Solution by Persona

Technical Teams

Data Engineer

You’ll be the boss. Sifflet gives you the capabilities and oversight to manage your data stack like never before, faster than you ever thought possible.

Launch Product Tour

Troubleshoot and Debug

Sifflet makes troubleshooting and debugging faster, more efficient and more effective thanks to pipeline failure or data anomaly alerts and rich contextual information.

Pipeline Performance Optimization

Pipelines power your data stack. Sifflet helps you monitor pipeline performance and get insight into bottlenecks and inefficient transformations.

Quality Assurance

Uplevel your data quality thanks to automated quality checks and validations and custom rules to ensure data integrity.

More Productive. More Powerful.

Sifflet augments your productivity by giving you end-to-end visibility into your architecture, assets, and pipelines. AI-powered monitoring sends you the right alerts, at the right time, so you can triage efficiently and effectively. And advanced lineage capabilities enable you to get to resolution faster.

Built for Business.

Sifflet helps you collaborate better with users on the business end. Give your data consumers self-serve tools, such as smart monitoring setup that leverages large language models and embed monitoring alerts into their data products.

See Value From Day One.

Sifflet connects to hundreds of tools already in your stack and offers out of the box monitors and tooling so you can start seeing value from day one.

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

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Frequently asked questions

How does the new Custom Metadata feature improve data governance?

With Custom Metadata, you can tag any asset, monitor, or domain in Sifflet using flexible key-value pairs. This makes it easier to organize and route data based on your internal logic, whether it's ownership, SLA compliance, or business unit. It's a big step forward for data governance and helps teams surface high-priority monitors more effectively.

What can I expect to learn from Sifflet’s session on cataloging and monitoring data assets?

Our Head of Product, Martin Zerbib, will walk you through how Sifflet enables data lineage tracking, real-time metrics, and data profiling at scale. You’ll get a sneak peek at our roadmap and see how we’re making data more accessible and reliable for teams of all sizes.

Why is smart alerting important in data observability?

Smart alerting helps your team focus on what really matters. Instead of flooding your Slack with every minor issue, a good observability tool prioritizes alerts based on business impact and data asset importance. This reduces alert fatigue and ensures the right people get notified at the right time. Look for platforms that offer customizable severity levels, real-time alerts, and integrations with your incident management tools like PagerDuty or email alerts.

How is data volume different from data variety?

Great question! Data volume is about how much data you're receiving, while data variety refers to the different types and formats of data sources. For example, a sudden drop in appointment data is a volume issue, while a new file format causing schema mismatches is a variety issue. Observability tools help you monitor both dimensions to maintain healthy pipelines.

What are some common signs of a data distribution issue?

Some red flags include missing categories, unusual clustering of values, unexpected outliers, or uneven splits that don’t align with business logic. These issues often sneak past volume or schema checks, which is why proactive data quality monitoring and data profiling are so important for catching them early.

How does data quality monitoring help improve data reliability?

Data quality monitoring is essential for maintaining trust in your data. A strong observability platform should offer features like anomaly detection, data profiling, and data validation rules. These tools help identify issues early, so you can fix them before they impact downstream analytics. It’s all about making sure your data is accurate, timely, and reliable.

How does Etam ensure pipeline health while scaling its data operations?

Etam uses observability tools like Sifflet to maintain a healthy data pipeline. By continuously monitoring real-time metrics and setting up proactive alerts, they can catch issues early and ensure their data remains trustworthy as they scale operations.

How does Sifflet enhance data governance for my organization?

Sifflet supports data governance by allowing you to classify assets with tags and labels, define business terms in a shared glossary, and track data lineage. These features help ensure consistent definitions and safe handling of sensitive data across your stack.

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