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 makes data observability different from traditional monitoring tools?

Traditional monitoring tools focus on infrastructure and application performance, while data observability digs into the health and trustworthiness of your data itself. At Sifflet, we combine metadata monitoring, data profiling, and log analysis to provide deep insights into pipeline health, data freshness checks, and anomaly detection. It's about ensuring your data is accurate, timely, and reliable across the entire stack.

What are the key components of an end-to-end data platform?

An end-to-end data platform includes layers for ingestion, storage, transformation, orchestration, governance, observability, and analytics. Each part plays a role in making data reliable and actionable. For example, data lineage tracking and real-time metrics collection help ensure transparency and performance across the pipeline.

What is data volume and why is it so important to monitor?

Data volume refers to the quantity of data flowing through your pipelines. Monitoring it is critical because sudden drops, spikes, or duplicates can quietly break downstream logic and lead to incomplete analysis or compliance risks. With proper data volume monitoring in place, you can catch these anomalies early and ensure data reliability across your organization.

How can data teams prioritize what to monitor in complex environments?

Not all data is created equal, so it's important to focus data quality monitoring efforts on the assets that drive business outcomes. That means identifying key dashboards, critical metrics, and high-impact models, then using tools like pipeline health dashboards and SLA monitoring to keep them reliable and fresh.

How does Sifflet’s observability platform help reduce alert fatigue?

We hear this a lot — too many alerts, not enough clarity. At Sifflet, we focus on intelligent alerting by combining metadata, data lineage tracking, and usage patterns to prioritize what really matters. Instead of just flagging that something broke, our platform tells you who’s affected, why it matters, and how to fix it. That means fewer false positives and more actionable insights, helping you cut through the noise and focus on what truly impacts your business.

How does integrating a data catalog with observability tools improve pipeline monitoring?

When integrated with observability tools, a data catalog becomes more than documentation. It provides real-time metrics, data freshness checks, and anomaly detection, allowing teams to proactively monitor pipeline health and quickly respond to issues. This integration enables faster root cause analysis and more reliable data delivery.

What types of metadata are captured in a modern data catalog?

Modern data catalogs capture four key types of metadata: technical (schemas, formats), business (definitions, KPIs), operational (usage patterns, SLA compliance), and governance (access controls, data classifications). These layers work together to support data quality monitoring and transparency in data pipelines.

What role does Sifflet play in Etam’s data governance efforts?

Sifflet supports Etam by embedding data governance into their workflows through automated monitoring, anomaly detection, and data lineage tracking. This gives the team better visibility into their data pipelines and helps them troubleshoot issues quickly without slowing down innovation.

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