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.

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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

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Frequently asked questions

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.

How is data freshness different from latency or timeliness?

Great question! While these terms are often used interchangeably, they each mean something different. Data freshness is about how up-to-date your data is. Latency measures the delay from data generation to availability, and timeliness refers to whether that data arrives within expected time windows. Understanding these differences is key to effective data pipeline monitoring and SLA compliance.

What role does data ownership play in data quality monitoring?

Clear data ownership is a game changer for data quality monitoring. When each data product has a defined owner, it’s easier to resolve issues quickly, collaborate across teams, and build a strong data culture that values accountability and trust.

Why is data lineage tracking important in a data catalog solution?

Data lineage tracking is key to understanding how data flows through your systems. It helps teams visualize the origin and transformation of datasets, making root cause analysis and impact assessments much faster. For teams focused on data observability and pipeline health, this feature is a must-have.

How has AI changed the way companies think about data quality monitoring?

AI has definitely raised the stakes. As Salma shared on the Joe Reis Show, executives are being asked to 'do AI,' but many still struggle with broken pipelines. That’s why data quality monitoring and robust data observability are now seen as prerequisites for scaling AI initiatives effectively.

What does 'observability culture' mean at Adaptavist?

For Adaptavist, observability culture means going beyond tools. It's about clear ownership of alerts, integrating data quality monitoring into sprints, and giving stakeholders ways to provide feedback directly in dashboards. They even track observability metrics to continuously improve their own observability practices.

How does Shippeo’s use of data pipeline monitoring enhance internal decision-making?

By enriching and aggregating operational data, Shippeo creates a reliable source of truth that supports product and operations teams. Their pipeline health dashboards and observability tools ensure that internal stakeholders can trust the data driving their decisions.

Can Sifflet support real-time metrics and monitoring for AI pipelines?

Absolutely! While Sifflet’s monitors are typically scheduled, you can run them on demand using our API. This means you can integrate real-time data quality checks into your AI pipelines, ensuring your models are making decisions based on the freshest and most accurate data available. It's a powerful way to keep your AI systems responsive and reliable.

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