

Software engineering manager, BBC Studios

"Sifflet allows us to find and trust our data"

"Sifflet has transformed our data observability management at Carrefour Links. Thanks to Sifflet's proactive monitoring, we can identify and resolve potential issues before they impact our operations. Additionally, the simplified access to data enables our teams to collaborate more effectively."

Mehdi Labassi

CTO, Carrefour Links

"A core component of our data strategy and transformation"

"Using Sifflet has helped us move much more quickly because we no longer experience the pain of constantly going back and fixing issues two, three, or four times."

Sami Rahman

Director of Data, Hypebeast

Frequently asked questions

Can Sifflet’s dbt Impact Analysis help with root cause analysis?

Absolutely! By identifying all downstream assets affected by a dbt model change, Sifflet’s Impact Report makes it easier to trace issues back to their source, significantly speeding up root cause analysis and reducing incident resolution time.

How has AI changed the way companies think about data quality monitoring?

AI has definitely raised the stakes. As Salma shared on the Joe Reis Show, executives are being asked to 'do AI,' but many still struggle with broken pipelines. That’s why data quality monitoring and robust data observability are now seen as prerequisites for scaling AI initiatives effectively.

What makes Etam’s data strategy resilient in a fast-changing retail landscape?

Etam’s data strategy is built on clear business alignment, strong data quality monitoring, and a focus on delivering ROI across short, mid, and long-term horizons. With the help of an observability platform, they can adapt quickly, maintain data reliability, and support strategic decision-making even in uncertain conditions.

Is this feature part of Sifflet’s larger observability platform?

Yes, dbt Impact Analysis is a key addition to Sifflet’s observability platform. It integrates seamlessly into your GitHub or GitLab workflows and complements other features like data lineage tracking and data quality monitoring to provide holistic data observability.

How does Flow Stopper improve data reliability for engineering teams?

By integrating real-time data quality monitoring directly into your orchestration layer, Flow Stopper gives Data Engineers the ability to stop the flow when something looks off. This means fewer broken pipelines, better SLA compliance, and more time spent on innovation instead of firefighting.

Why is data freshness so important for data reliability?

Great question! Data freshness is a key part of data reliability because decisions are only as good as the data they're based on. If your data is outdated or delayed, it can lead to flawed insights and missed opportunities. That's why data freshness checks are a foundational element of any strong data observability strategy.

What role does data lineage tracking play in managing complex dbt pipelines?

Data lineage tracking is essential when your dbt projects grow in size and complexity. Sifflet provides a unified, metadata-rich lineage graph that spans your entire data stack, helping you quickly perform root cause analysis and impact assessments. This visibility is crucial for maintaining trust and transparency in your data pipelines.

How can data lineage tracking help with root cause analysis?

Data lineage tracking shows how data flows through your systems and how different assets depend on each other. This is incredibly helpful for root cause analysis because it lets you trace issues back to their source quickly. With Sifflet’s lineage capabilities, you can understand both upstream and downstream impacts of a data incident, making it easier to resolve problems and prevent future ones.

Read all FAQs

Still have questions?

Want to try Sifflet on your Redshift Stack

Give it a try now!

I want to try