Product

Data Quality Monitoring

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.

Talk to an Expert

Launch Product Tour

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

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

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)

Reinforced %%Reliability%%

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

Get in touch

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.

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.

Data Leaders

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

Looking for more?

sifflet a guide to assessing your need for a data observability

Insights

A Guide to Assessing your Need for Data Observability

Data Monitoring

Data Quality Monitoring is Dead

Data Monitoring

Data monitoring vs observability solutions

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 ?

Frequently asked questions

How did implementing a data observability platform impact Hypebeast’s operations?

After adopting Sifflet’s observability platform, Hypebeast saw a 204% improvement in data quality, a 178% increase in data product delivery, and a 75% boost in ad hoc request speed. These gains translated into faster, more reliable insights and better collaboration across departments.

How do declared assets improve data quality monitoring?

Declared assets appear in your Data Catalog just like built-in assets, with full metadata and business context. This improves data quality monitoring by making it easier to track data lineage, perform data freshness checks, and ensure SLA compliance across your entire pipeline.

What are some key features to look for in an observability platform for data?

A strong observability platform should offer data lineage tracking, real-time metrics, anomaly detection, and data freshness checks. It should also integrate with your existing tools like Airflow or Snowflake, and support alerting through Slack or webhook integrations. These capabilities help teams monitor data pipelines effectively and respond quickly to issues.

How does SQL Table Tracer handle complex SQL features like CTEs and subqueries?

SQL Table Tracer uses a Monoid-based design to handle complex SQL structures like Common Table Expressions (CTEs) and subqueries. This approach allows it to incrementally and safely compose lineage information, ensuring accurate root cause analysis and data drift detection.

How can poor data distribution impact machine learning models?

When data distribution shifts unexpectedly, it can throw off the assumptions your ML models are trained on. For example, if a new payment processor causes 70% of transactions to fall under $5, a fraud detection model might start flagging legitimate behavior as suspicious. That's why real-time metrics and anomaly detection are so crucial for ML model monitoring within a good data observability framework.

What’s the difference between batch ingestion and real-time ingestion?

Batch ingestion processes data in chunks at scheduled intervals, making it ideal for non-urgent tasks like overnight reporting. Real-time ingestion, on the other hand, handles streaming data as it arrives, which is perfect for use cases like fraud detection or live dashboards. If you're focused on streaming data monitoring or real-time alerts, real-time ingestion is the way to go.

Why is aligning data initiatives with business objectives important for Etam?

At Etam, every data project begins with the question, 'How does this help us reach our OKRs?' This alignment ensures that data initiatives are directly tied to business impact, improving sponsorship and fostering collaboration across departments. It's a great example of business-aligned data strategy in action.

Can data observability support better demand forecasting for retailers?

Absolutely. By integrating historical sales, real-time transactions, and external data sources like weather or social trends, data observability platforms enhance forecast accuracy. They use machine learning to evaluate and adjust predictions, helping retailers align inventory with actual consumer demand more effectively.

Read all FAQs