Solution by Use Case

Cost Observability

Cost-efficient data pipelines

Pinpoint cost inefficiencies and anomalies thanks to full-stack data observability.

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Data asset optimization

Leverage lineage and Data Catalog to pinpoint underutilized assets
Get alerted on unexpected behaviors in data consumption patterns

Proactive data pipeline management

Proactively prevent pipelines from running in case a data quality anomaly is detected

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 did Shippeo decide to invest in a data observability solution like Sifflet?

As Shippeo scaled, they faced silent data leaks, inconsistent metrics, and data quality issues that impacted billing and reporting. By adopting Sifflet, they gained visibility into their data pipelines and could proactively detect and fix problems before they reached end users.

Why is a user-friendly interface important in an observability tool?

A user-friendly interface boosts adoption across teams and makes it easier to navigate complex datasets. For observability tools, especially those focused on data cataloging and data discovery, a clean UI enables faster insights and more efficient collaboration.

What makes Sifflet stand out among the best data observability tools in 2025?

Great question! Sifflet shines because it treats data observability as both an engineering and a business challenge. Our platform offers full end-to-end coverage, strong business context, and a collaboration layer that helps teams resolve issues faster. Plus, with enterprise-grade security and scalability, Sifflet is built to grow with your data needs.

How does Sifflet help with SLA compliance and incident response?

Sifflet supports SLA compliance by offering intelligent alerting, dynamic thresholding, and real-time dashboards that track incident metrics and resolution times. Its data reliability dashboard gives teams visibility into SLA adherence and helps prioritize issues based on business impact, streamlining incident management workflows and reducing mean time to resolution.

How does Sifflet support data lineage tracking across tools like Snowflake and dbt?

Sifflet provides end-to-end data lineage tracking that connects your tables to dbt models, semantic layers, and BI dashboards. This visibility helps you understand the full impact of any metadata change, ensuring data quality monitoring and reducing the risk of breaking critical business KPIs.

What are some common reasons data freshness breaks down in a pipeline?

Freshness issues often start with delays in source systems, ingestion bottlenecks, slow transformation jobs, or even caching problems in dashboards. That's why a strong observability platform needs to monitor every stage of the pipeline, from ingestion latency to delivery, to ensure data reliability and timely decision-making.

Will dbt Impact Analysis be available for other version control tools?

Yes! While it currently supports GitHub and GitLab, Sifflet is actively working on bringing dbt Impact Analysis to Bitbucket. This expansion ensures broader coverage and supports more teams in achieving better data governance and observability.

What makes a metadata catalog different from a traditional data catalog?

Great question! A metadata catalog goes beyond just listing data assets. It enriches technical metadata with business context like ownership, definitions, and data quality scores. This makes it easier for users to trust what they find, and it supports advanced features like data lineage tracking, data freshness checks, and automated impact analysis. It's a big leap forward in data discovery and governance.

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