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Analytics Trust and Reliability

Shared Understanding. Ultimate Confidence. At Scale.

When everyone knows your data is systematically validated for quality, understands where it comes from and how it's transformed, and is aligned on freshness and SLAs, what’s not to trust?

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Always Fresh. Always Validated.

No more explaining data discrepancies to the C-suite. Thanks to automatic and systematic validation, Sifflet ensures your data is always fresh and meets your quality requirements. Stakeholders know when data might be stale or interrupted, so they can make decisions with timely, accurate data.

Automatically detect schema changes, null values, duplicates, or unexpected patterns that could comprise analysis.
Set and monitor service-level agreements (SLAs) for critical data assets.
Track when data was last updated and whether it meets freshness requirements

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Understand Your Data, Inside and Out

Give data analysts and business users ultimate clarity. Sifflet helps teams understand their data across its whole lifecycle, and gives full context like business definitions, known limitations, and update frequencies, so everyone works from the same assumptions.

Create transparency by helping users understand data pipelines, so they always know where data comes from and how it’s transformed.
Develop shared understanding in data that prevents misinterpretation and builds confidence in analytics outputs.
Quickly assess which downstream reports and dashboards are affected

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

Why is data lineage tracking important in a data catalog solution?

Data lineage tracking is key to understanding how data flows through your systems. It helps teams visualize the origin and transformation of datasets, making root cause analysis and impact assessments much faster. For teams focused on data observability and pipeline health, this feature is a must-have.

Why is data observability important for large organizations?

Data observability helps organizations ensure data quality, monitor pipelines in real time, and build trust in their data. At Big Data LDN, we’ll share how companies like Penguin Random House use observability tools to improve data governance and drive better decisions.

Can I deploy Sifflet in my own environment for better control?

Absolutely! Sifflet offers both SaaS and self-managed deployment models. With the self-managed option, you can run the platform entirely within your own infrastructure, giving you full control and helping meet strict compliance and security requirements.

How can I monitor data freshness proactively instead of reacting to problems?

You can use a mix of threshold-based alerts, machine learning for anomaly detection, and visual freshness indicators in your BI tools. Pair these with data lineage tracking and root cause analysis to catch and resolve issues quickly. A modern data observability platform like Sifflet makes it easy to set up proactive monitoring tailored to your business needs.

How does Sifflet help with data lineage tracking?

Sifflet offers detailed data lineage tracking at both the table and field level. You can easily trace data upstream and downstream, which helps avoid unexpected issues when making changes. This transparency is key for data governance and ensuring trust in your analytics pipeline.

How does Sifflet help close the observability gap for Airbyte pipelines?

Great question! Sifflet bridges the observability gap for Airbyte by using our Declarative Lineage API and a custom Python script. This allows you to capture complete data lineage from Airbyte and ingest it into Sifflet, giving you full visibility into your pipelines and enabling better root cause analysis and data quality monitoring.

How can poor data distribution impact machine learning models?

When data distribution shifts unexpectedly, it can throw off the assumptions your ML models are trained on. For example, if a new payment processor causes 70% of transactions to fall under $5, a fraud detection model might start flagging legitimate behavior as suspicious. That's why real-time metrics and anomaly detection are so crucial for ML model monitoring within a good data observability framework.

How does MCP improve root cause analysis in modern data systems?

MCP empowers LLMs to use structured inputs like logs and pipeline metadata, making it easier to trace issues across multiple steps. This structured interaction helps streamline root cause analysis, especially in complex environments where traditional observability tools might fall short. At Sifflet, we’re integrating MCP to enhance how our platform surfaces and explains data incidents.

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