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

Why is data lineage so critical in a data observability strategy?

Data lineage is the backbone of any strong data observability strategy. It helps teams trace data issues to their source by showing how data flows from ingestion to dashboards and models. With lineage, you can assess the impact of changes, improve collaboration across teams, and resolve anomalies faster. It's especially powerful when combined with anomaly detection and real-time metrics for full visibility across your pipelines.

What are some common consequences of bad data?

Bad data can lead to a range of issues including financial losses, poor strategic decisions, compliance risks, and reduced team productivity. Without proper data quality monitoring, companies may struggle with inaccurate reports, failed analytics, and even reputational damage. That’s why having strong data observability tools in place is so critical.

Can Sifflet help with root cause analysis when data issues arise?

Absolutely! Sifflet’s field-level data lineage tracking lets you trace data issues from BI dashboards all the way back to source systems. Its AI agent, Sage, even recalls past incidents to suggest likely causes, making root cause analysis faster and more accurate for data engineers and analysts alike.

Is there a networking opportunity with the Sifflet team at Big Data Paris?

Yes, we’re hosting an exclusive after-party at our booth on October 15! Come join us for great conversations, a champagne toast, and a chance to connect with data leaders who care about data governance, pipeline health, and building resilient systems.

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

Data lineage is a visual map that shows how data flows from its source through transformations to its final destination, like dashboards or ML models. It's essential for data teams because it enables faster root cause analysis, improves data trust, and supports smarter change management. When paired with a data observability platform like Sifflet, lineage becomes a powerful tool for tracking data quality and ensuring SLA compliance.

What strategies can help smaller data teams stay productive and happy?

For smaller teams, simplicity and clarity are key. Implementing lightweight data observability dashboards and using tools that support real-time alerts and Slack notifications can help them stay agile without feeling overwhelmed. Also, defining clear roles and giving access to self-service tools boosts autonomy and satisfaction.

How does Sifflet help reduce AI bias and improve model fairness?

Reducing AI bias starts with understanding your data. Sifflet’s observability platform gives you deep visibility into data sources, transformations, and quality. By tracking data lineage and applying data profiling, teams can identify and correct biased inputs before they affect model outcomes. This transparency helps build more ethical and reliable AI systems.

What role does technology play in supporting data team well-being?

The right technology can make a big difference. Adopting observability tools that offer features like data lineage tracking, data freshness checks, and pipeline health dashboards can reduce manual firefighting and help your team work more autonomously. This not only improves productivity but also makes day-to-day work more enjoyable.

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