Modern Data Governance Starts with Observability

Data Governance Leader

You’re not just checking compliance boxes. You’re enabling safe, scalable data use across the organization. Sifflet gives governance leaders full visibility, control, and automation, so policies stick, risks shrink, and data stays trusted no matter how fast things move.

Stronger Compliance, Less Manual Work

Automated cataloging, lineage, and audit trails make it easier to meet regulatory requirements without drowning in spreadsheets or manual updates. Sifflet keeps your governance up to date and always inspection-ready.

Fewer Blind Spots Across the Data Stack

With Sifflet, governance teams get full visibility across ingestion, transformation, and consumption, including shadow data and undocumented assets. You can finally govern what’s actually in use, not just what’s documented.

Governance That Scales With the Business

Sifflet integrates directly into your data workflows, so policies and controls scale alongside your teams and infrastructure. Whether you're onboarding new domains or expanding your stack, governance stays aligned and under control.

A Catalog That’s Actually Alive

Most catalogs go out of date the moment they’re published. Sifflet’s catalog is powered by automated metadata ingestion across your full stack: Snowflake, Databricks, dbt, Tableau, and more. You get real-time visibility into schema changes, freshness, usage, and ownership. No more chasing people for updates. No more flying blind.

‍

Lineage That Goes Beyond Tables

Sifflet provides deep, column-level lineage with full context, not just technical dependencies, but business impact. You can trace an issue from a broken pipeline to a downstream dashboard, and instantly see which KPIs, reports, or teams are affected. This makes policy enforcement, impact analysis, and root-cause resolution fast and reliable.

Governance Built Into the Workflow

Sifflet turns governance from a reactive process into a built-in feature of everyday data ops. You can tag sensitive assets, certify trusted datasets, monitor for violations, and set up alerts, all from inside the platform. No need to bolt governance onto the side. It’s already baked in, and it scales with your data.

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

Discover More

Data Governance: a Thought Leader Perspective

Data Quality is one of the core pillars of a successful Data Governance Strategy, find out more about it with Dan Power, Thought Leader and MD in Data Governance at State Street Asset Management.

Tell me more

What is Data Governance? A Reminder

Discover how data governance offers value beyond compliance: what it is, why it matters, and best practices for implementation.

Take me back to school

Frequently asked questions

Can MCP help with data pipeline monitoring and incident response?

Absolutely! MCP allows LLMs to remember past interactions and call diagnostic tools, which is a game-changer for data pipeline monitoring. It supports multi-turn conversations and structured tool use, making incident response faster and more contextual. This means less time spent digging through logs and more time resolving issues efficiently.

What’s on the horizon for data observability as AI and regulations evolve?

The future of data observability is all about scale and responsibility. With AI adoption growing and regulations tightening, businesses need observability tools that can handle unstructured data, ensure SLA compliance, and support security observability. At Sifflet, we're already helping customers monitor ML models and enforce data contracts, and we're excited about building self-healing pipelines and extending observability to new data types.

What trends are driving the demand for centralized data observability platforms?

The growing complexity of data products, especially with AI and real-time use cases, is driving the need for centralized data observability platforms. These platforms support proactive monitoring, root cause analysis, and incident response automation, making it easier for teams to maintain data reliability and optimize resource utilization.

Can Sifflet integrate with my existing data stack for seamless data pipeline monitoring?

Absolutely! One of Sifflet’s strengths is its seamless integration across your existing data stack. Whether you're working with tools like Airflow, Snowflake, or Kafka, Sifflet helps you monitor your data pipelines without needing to overhaul your infrastructure.

Why is combining dbt Core with a data observability platform like Sifflet a smart move?

Combining dbt Core with a data observability platform like Sifflet helps data teams go beyond transformation and into full-stack monitoring. It enables better root cause analysis, reduces time to resolution, and ensures your data products are trustworthy and resilient.

What is data observability and why is it important for modern data teams?

Data observability is the practice of monitoring data as it moves through your pipelines to detect, understand, and resolve issues proactively. It’s crucial because it helps data teams ensure data reliability, improve decision-making, and reduce the time spent firefighting data issues. With the growing complexity of data systems, having a robust observability platform is key to maintaining trust in your data.

What is data lineage and why is it important for data observability?

Data lineage is the process of tracing data as it moves from source to destination, including all transformations along the way. It's a critical component of data observability because it helps teams understand dependencies, troubleshoot issues faster, and maintain data reliability across the entire pipeline.

What’s the main difference between ETL and ELT?

Great question! While both ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) are data integration methods, the key difference lies in the order of operations. ETL transforms data before loading it into a data warehouse, whereas ELT loads raw data first and transforms it inside the warehouse. ELT has become more popular with the rise of cloud data warehouses like Snowflake and BigQuery, which offer scalable storage and computing power. If you're working with large volumes of data, ELT might be the better fit for your data pipeline monitoring strategy.

Read all FAQs

Still have questions?