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

Can I see how a business metric is calculated in Sifflet?

Absolutely! With Sifflet’s data lineage tracking, users can view the full column-level lineage from ingestion to consumption. This transparency helps users understand how each metric is computed and how it relates to other data or metrics in the pipeline.

How does data observability complement a data catalog?

While a data catalog helps you find and understand your data, data observability ensures that the data you find is actually reliable. Observability tools like Sifflet monitor the health of your data pipelines in real time, using features like data freshness checks, anomaly detection, and data quality monitoring. Together, they give you both visibility and trust in your data.

What role does Sifflet play in Etam’s data governance efforts?

Sifflet supports Etam by embedding data governance into their workflows through automated monitoring, anomaly detection, and data lineage tracking. This gives the team better visibility into their data pipelines and helps them troubleshoot issues quickly without slowing down innovation.

Is this feature scalable for large datasets and multiple data assets?

Yes, it is! With Sifflet’s auto-coverage and observability tools, you can monitor distribution deviation at scale with just a few clicks. Whether you're working with batch data observability or streaming data monitoring, Sifflet has you covered with automated, scalable insights.

Why might Metaplane fall short for teams with complex data environments?

Metaplane is great for small teams and dbt-centric workflows, but it lacks depth in areas like infrastructure observability, field-level lineage, and ML model monitoring. As your stack grows to include streaming data, hybrid cloud, or multiple orchestration tools, you’ll need a more robust observability platform to maintain data quality and SLA compliance.

Why is data quality such a critical part of a data governance strategy?

Great question! Data quality is one of the foundational pillars of a strong data governance strategy because it directly impacts decision-making, compliance, and trust in your data. Poor data quality can lead to biased AI models, flawed analytics, and even regulatory risk. That's why integrating data quality monitoring early in your data lifecycle is key to building a reliable and responsible data foundation.

How does Sifflet stand out among other data observability tools?

Sifflet takes a unique approach by addressing data reliability as both an engineering and business challenge. Our observability platform offers end-to-end coverage, business context, and a collaboration layer that aligns technical teams with strategic outcomes, making it easier to maintain analytics and AI-ready data.

How does the updated lineage graph help with root cause analysis?

By merging dbt model nodes with dataset nodes, our streamlined lineage graph removes clutter and highlights what really matters. This cleaner view enhances root cause analysis by letting you quickly trace issues back to their source with fewer distractions and more context.

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