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

Can I use custom dbt metadata for data governance in Sifflet?

Absolutely! Our new dbt tab surfaces custom metadata defined in your dbt models, which you can leverage for better data governance and data profiling. It’s all about giving you the flexibility to manage your data assets exactly the way you need.

What should I look for when choosing a data integration tool?

Look for tools that support your data sources and destinations, offer automation, and ensure compliance. Features like schema registry integration, real-time metrics, and alerting can also make a big difference. A good tool should work seamlessly with your observability tools to maintain data quality and trust.

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.

Can container-based environments improve incident response for data teams?

Absolutely. Containerized environments paired with observability tools like Kubernetes and Prometheus for data enable faster incident detection and response. Features like real-time alerts, dynamic thresholding, and on-call management workflows make it easier to maintain healthy pipelines and reduce downtime.

How does Sifflet help with end-to-end data observability?

Sifflet enhances end-to-end data observability by allowing you to declare any asset in your data stack, including custom applications and scripts. This ensures full visibility into your data pipelines and supports comprehensive data lineage tracking and root cause analysis.

How can Sifflet help ensure SLA compliance and prevent bad data from affecting business decisions?

Sifflet helps teams stay on top of SLA compliance with proactive data freshness checks, anomaly detection, and incident tracking. Business users can rely on health indicators and lineage views to verify data quality before making decisions, reducing the risk of costly errors due to unreliable data.

What’s new with the Distribution Change monitor and how does it improve anomaly detection?

The upgraded Distribution Change monitor now focuses on tracking volume shifts between specific categories, like product lines or customer segments. This makes anomaly detection more precise by reducing noise and highlighting only the changes that truly matter. It's a smarter way to stay on top of data drift and ensure your metrics reflect reality.

Why is data observability a crucial part of the modern data stack?

Data observability is essential because it ensures data reliability across your entire stack. As data pipelines grow more complex, having visibility into data freshness, quality, and lineage helps prevent issues before they impact the business. Tools like Sifflet offer real-time metrics, anomaly detection, and root cause analysis so teams can stay ahead of data problems and maintain trust in their analytics.

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