Product

Data Lineage

Take Charge. Trace %%Anything.%%

Map out the relationships between your data assets, find what’s broken upstream and prevent downstream impacts with advanced lineage.

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Master Mapping

Map your asset dependencies from end-to-end. Modern UI makes it easy to look at lineage at both the table and column level.

Find Root Causes Fast

When data breaks, you need to know why and where. Sifflet’s lineage capabilities help you get to the root cause fast.

Take Care of Business

Assess the impact of data quality issues and prevent downstream trouble before it happens.

OVERSEE

Precision Mapping

See how data moves through your system from its origin to final destination and all the stops in between.

Map your data dependencies on day one with OOTB lineage enabled by integrations & SQL history parsing
Benefit from last mile dependencies mapping with declarative lineage
Work with column level granularity

UNDERSTAND

Full Context At Your Fingertips

Everything you need to get to resolution, faster.

Asset Health Status
Documentation

EXPLORE

Effortless Navigation & Exports

Investigate, collaborate and share your lineage.

Navigate lineage effortlessly by folding and unfolding your map
Screengrab lineage
Export your lineage as a CSV

Reinforced %%Reliability%%

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

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Data Engineers

With Sifflet’s lineage, get up to 50% of the time you spend on mundane reliability tasks back and gain insight into your data across the entire lifecycle.

Data Leaders

Reduce data downtime and help the whole company benefit from better data quality by ensuring your teams can get to the bottom of root causes, faster.

Data Users

Understand where your data comes from to make informed decisions and break down silos between teams.

Looking for more?

sifflet a guide to assessing your need for a data observability

Insight

A Guide to Assessing your Need for Data Observability

Obeservability 101

Troubleshoot Faster with Data Observability

Obeservability 101

End-To-End Data Observability With Declarative Assets and Lineage

%%Improve%% Data Quality Rapidly

Sifflet’s lineage features help you break silos between teams and get to the bottom of root causes, so the whole company benefits from better data quality.

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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 Sifflet's approach to data quality unique?

At Sifflet, we believe data quality isn't one-size-fits-all. Our observability platform blends technical robustness with business context, offering customized data quality monitoring that adapts to your specific use cases. This means you get both reliable pipelines and meaningful metrics that align with your business goals.

Can Sifflet help reduce false positives during holidays or special events?

Absolutely! We know that data patterns can shift during holidays or unique business dates. That’s why Sifflet now lets you exclude these dates from alerts by selecting from common calendars or customizing your own. This helps reduce alert fatigue and improves the accuracy of anomaly detection across your data pipelines.

What does it mean to treat data as a product?

Treating data as a product means managing data with the same care and strategy as a traditional product. It involves packaging, maintaining, and delivering high-quality data that serves a specific purpose or audience. This approach improves data reliability and makes it easier to monetize or use for strategic decision-making.

How can data observability support a strong data governance strategy?

Data observability complements data governance by continuously monitoring data pipelines for issues like data drift, freshness problems, or anomalies. With an observability platform like Sifflet, teams can proactively detect and resolve data quality issues, enforce data validation rules, and gain visibility into pipeline health. This real-time insight helps governance policies work in practice, not just on paper.

How did Dailymotion use data observability to support their shift to a product-oriented data platform?

Dailymotion embedded data observability into their data ecosystem to ensure trust, reliability, and discoverability across teams. This shift allowed them to move from ad hoc data requests to delivering scalable, analytics-driven data products that empower both engineers and business users.

Is there a data observability platform that supports both business and technical users?

Yes, Sifflet is designed to be accessible for both business stakeholders and data engineers. It offers intuitive interfaces for no-code monitor creation, context-rich alerts, and field-level data lineage tracking. This democratizes data quality monitoring and helps teams across the organization stay aligned on data health and pipeline performance.

What is data observability and why is it important for modern data teams?

Data observability is the ability to monitor, understand, and troubleshoot data health across the entire data stack. It's essential for modern data teams because it helps ensure data reliability, improves trust in analytics, and prevents costly issues caused by broken data pipelines or inaccurate dashboards. With the rise of complex infrastructures and real-time data usage, having a strong observability platform in place is no longer optional.

How does data ingestion relate to data observability?

Great question! Data ingestion is where observability starts. Once data enters your system, observability platforms like Sifflet help monitor its quality, detect anomalies, and ensure data freshness. This allows teams to catch ingestion issues early, maintain SLA compliance, and build trust in their data pipelines.

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