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Sifflet seamlessly integrates into your data sources and preferred tools, and can run on AWS, Google Cloud Platform, and Microsoft Azure.

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The Sifflet team is always working hard on incorporating more integrations into our product. Get in touch if you want us to keep you updated!

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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

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Frequently asked questions

What is SQL Table Tracer and how does it help with data lineage tracking?

SQL Table Tracer (STT) is a lightweight library that automatically extracts table-level lineage from SQL queries. It identifies both destination and upstream tables, making it easier to understand data dependencies and build reliable data lineage workflows. This is a key component of any effective data observability strategy.

What’s the difference between a data schema and a database schema?

Great question! A data schema defines structure across your entire data ecosystem, including pipelines, APIs, and ingestion tools. A database schema, on the other hand, is specific to one system, like PostgreSQL or BigQuery, and focuses on tables, columns, and relationships. Both are essential for effective data governance and observability.

What makes Sifflet’s approach to anomaly detection more reliable than traditional methods?

Sifflet uses intelligent, ML-driven anomaly detection that evolves with your data. Instead of relying on static rules, it adjusts sensitivity and parameters in real time, improving data reliability and helping teams focus on real issues without being overwhelmed by alert fatigue.

Can Subdomains help with data governance and compliance requirements like GDPR or HIPAA?

Absolutely. With granular access control at the subdomain level, you can restrict sensitive data access to only the right people. This makes it much easier to meet data governance and compliance standards such as GDPR, HIPAA, and SOC 2, especially in highly regulated industries.

What’s the best way to prevent bad data from impacting our business decisions?

Preventing bad data starts with proactive data quality monitoring. That includes data profiling, defining clear KPIs, assigning ownership, and using observability tools that provide real-time metrics and alerts. Integrating data lineage tracking also helps you quickly identify where issues originate in your data pipelines.

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.

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.

What are some of the latest technologies integrated into Sifflet's observability tools?

We've been exploring and integrating a variety of cutting-edge technologies, including dynamic thresholding for anomaly detection, data profiling tools, and telemetry instrumentation. These tools help enhance our pipeline health dashboard and improve transparency in data pipelines.

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