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

How does Etam ensure pipeline health while scaling its data operations?
Etam uses observability tools like Sifflet to maintain a healthy data pipeline. By continuously monitoring real-time metrics and setting up proactive alerts, they can catch issues early and ensure their data remains trustworthy as they scale operations.
Why does great design matter in data observability platforms?
Great design is essential in data observability platforms because it helps users navigate complex workflows with ease and confidence. At Sifflet, we believe that combining intuitive UX with a visually consistent UI empowers Data Engineers and Analysts to monitor data quality, detect anomalies, and ensure SLA compliance more efficiently.
What is data lineage and why does it matter for modern data teams?
Data lineage is the process of mapping the journey of data from its origin to its final destination, including all the transformations it undergoes. It's essential for data pipeline monitoring and root cause analysis because it helps teams quickly identify where data issues originate, saving time and reducing stress under pressure.
How does a unified data observability platform like Sifflet help reduce chaos in data management?
Great question! At Sifflet, we believe that bringing together data cataloging, data quality monitoring, and lineage tracking into a single observability platform helps reduce Data Entropy and streamline how teams manage and trust their data. By centralizing these capabilities, users can quickly discover assets, monitor their health, and troubleshoot issues without switching tools.
What is the Universal Connector and how does it support data pipeline monitoring?
The Universal Connector lets you integrate Sifflet with any tool in your stack using YAML and API endpoints. It enables full-stack data pipeline monitoring and data lineage tracking, even for tools Sifflet doesn’t natively support, offering a more complete view of your observability workflows.
Why is stakeholder trust in data so important, and how can we protect it?
Stakeholder trust is crucial because inconsistent or unreliable data can lead to poor decisions and reduced adoption of data-driven practices. You can protect this trust with strong data quality monitoring, real-time metrics, and consistent reporting. Data observability tools help by alerting teams to issues before they impact dashboards or reports, ensuring transparency and reliability.
Why is data categorization important for data governance and compliance?
Effective data categorization is essential for data governance and compliance because it helps identify sensitive data like PII, ensuring the correct protection policies are applied. With Sifflet’s classification tags, governance teams can easily locate and safeguard sensitive information, supporting GDPR data monitoring and overall data security compliance.
What kind of visibility does Sifflet provide for Airflow DAGs?
Sifflet offers a clear view of DAG run statuses and their potential impact on the rest of your data pipeline. Combined with data lineage tracking, it gives you full transparency, making root cause analysis and incident response much easier.
Still have questions?