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Data Leader

Transform your data and analytics strategy and pave the way for AI by upleveling data quality, trust, reliability and overall team efficiency.

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Data Quality and Trust

Sifflet makes it possible to establish trust in data across your organization thanks to real time monitoring of data quality, completeness, and accuracy.

Operational Efficiency

Increase your team’s operational efficiency. Sifflet reduces the time your data teams spend on manual quality checks and troubleshooting. It also enables proactive issue resolution before problems cause downstream systems.

Risk and Compliance Management

Manage data risk and compliance. Sifflet helps you document and monitor data access patterns and potential security risks.

Drive Innovation and Enable AI

Sifflet’s data observability platform delivers the performance you need to keep data quality and reliability at peak, paving the way for game-changing digital capabilities and products.

Augment Your Team’s Productivity and Effectiveness

Data engineers, data analysts and data scientists are critical to your business’s most strategic work. Sifflet augments their productivity by giving them back hundreds of hours spent on mundane reliability or accuracy tasks. Everyone’s more effective with data observability.

See Value From Day One

Sifflet connects to hundreds of tools already in your stack and offers out of the box monitors and tooling so you can start seeing value from day one.

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

Get Further

Data Governance - A Thought Leader's Perspective

A Guide to Assessing your Need for Data Observability

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Frequently asked questions

Can data observability improve collaboration across data teams?

Absolutely! With shared visibility into data flows and transformations, observability platforms foster better communication between data engineers, analysts, and business users. Everyone can see what's happening in the pipeline, which encourages ownership and teamwork around data reliability.

How does Sifflet support reverse ETL and operational analytics?

Sifflet enhances reverse ETL workflows by providing data observability dashboards and real-time monitoring. Our platform ensures your data stays fresh, accurate, and actionable by enabling root cause analysis, data lineage tracking, and proactive anomaly detection across your entire pipeline.

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.

What are some key features to look for in an observability platform for data?

A strong observability platform should offer data lineage tracking, real-time metrics, anomaly detection, and data freshness checks. It should also integrate with your existing tools like Airflow or Snowflake, and support alerting through Slack or webhook integrations. These capabilities help teams monitor data pipelines effectively and respond quickly to issues.

Why is data observability becoming such a priority for enterprises in 2025?

Great question! As more organizations rely on AI and analytics for decision-making, ensuring data quality, health, and reliability has become non-negotiable. Data observability platforms like Sifflet help teams detect issues early, reduce downtime, and maintain trust in their data pipelines.

How can organizations choose the right observability tools for their data stack?

Choosing the right observability tools depends on your data maturity and stack complexity. Look for platforms that offer comprehensive data quality monitoring, support for both batch and streaming data, and features like data lineage tracking and alert correlation. Platforms like Sifflet provide end-to-end visibility, making it easier to maintain SLA compliance and reduce incident response times.

What role does data ownership play in data quality monitoring?

Clear data ownership is a game changer for data quality monitoring. When each data product has a defined owner, it’s easier to resolve issues quickly, collaborate across teams, and build a strong data culture that values accountability and trust.

How can I monitor the health of my ETL or ELT pipelines?

Monitoring pipeline health is essential for maintaining data reliability. You can use tools that offer data pipeline monitoring features such as real-time metrics, ingestion latency tracking, and pipeline error alerting. Sifflet’s pipeline health dashboard gives you full visibility into your ETL and ELT processes, helping you catch issues early and keep your data flowing smoothly.

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