Data Observability Platform for Real-Time Anomaly Detection
Data Observability Platform for Real-Time Anomaly Detection
Jun 10, 2025
Maarten Masschelein
Maarten Masschelein
Maarten Masschelein
CEO and Founder at Soda
CEO and Founder at Soda
CEO and Founder at Soda
Table of Contents
Today’s drop is built for data teams who move fast and don’t have time for mystery failures.
We’re launching Soda Data Observability—an AI-powered system for detecting anomalies across all your datasets.
It sets up in under 5 minutes, delivers results immediately (no model long training time required), and works at the scale you’re already operating at.
It’s 70% more accurate than Prophet-based systems.
It runs on billions of rows.
It doesn’t require a lot of config.
And it’s built to help you start right and shift left → Access Now
Why Metrics Observability?
Data testing is great—if you already know what to look for.
But most issues in production aren’t what you expected.
Observability helps you catch:
Unexpected schema changes
Freshness gaps
Partition dropouts
Sudden volume shifts
No manual rules. No complex setup. Just results—fast.
Start Right. Shift Left.
With Soda Metrics Observability, you can:
✅ Set up in under 5 minutes
Just connect Soda, scan your datasets, and turn it on. Done.
✅ Get results immediately
No need to wait for model training. No tuning. You’ll see anomalies on your very first scan.
✅ Backfill up to 1 year
If you have historical data, we’ll use it—automatically. You’ll get instant visibility into past anomalies.
✅ Monitor everything, then harden
Turn on observability for all your datasets, spot weak points, then codify what matters into Soda checks and data contracts.
This is how you go from unknown unknowns → tested expectations.
From brittle pipelines → resilient platforms.
Under the Hood
⚡️ Proprietary anomaly detection
70% more accurate than Prophet-based models. Designed and benchmarked specifically for data quality.
📈 Explainable anomaly visualizations
Each anomaly is shown with expected ranges, impact, and trend history, so you know what changed and why it matters.
🔁 Feedback-aware detection
Mark false positives, confirm true issues—our engine learns from your input.
Already Proven at Scale
We’ve already scanned over 1.1 billion rows in 64 seconds on Databricks.
This system isn’t theoretical. It works, today, in production.
Bonus: Win a $1,000+ Keyboard
Sign up this week and you’ll be entered to win a custom mechanical keyboard built for data engineers.
Company emails count double → Request Access
What’s Next
Tomorrow we will release collaborative data contracts. They bridge the gap between business and engineering.
But for today?
Turn it on. Start right. Shift left.
Read our docs on Data Observability to learn more about the new features.
Today’s drop is built for data teams who move fast and don’t have time for mystery failures.
We’re launching Soda Data Observability—an AI-powered system for detecting anomalies across all your datasets.
It sets up in under 5 minutes, delivers results immediately (no model long training time required), and works at the scale you’re already operating at.
It’s 70% more accurate than Prophet-based systems.
It runs on billions of rows.
It doesn’t require a lot of config.
And it’s built to help you start right and shift left → Access Now
Why Metrics Observability?
Data testing is great—if you already know what to look for.
But most issues in production aren’t what you expected.
Observability helps you catch:
Unexpected schema changes
Freshness gaps
Partition dropouts
Sudden volume shifts
No manual rules. No complex setup. Just results—fast.
Start Right. Shift Left.
With Soda Metrics Observability, you can:
✅ Set up in under 5 minutes
Just connect Soda, scan your datasets, and turn it on. Done.
✅ Get results immediately
No need to wait for model training. No tuning. You’ll see anomalies on your very first scan.
✅ Backfill up to 1 year
If you have historical data, we’ll use it—automatically. You’ll get instant visibility into past anomalies.
✅ Monitor everything, then harden
Turn on observability for all your datasets, spot weak points, then codify what matters into Soda checks and data contracts.
This is how you go from unknown unknowns → tested expectations.
From brittle pipelines → resilient platforms.
