Solution by Platform

Databricks

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

Still have a question in mind ?

Frequently asked questions

What are some engineering challenges around the 'right to be forgotten' under GDPR?

The 'right to be forgotten' introduces several technical hurdles. For example, deleting user data across multiple systems, backups, and caches can be tricky. That's where data lineage tracking and pipeline orchestration visibility come in handy. They help you understand dependencies and ensure deletions are complete and safe without breaking downstream processes.

Why is data quality such a critical part of a data governance strategy?

Great question! Data quality is one of the foundational pillars of a strong data governance strategy because it directly impacts decision-making, compliance, and trust in your data. Poor data quality can lead to biased AI models, flawed analytics, and even regulatory risk. That's why integrating data quality monitoring early in your data lifecycle is key to building a reliable and responsible data foundation.

Who should be responsible for data quality in an organization?

That's a great topic! While there's no one-size-fits-all answer, the best data quality programs are collaborative. Everyone from data engineers to business users should play a role. Some organizations adopt data contracts or a Data Mesh approach, while others use centralized observability tools to enforce data validation rules and ensure SLA compliance.

How does Sifflet support proactive data pipeline monitoring?

Sifflet’s observability platform offers proactive data pipeline monitoring through extensive monitoring tools, real-time alerts, and historical performance insights. These features help your team stay ahead of issues and ensure your data pipelines are always delivering high-quality, reliable data.

How does Sifflet help with root cause analysis in data pipelines?

Sifflet uses intelligent agents to perform root cause analysis across your data lineage. Instead of just alerting you to an issue, it highlights the upstream source, impacted KPIs, and suggests remediation steps. This drastically cuts down investigation time and improves incident response in your data pipeline monitoring workflows.

How does data observability differ from traditional data quality monitoring?

Great question! Traditional data quality monitoring focuses on pre-defined rules and tests, but it often falls short when unexpected issues arise. Data observability, on the other hand, provides end-to-end visibility using telemetry instrumentation like metrics, metadata, and lineage. This makes it possible to detect anomalies in real time and troubleshoot issues faster, even in complex data environments.

How does Sifflet help detect and prevent data drift in AI models?

Sifflet is designed to monitor subtle changes in data distributions, which is key for data drift detection. This helps teams catch shifts in data that could negatively impact AI model performance. By continuously analyzing incoming data and comparing it to historical patterns, Sifflet ensures your models stay aligned with the most relevant and reliable inputs.

How does Sifflet enhance data observability compared to traditional monitoring tools?

Sifflet takes data observability to the next level by combining metadata with AI-powered features like automated root cause analysis, anomaly detection, and impact mapping. Unlike basic monitoring tools, our observability platform doesn't just alert you—it explains what happened and guides you toward resolution, helping teams respond faster and with more confidence.

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