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 a more inclusive data observability platform compared to Monte Carlo?

Sifflet is designed for both technical and non-technical users, offering no-code monitors, natural-language setup, and cross-persona alerts. This means analysts, data scientists, and executives can all engage with data quality monitoring without needing engineering support, making it a truly inclusive observability platform.

Can observability tools help with GDPR-related incident response?

Absolutely! Observability tools can support GDPR compliance by enabling faster incident response automation. If there's a data breach, you need to notify users and authorities within 72 hours. Real-time alerts, telemetry instrumentation, and logs management help your team detect issues quickly, understand the impact, and take action to stay compliant.

Why is data observability gaining momentum now, even though software observability has been around for a while?

Great question! Software observability took off in the 2010s with the rise of cloud-native apps, but data observability is catching up fast. As businesses start treating data as a mission-critical asset—especially with the growth of AI and cloud data platforms like Snowflake—the need for real-time visibility, data reliability, and governance has become urgent. We're in the early innings, but the pace is accelerating quickly.

How does data observability fit into the modern data stack?

Data observability integrates across your existing data stack, from ingestion tools like Airflow and AWS Glue to storage solutions like Snowflake and Redshift. It acts as a monitoring layer that provides real-time insights and alerts across each stage, helping teams maintain pipeline health and ensure data freshness checks are always in place.

How does data observability improve incident response and SLA compliance?

With data observability, teams get real-time metrics and deep context around data issues. This means faster incident response and better SLA compliance. Sifflet’s observability platform helps you pinpoint root causes quickly, reducing downtime and giving stakeholders confidence in the reliability of your data.

How does data observability support better data quality management?

Data observability plays a key role by giving teams real-time visibility into the health of their data pipelines. With observability tools like Sifflet, you can monitor data freshness, detect anomalies, and trace issues back to their root cause. This allows you to catch and fix data quality issues before they impact business decisions, making your data more reliable and your operations more efficient.

What is data distribution deviation and why should I care about it?

Data distribution deviation happens when the distribution of your data changes over time, either gradually or suddenly. This can lead to serious issues like data drift, broken queries, and misleading business metrics. With Sifflet's data observability platform, you can automatically monitor for these deviations and catch problems before they impact your decisions.

What trends are driving the demand for centralized data observability platforms?

The growing complexity of data products, especially with AI and real-time use cases, is driving the need for centralized data observability platforms. These platforms support proactive monitoring, root cause analysis, and incident response automation, making it easier for teams to maintain data reliability and optimize resource utilization.

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