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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

"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

"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

"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 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

"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

How does data ingestion relate to data observability?

Great question! Data ingestion is where observability starts. Once data enters your system, observability platforms like Sifflet help monitor its quality, detect anomalies, and ensure data freshness. This allows teams to catch ingestion issues early, maintain SLA compliance, and build trust in their data pipelines.

What role does anomaly detection play in modern data contracts?

Anomaly detection helps identify unexpected changes in data that might signal contract violations or semantic drift. By integrating predictive analytics monitoring and dynamic thresholding into your observability platform, you can catch issues before they break dashboards or compromise AI models. It’s a core feature of a resilient, intelligent metadata layer.

How does Sifflet help with root cause analysis when something breaks in a data pipeline?

When a data issue arises, Sifflet gives you the context you need to act fast. Our observability platform connects the dots across your data stack—tracking lineage, surfacing schema changes, and highlighting impacted assets. That makes root cause analysis much easier, whether you're dealing with ingestion latency or a failed transformation job. Plus, our AI helps explain anomalies in plain language.

How does MCP support data quality monitoring in modern observability platforms?

MCP helps LLMs become active participants in data quality monitoring by giving them access to structured resources like schema definitions, data validation rules, and profiling metrics. At Sifflet, we use this to detect anomalies, enforce data contracts, and ensure SLA compliance more effectively.

How does SQL Table Tracer handle different SQL dialects?

SQL Table Tracer uses Antlr4 with semantic predicates to support multiple SQL dialects like Snowflake, Redshift, and PostgreSQL. This flexible parsing approach ensures accurate lineage extraction across diverse environments, which is essential for data pipeline monitoring and distributed systems observability.

How does integrating dbt with Sifflet improve data observability?

Great question! When you integrate dbt with Sifflet, you unlock a whole new level of data observability. Sifflet enhances visibility into your dbt models by pulling in metadata, surfacing test results, and mapping them into a unified lineage view. This makes it easier to monitor data pipelines, catch issues early, and ensure data reliability across your organization.

How does data observability improve data contract enforcement?

Data observability adds critical context that static contracts lack, such as data lineage tracking, real-time usage patterns, and anomaly detection. With observability tools, teams can proactively monitor contract compliance, detect schema drift early, and ensure SLA compliance before issues impact downstream systems. It transforms contracts from documentation into enforceable, living agreements.

Why is embedding observability tools at the orchestration level important?

Embedding observability tools like Flow Stopper at the orchestration level gives teams visibility into pipeline health before data hits production. This kind of proactive monitoring is key for maintaining data reliability and reducing downtime due to broken pipelines.

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