Take Charge. Trace %%Anything.%%

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

Sifflet dashboard features overview

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
Sifflet dashboard features overview
UNDERSTAND

Full Context At Your Fingertips

Everything you need to get to resolution, faster.

  • Asset Health Status
  • Documentation
Sifflet dashboard features overview
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
Sifflet dashboard features overview

Reinforced %%Reliability%%

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

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.

Read more

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.

Read more

Data Users

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

Read more

%%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.

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 ?
Contact Us

Frequently asked questions

What can I expect from Sifflet at Big Data Paris 2024?
We're so excited to welcome you at Booth #D15 on October 15 and 16! You’ll get to experience live demos of our latest data observability features, hear real client stories like Saint-Gobain’s, and explore how Sifflet helps improve data reliability and streamline data pipeline monitoring.
When should I consider using a point solution like Anomalo or Bigeye instead of a full observability platform?
If your team has a narrow focus on anomaly detection or prefers a SQL-first, hands-on approach to monitoring, tools like Anomalo or Bigeye can be great fits. However, for broader needs like data governance, business impact analysis, and cross-functional collaboration, a platform like Sifflet offers more comprehensive data observability.
How does Sifflet help close the observability gap for Airbyte pipelines?
Great question! Sifflet bridges the observability gap for Airbyte by using our Declarative Lineage API and a custom Python script. This allows you to capture complete data lineage from Airbyte and ingest it into Sifflet, giving you full visibility into your pipelines and enabling better root cause analysis and data quality monitoring.
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.
How does Sifflet help reduce alert fatigue in data observability?
Sifflet uses AI-driven context and dynamic thresholding to prioritize alerts based on impact and relevance. Its intelligent alerting system ensures users only get notified when it truly matters, helping reduce alert fatigue and enabling faster, more focused incident response.
What does a modern data stack look like and why does it matter?
A modern data stack typically includes tools for ingestion, warehousing, transformation and business intelligence. For example, you might use Fivetran for ingestion, Snowflake for warehousing, dbt for transformation and Looker for analytics. Investing in the right observability tools across this stack is key to maintaining data reliability and enabling real-time metrics that support smart, data-driven decisions.
How can I monitor the health of my ingestion pipelines?
To keep your ingestion pipelines healthy, it's best to use observability tools that offer features like pipeline health dashboards, data quality monitoring, and anomaly detection. These tools provide visibility into data flow, alert you to schema drift, and help with root cause analysis when issues arise.
How did Adaptavist reduce data downtime with Sifflet?
Adaptavist used Sifflet’s observability platform to map the blast radius of changes, alert users before issues occurred, and validate results pre-production. This proactive approach to data pipeline monitoring helped them eliminate downtime during a major refactor and shift from 'merge and pray' to a risk-aware, observability-first workflow.