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.

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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

How does Sifflet's Data Sharing feature help with enforcing data governance policies?

Great question! Sifflet's Data Sharing provides access to rich metadata about your data assets, including tags, owners, and monitor configurations. By making this available in your own data warehouse, you can set up automated checks to ensure compliance with your governance standards. It's a powerful way to implement scalable data governance and reduce manual audits using our observability platform.

What does it mean to treat data as a product?

Treating data as a product means managing data with the same care and strategy as a traditional product. It involves packaging, maintaining, and delivering high-quality data that serves a specific purpose or audience. This approach improves data reliability and makes it easier to monetize or use for strategic decision-making.

How does the new Custom Metadata feature improve data governance?

With Custom Metadata, you can tag any asset, monitor, or domain in Sifflet using flexible key-value pairs. This makes it easier to organize and route data based on your internal logic, whether it's ownership, SLA compliance, or business unit. It's a big step forward for data governance and helps teams surface high-priority monitors more effectively.

What should I look for in a data quality monitoring solution?

You’ll want a solution that goes beyond basic checks like null values and schema validation. The best data quality monitoring tools use intelligent anomaly detection, dynamic thresholding, and auto-generated rules based on data profiling. They adapt as your data evolves and scale effortlessly across thousands of tables. This way, your team can confidently trust the data without spending hours writing manual validation rules.

Why is data lineage tracking essential for modern data teams?

Data lineage tracking is key to understanding how data flows through your systems. It helps teams trace anomalies back to their source, identify downstream dependencies, and improve collaboration across departments. This visibility is crucial for maintaining data pipeline monitoring and SLA compliance.

What is dbt Impact Analysis and how does it help with data observability?

dbt Impact Analysis is a new feature from Sifflet that automatically comments on GitHub or GitLab pull requests with a list of impacted assets when a dbt model is changed. This helps teams enhance their data observability by understanding downstream effects before changes go live.

How does Shippeo ensure data reliability across its supply chain platform?

Shippeo uses Sifflet’s data observability platform to monitor every stage of their data pipelines. By implementing raw data monitoring, intermediate layer checks, and front-facing metric validation, they catch issues early and maintain trust in their real-time supply chain visibility tools.

Can I use data monitoring and data observability together?

Absolutely! In fact, data monitoring is often a key feature within a broader data observability solution. At Sifflet, we combine traditional monitoring with advanced capabilities like data profiling, pipeline health dashboards, and data drift detection so you get both alerts and insights in one place.

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