Solution by Use Case

Support for your Federated Data Model

Scale Trust Across Domains

Empower business users to confidently own their data without sacrificing central governance and reliability.

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Enable Decentralized Ownership without Losing Control

Sifflet supports enterprise operating models by establishing clear data contracts and accountability, enabling domain teams to own data quality without central bottlenecks.

Route incidents automatically to the specific domain team responsible for the data, eliminating triage time spent by central teams lacking context.
Empower business users with role-based access and incident-centric alerting to manage their own data health.
Embed guardrails directly into domain workflows with declarative "Trust-as-Code" policies.

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End-to-End Visibility Across the Mesh

Give every team a shared, trusted view of data flows. Sifflet provides cross-domain lineage visibility to end arguments about transformation logic and ownership.

Visually trace column-level lineage between different assets to instantly understand downstream impact when something breaks.
Enable self-service discovery so any team can securely see the provenance of the data they consume.
Provide data consumers with Data Product Health Scores to ensure data is safe to use across domain boundaries.

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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Still have a question in mind ?

Frequently asked questions

What makes observability scalable across different teams and roles?

Scalable observability works for engineers, analysts, and business stakeholders alike. It supports telemetry instrumentation for developers, intuitive dashboards for analysts, and high-level confidence signals for executives. By adapting to each role without adding friction, observability becomes a shared language across the organization.

How does Sifflet use MCP to enhance observability in distributed systems?

At Sifflet, we’re leveraging MCP to build agents that can observe, decide, and act across distributed systems. By injecting telemetry data, user context, and pipeline metadata as structured resources, our agents can navigate complex environments and improve distributed systems observability in a scalable and modular way.

Why is data observability gaining momentum now, even though software observability has been around for a while?

Great question! Software observability took off in the 2010s with the rise of cloud-native apps, but data observability is catching up fast. As businesses start treating data as a mission-critical asset—especially with the growth of AI and cloud data platforms like Snowflake—the need for real-time visibility, data reliability, and governance has become urgent. We're in the early innings, but the pace is accelerating quickly.

What are some key benefits of using an observability platform like Sifflet?

Using an observability platform like Sifflet brings several benefits: real-time anomaly detection, proactive incident management, improved SLA compliance, and better data governance. By combining metrics, metadata, and lineage, we help teams move from reactive data quality monitoring to proactive, scalable observability that supports reliable, data-driven decisions.

How does Sifflet support data documentation in Airflow?

Sifflet centralizes documentation for all your data assets, including DAGs, models, and dashboards. This makes it easier for teams to search, explore dependencies, and maintain strong data governance practices.

How does data observability differ from traditional data quality monitoring?

Great question! While data quality monitoring focuses on detecting when data doesn't meet expected thresholds, data observability goes further. It continuously collects signals like metrics, metadata, and lineage to provide context and root cause analysis when issues arise. Essentially, observability helps you not only detect anomalies but also understand and fix them faster, making it a more proactive and scalable approach.

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 makes Sifflet a strong alternative to Metaplane for enterprise data teams?

Sifflet stands out as a Metaplane alternative because it offers full-stack data observability with field-level lineage, automated root cause analysis, and business context built into every alert. Its AI-powered agents help reduce alert fatigue and guide remediation, making it ideal for complex, fast-scaling environments where data reliability is crucial.

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