Lean. Mean. Monitoring %%Machine.%% 

Finally, dynamic monitoring that can keep up with your stack. AI features optimize your coverage and minimize noise, detecting issues before they arise. 

Sifflet dashboard features overview

Customize to Your Heart’s Content

Sifflet offers both a robust library of out of the box monitors and customization capability. Your teams decide what needs monitoring and how to set it up. 

Bye-Bye, Alert Fatigue

Data engineers don’t need more alerts, they need smarter alerts. Our AI learns adaptively as it goes to optimize coverage and minimize noise.  

Hello, Data Reliability 

Data reliability is reinforced with less manual work for technical teams, faster response times, and overall stronger performance. 

IMPLEMENT

Ready-to-Go Monitors 

Quick set up and implementation means quicker results. 

  • See value instantly with pre-defined templates to check data at field and table levels
  • Help your business users and technical teams meet their quality and reliability objectives thanks to ready-to-go monitors
Sifflet dashboard overview
SUPERVISE

Lifecycle Monitoring

End-to-end coverage that never sleeps. 

  • Detect anomalies continuously thanks to ML models 
  • Give your business users ownership over monitors through LLM monitoring setup 
  • Maintain control and accuracy with optional manual setup and user feedback
Sifflet dashboard features overview
MAINTAIN

Scalability & Optimization

Monitoring that’s easy to maintain and coverage that’s just right.

  • Optimize monitoring coverage and minimize noise levels with AI-powered suggestions and supervision
  • Implement programmatic monitoring set up and maintenance with Data Quality as Code (DQaC)
Sifflet dashboard overview

Reinforced %%Reliability%%

Sifflet’s monitoring features reinforce data reliability for all users, so business can deliver.

Data Users

Stop working with corrupt data. Sifflet embeds alerts in your dashboards, so you know exactly when there’s an incident or issue. And you can set up data monitors on your own.

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Data Engineers

No more scaling monitors manually. Sifflet’s ML will optimize coverage for you, so you can be proactive instead of reactive in reducing downtimes.

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Data Leaders

Give your teams the tools they need to reduce monitoring tasks by up to 50% thanks to Sifflet’s monitoring features.

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Data Reliability, %%Reinforced%%

Sifflet’s monitoring features reinforce data reliability for all users, so business can deliver.

Speak with our Experts

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 ?
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Frequently asked questions

How does Sifflet help close the observability gap for Airbyte pipelines?
Great question! Sifflet bridges the observability gap for Airbyte by using our Declarative Lineage API and a custom Python script. This allows you to capture complete data lineage from Airbyte and ingest it into Sifflet, giving you full visibility into your pipelines and enabling better root cause analysis and data quality monitoring.
Why is collaboration important in building a successful observability platform?
Collaboration is key to building a robust observability platform. At Sifflet, our teams work cross-functionally to ensure every part of the platform, from data lineage tracking to real-time metrics collection, aligns with business goals. This teamwork helps us deliver a more comprehensive and user-friendly solution.
Why is data observability important during cloud migration?
Great question! Data observability helps you monitor the health and integrity of your data as it moves to the cloud. By using an observability platform, you can track data lineage, detect anomalies, and validate consistency between environments, which reduces the risk of disruptions and broken pipelines.
What is metrics observability and why does it matter for business users?
Metrics observability helps business users trust and understand the KPIs they rely on by making it easy to trace how metrics are defined, calculated, and connected to other data assets. With Sifflet’s observability platform, teams can ensure their business metrics are accurate, reliable, and aligned across departments.
How does Sifflet enhance data lineage tracking for dbt projects?
Sifflet enriches your data lineage tracking by visually mapping out your dbt models and how they connect across different projects. This is especially useful for teams managing multiple dbt repositories, as Sifflet brings everything together into a clear, centralized lineage view that supports root cause analysis and proactive monitoring.
What kind of visibility does Sifflet provide for Airflow DAGs?
Sifflet offers a clear view of DAG run statuses and their potential impact on the rest of your data pipeline. Combined with data lineage tracking, it gives you full transparency, making root cause analysis and incident response much easier.
Can I monitor my BigQuery data with Sifflet?
Absolutely! Sifflet’s observability tools are fully compatible with Google BigQuery, so you can perform data quality monitoring, data lineage tracking, and anomaly detection right where your data lives.
Can classification tags improve data pipeline monitoring?
Absolutely! By tagging fields like 'Low Cardinality', data teams can quickly identify which fields are best suited for specific monitors. This enables more targeted data pipeline monitoring, making it easier to detect anomalies and maintain SLA compliance across your analytics pipeline.