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.

‍

Learn More

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

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.

Let's chat

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 role did data observability play in improving Meero's data reliability?

Data observability was key to Meero's success in maintaining reliable data pipelines. By using Sifflet’s observability platform, they could monitor data freshness, schema changes, and volume anomalies, ensuring their data remained trustworthy and accurate for business decision-making.

Why is data observability so important for AI-powered organizations in 2025?

Great question! As AI continues to evolve, the quality and reliability of the data feeding those models becomes even more critical. Data observability ensures that your AI systems are powered by clean, accurate, and up-to-date data. With platforms like Sifflet, organizations can detect issues like data drift, monitor real-time metrics, and maintain data governance, all of which help AI models stay accurate and trustworthy.

What is data observability and why is it important for modern data teams?

Data observability is the ability to monitor and understand the health of your data across the entire data stack. As data pipelines become more complex, having real-time visibility into where and why data issues occur helps teams maintain data reliability and trust. At Sifflet, we believe data observability is essential for proactive data quality monitoring and faster root cause analysis.

How does data profiling support GDPR compliance efforts?

Data profiling helps by automatically identifying and tagging personal data across your systems. This is vital for GDPR, where you need to know exactly what PII you have and where it's stored. Combined with data quality monitoring and metadata discovery, profiling makes it easier to manage consent, enforce data contracts, and ensure data security compliance.

How does data lineage tracking help with root cause analysis in data integration?

Data lineage tracking gives visibility into how data flows from source to destination, making it easier to pinpoint where issues originate. This is essential for root cause analysis, especially when dealing with complex integrations across multiple systems. At Sifflet, we see data lineage as a cornerstone of any observability platform.

What’s the main difference between ETL and ELT?

Great question! While both ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) are data integration methods, the key difference lies in the order of operations. ETL transforms data before loading it into a data warehouse, whereas ELT loads raw data first and transforms it inside the warehouse. ELT has become more popular with the rise of cloud data warehouses like Snowflake and BigQuery, which offer scalable storage and computing power. If you're working with large volumes of data, ELT might be the better fit for your data pipeline monitoring strategy.

Why is data quality monitoring so important for data-driven decision-making, especially in uncertain times?

Great question! Data quality monitoring helps ensure that the data you're relying on is accurate, timely and complete. In high-stress or uncertain situations, poor data can lead to poor decisions. By implementing scalable data quality monitoring, including anomaly detection and data freshness checks, you can avoid the 'garbage in, garbage out' problem and make confident, informed decisions.

How does data observability differ from traditional data quality monitoring?

Great question! While data quality monitoring focuses on alerting teams when data deviates from expected parameters, data observability goes further by providing context through data lineage tracking, real-time metrics, and root cause analysis. This holistic view helps teams not only detect issues but also understand and fix them faster, making it a more proactive approach.

Read all FAQs

Still have questions?

Built for Scale: How Sifflet outperforms Metaplane

Augmented data quality for analytics and AI

✅ AI-Powered, Context Rich Prevention

⚡ True End-to-End Lineage

🔕 Rich Metadata & Catalog

🚀 Built for Enterprise Collaboration

Don't Solve Half the Problem.

There's no one size fits all.

Frequently asked questions