The Ultimate Observability Duo for the Modern Data Stack

Monitor. Trust. Act.

With Sifflet fully integrated into your Databricks environment, your data teams gain end-to-end visibility, AI-powered monitoring, and business-context awareness, without compromising performance.

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Why Choose Sifflet for Databricks?

Modern organizations rely on Databricks to unify data engineering, machine learning, and analytics. But as the platform grows in complexity, new risks emerge:

Broken pipelines that go unnoticed
Data quality issues that erode trust
Limited visibility across orchestration and workflows

That’s where Sifflet comes in. Our native integration with Databricks ensures your data pipelines are transparent, reliable, and business-aligned, at scale.

Deep Integration with Databricks

Sifflet enhances the observability of your Databricks stack across:

Delta Pipelines & DLT

Monitor transformation logic, detect broken jobs, and ensure SLAs are met across streaming and batch workflows.

Notebooks & ML Models

Trace data quality issues back to the tables or features powering production models.

Unity Catalog & Lakehouse Metadata

Integrate catalog metadata into observability workflows, enriching alerts with ownership and context.

Cross-Stack Connectivity

Sifflet integrates with dbt, Airflow, Looker, and more, offering a single observability layer that spans your entire lakehouse ecosystem.

End-to-End Data Observability

Full monitoring across the data lifecycle: from raw ingestion in Databricks to BI consumption
Real-time alerts for freshness, volume, nulls, and schema changes
AI-powered prioritization so teams focus on what really matters

Deep Lineage & Root Cause Analysis

Column-level lineage across tables, SQL jobs, notebooks, and workflows
Instantly surface the impact of schema changes or upstream issues
Native integration with Unity Catalog for a unified metadata view

Operational & Governance Insights

Query-level telemetry, access logs, job runs, and system metadata
All fully queryable and visualized in observability dashboards
Enables governance, cost optimization, and security monitoring

Native Integration with Databricks Ecosystem

Tight integration with Databricks REST APIs and Unity Catalog
Observability for Databricks Workflows from orchestration to execution
Plug-and-play setup, no heavy engineering required

Built for Enterprise-Grade Data Teams

Certified Databricks Technology Partner
Deployed in production across global enterprises like St-Gobain and or Euronext
Designed for scale, governance, and collaboration

“The real value isn’t just in surfacing anomalies. It’s in turning observability into a strategic advantage. Sifflet enables exactly that, on Databricks, at scale.”
‍Senior Data Leader, North American Enterprise (Anonymous by Choice but happy)

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Perfect For…

Data leaders scaling Databricks across teams
Analytics teams needing trustworthy dashboards
Governance teams requiring real lineage and audit trails
ML teams who need reliable, explainable training 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

Frequently asked questions

Why is data governance important when treating data as a product?

Data governance ensures that data is collected, managed, and shared responsibly, which is especially important when data is treated as a product. It helps maintain compliance with regulations and supports data quality monitoring. With proper governance in place, businesses can confidently deliver reliable and secure data products.

How do the four pillars of data observability help improve data quality?

The four pillars—metrics, metadata, data lineage, and logs—work together to give teams full visibility into their data systems. Metrics help with data profiling and freshness checks, metadata enhances data governance, lineage enables root cause analysis, and logs provide insights into data interactions. Together, they support proactive data quality monitoring.

Can Sifflet’s dbt Impact Analysis help with root cause analysis?

Absolutely! By identifying all downstream assets affected by a dbt model change, Sifflet’s Impact Report makes it easier to trace issues back to their source, significantly speeding up root cause analysis and reducing incident resolution time.

What makes Sifflet's approach to data pipeline monitoring unique?

We take a holistic, end-to-end approach to data pipeline monitoring. By collecting telemetry across the entire data stack and automatically tracking field-level data lineage, we empower teams to quickly identify issues and understand their downstream impact, making incident response and resolution much more efficient.

How can I measure whether my data is trustworthy?

Great question! To measure data quality, you can track key metrics like accuracy, completeness, consistency, relevance, and freshness. These indicators help you evaluate the health of your data and are often part of a broader data observability strategy that ensures your data is reliable and ready for business use.

How does Sifflet make data observability more accessible to BI users?

Great question! At Sifflet, we're committed to making data observability insights available right where you work. That’s why we’ve expanded beyond our Chrome extension to integrate directly with popular Data Catalogs like Atlan, Alation, Castor, and Data Galaxy. This means BI users can access real-time metrics and data quality insights without ever leaving their workflow.

What exactly is data quality, and why should teams care about it?

Data quality refers to how accurate, complete, consistent, and timely your data is. It's essential because poor data quality can lead to unreliable analytics, missed business opportunities, and even financial losses. Investing in data quality monitoring helps teams regain trust in their data and make confident, data-driven decisions.

What makes Carrefour’s approach to observability scalable and effective?

Carrefour’s approach combines no-code self-service tools with as-code automation, making it easy for both technical and non-technical users to adopt. This balance, along with incremental implementation and cultural emphasis on data quality, supports scalable observability across the organization.

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