COMPARISON

Built for Scale: How Sifflet outperforms Metaplane

Sifflet offers a more complete and scalable approach to data observability than Metaplane, built for the needs of modern enterprises—not just lean, dbt-centric teams. With deeper lineage, smarter automation, and broader team support, Sifflet helps organizations turn data trust into business impact.

THE BIG PICTURE

Augmented data quality for analytics and AI

Metaplane covers the basics of technical data quality: freshness, volume, and anomaly detection, mainly for dbt-centric teams. Sifflet goes further, layering rich metadata, lineage, and cataloging to give full visibility and faster resolution across complex data environments.

Built for scale, Sifflet supports both technical and business users with AI-powered automation, broad integrations, and an adaptive UX. It’s observability that drives trust, governance, and business value, not just detection.

Don't Solve Half the Problem.

If you want to tackle data quality just from a technical perspective, Sifflet isn’t for you. But if you want to reach augmented data quality for analytics and AI that truly brings business value to downstream users, Sifflet is the right choice for today… and tomorrow.

Metaplane
Monitoring Coverage

OOTB monitors + SQL logic + NLP monitor wizard; scales across complex environments

Freshness, volume, null checks; dbt-aware

Root Cause Analysis (RCA)

Automated RCA with health-aware lineage and pipeline insights

Manual triage with limited lineage context

Lineage

End-to-end lineage from ingestion to BI, with health overlays

dbt metadata or warehouse schema-based; partial

Catalog & Metadata

Full catalog with glossary, usage tracking, and business context

No built-in catalog; limited metadata visualization

Alerting & Surfacing

Alerts surface across tools—including BI dashboards via Chrome extension

Slack and email alerts

User Experience & Scalability

Adaptive UX for both technical and business users; built for large, decentralized orgs

Simple UI, CLI, fast setup; built for dbt-native, lean teams

Integrations

Wide coverage across orchestration, warehouse, modeling, and BI tools

Strong in dbt and warehouse tools; limited elsewhere

There's no one size fits all.

When it comes to data observability platforms, there's no one size fits all.
Chat with one of our experts today to learn more about Sifflet and if it's the right option for you.

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

Frequently asked questions

What makes Sifflet's approach to data quality unique?
At Sifflet, we believe data quality isn't one-size-fits-all. Our observability platform blends technical robustness with business context, offering customized data quality monitoring that adapts to your specific use cases. This means you get both reliable pipelines and meaningful metrics that align with your business goals.
What role does data lineage tracking play in managing complex dbt pipelines?
Data lineage tracking is essential when your dbt projects grow in size and complexity. Sifflet provides a unified, metadata-rich lineage graph that spans your entire data stack, helping you quickly perform root cause analysis and impact assessments. This visibility is crucial for maintaining trust and transparency in your data pipelines.
What makes debugging data pipelines so time-consuming, and how can observability help?
Debugging complex pipelines without the right tools can feel like finding a needle in a haystack. A data observability platform simplifies root cause analysis by providing detailed telemetry and pipeline health dashboards, so you can quickly identify where things went wrong and fix them faster.
Can Sifflet extend the capabilities of dbt tests for better observability?
Absolutely! While dbt tests are a great starting point, Sifflet takes things further with advanced observability tools. By ingesting dbt tests into Sifflet, you can apply powerful features like dynamic thresholding, real-time alerts, and incident response automation. It’s a big step up in data reliability and SLA compliance.
How is Etam using data observability to support its 2025 strategy?
Etam is leveraging data observability as a foundational element of its 2025 data strategy. With Sifflet’s observability platform, the team can monitor data quality, detect issues early, and ensure data reliability, which helps them move faster and with more confidence across the business.
Why is full-stack visibility important in data pipelines?
Full-stack visibility is key to understanding how data moves across your systems. With a data observability tool, you get data lineage tracking and metadata insights, which help you pinpoint bottlenecks, track dependencies, and ensure your data is accurate from source to destination.
What improvements has Sifflet made to incident management workflows?
We’ve introduced Augmented Resolution to help teams group related alerts into a single collaborative ticket, streamlining incident response. Plus, with integrations into your ticketing systems, Sifflet ensures that data issues are tracked, communicated, and resolved efficiently. It’s all part of our mission to boost data reliability and support your operational intelligence.
What makes Sifflet’s Data Catalog different from built-in catalogs like Snowsight or Unity Catalog?
Unlike tool-specific catalogs, Sifflet serves as a 'Catalog of Catalogs.' It brings together metadata from across your entire data ecosystem, providing a single source of truth for data lineage tracking, asset discovery, and SLA compliance.
Still have questions?