COMPARISON

Sifflet is better for business

The best data leaders know that data quality isn’t just a technical issue. They want tooling that allows technical and business users to engage with data observability. That’s why teams are switching from Monte Carlo to Sifflet.

THE BIG PICTURE

Augmented data quality for analytics and AI

Monte Carlo focuses on technical data quality, looking at freshness, accuracy, nulls, voids and more. Sifflet does this too, but it goes one step further—pulling in documentation and business context with input from downstream users to understand business critical data products and processes.

The result is truly augmented data quality for analytics and AI, because technical teams know exactly what to prioritize when it comes to monitoring and triaging.

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.

Monte Carlo
Programmatic & Infrastructure as Code Capabilities
No Code Options/ Intuitive UI
Automated Machine Learning of Data Environments
Business Impact Prioritized Alerts
AI-Powered Monitoring Wizard for Monitor Setup
Programmatic Monitoring (SQL based Monitoring, Monitoring-as-Code)
Dynamic Context Engine for RCA
Downstream Impact Analysis
Proactive Data Quality Mitigation 

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 can data observability help improve the happiness of my data team?
Great question! A strong data observability platform helps reduce uncertainty in your data pipelines by providing transparency, real-time metrics, and proactive anomaly detection. When your team can trust the data and quickly identify issues, they feel more confident, empowered, and less stressed, which directly boosts team morale and satisfaction.
What does a modern data stack look like and why does it matter?
A modern data stack typically includes tools for ingestion, warehousing, transformation and business intelligence. For example, you might use Fivetran for ingestion, Snowflake for warehousing, dbt for transformation and Looker for analytics. Investing in the right observability tools across this stack is key to maintaining data reliability and enabling real-time metrics that support smart, data-driven decisions.
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.
What trends in data observability should we watch for in 2025?
In 2025, expect to see more focus on AI-driven anomaly detection, dynamic thresholding, and predictive analytics monitoring. Staying ahead means experimenting with new observability tools, engaging with peers, and continuously aligning your data strategy with evolving business needs.
What new dbt metadata can I now see in Sifflet?
You’ll now find key dbt metadata like the last execution timestamp and status directly within the dataset catalog and asset pages. This makes real-time metrics and pipeline health monitoring more accessible and actionable across your observability platform.
How does Sifflet make data observability more accessible to BI users?
Great question! At Sifflet, we're committed to making data observability insights available right where you work. That’s why we’ve expanded beyond our Chrome extension to integrate directly with popular Data Catalogs like Atlan, Alation, Castor, and Data Galaxy. This means BI users can access real-time metrics and data quality insights without ever leaving their workflow.
What kind of health scoring does Adaptavist use for their data assets?
Adaptavist built a platform health dashboard that scores each asset based on data freshness, quality, and reliability. This kind of data profiling helps them prioritize fixes, improve root cause analysis, and ensure continued trust in their analytics pipeline observability.
What kind of data quality monitoring features does Sifflet Insights offer?
Sifflet Insights offers features like real-time alerts, incident tracking, and access to metadata through your Data Catalog. These capabilities support proactive data quality monitoring and streamline root cause analysis when issues arise.
Still have questions?