Under the Hood
⚡️ Proprietary anomaly detection
70% more accurate than Prophet-based models. Designed and benchmarked specifically for data quality.
📈 Explainable anomaly visualizations
Each anomaly is shown with expected ranges, impact, and trend history, so you know what changed and why it matters.
🔁 Feedback-aware detection
Mark false positives, confirm true issues—our engine learns from your input.
Already Proven at Scale
We’ve already scanned over 1.1 billion rows in 64 seconds on Databricks.
This system isn’t theoretical. It works, today, in production.
Bonus: Win a $1,000+ Keyboard
Sign up this week and you’ll be entered to win a custom mechanical keyboard built for data engineers.
Company emails count double → Request Access
What’s Next
Tomorrow we will release collaborative data contracts. They bridge the gap between business and engineering.
But for today?
Turn it on. Start right. Shift left.
Read our docs on Data Observability to learn more about the new features.
Today’s drop is built for data teams who move fast and don’t have time for mystery failures.
We’re launching Soda Data Observability—an AI-powered system for detecting anomalies across all your datasets.
It sets up in under 5 minutes, delivers results immediately (no model long training time required), and works at the scale you’re already operating at.
It’s 70% more accurate than Prophet-based systems.
It runs on billions of rows.
It doesn’t require a lot of config.
And it’s built to help you start right and shift left → Access Now
Why Metrics Observability?
Data testing is great—if you already know what to look for.
But most issues in production aren’t what you expected.
Observability helps you catch:
Unexpected schema changes
Freshness gaps
Partition dropouts
Sudden volume shifts
No manual rules. No complex setup. Just results—fast.
Start Right. Shift Left.
With Soda Metrics Observability, you can:
✅ Set up in under 5 minutes
Just connect Soda, scan your datasets, and turn it on. Done.
✅ Get results immediately
No need to wait for model training. No tuning. You’ll see anomalies on your very first scan.
✅ Backfill up to 1 year
If you have historical data, we’ll use it—automatically. You’ll get instant visibility into past anomalies.
✅ Monitor everything, then harden
Turn on observability for all your datasets, spot weak points, then codify what matters into Soda checks and data contracts.
This is how you go from unknown unknowns → tested expectations.
From brittle pipelines → resilient platforms.
Under the Hood
⚡️ Proprietary anomaly detection
70% more accurate than Prophet-based models. Designed and benchmarked specifically for data quality.
📈 Explainable anomaly visualizations
Each anomaly is shown with expected ranges, impact, and trend history, so you know what changed and why it matters.
🔁 Feedback-aware detection
Mark false positives, confirm true issues—our engine learns from your input.
Already Proven at Scale
We’ve already scanned over 1.1 billion rows in 64 seconds on Databricks.
This system isn’t theoretical. It works, today, in production.
Bonus: Win a $1,000+ Keyboard
Sign up this week and you’ll be entered to win a custom mechanical keyboard built for data engineers.
Company emails count double → Request Access
What’s Next
Tomorrow we will release collaborative data contracts. They bridge the gap between business and engineering.
But for today?
Turn it on. Start right. Shift left.
Read our docs on Data Observability to learn more about the new features.
Case studies
Trusted by the world’s leading enterprises
Real stories from companies using Soda to keep their data reliable, accurate, and ready for action.
At the end of the day, we don’t want to be in there managing the checks, updating the checks, adding the checks. We just want to go and observe what’s happening, and that’s what Soda is enabling right now.
Sid Srivastava
Director of Data Governance, Quality and MLOps
Investing in data quality is key for cross-functional teams to make accurate, complete decisions with fewer risks and greater returns, using initiatives such as product thinking, data governance, and self-service platforms.
Mario Konschake
Director of Product-Data Platform
Soda has integrated seamlessly into our technology stack and given us the confidence to find, analyze, implement, and resolve data issues through a simple self-serve capability.
Sutaraj Dutta
Data Engineering Manager
Our goal was to deliver high-quality datasets in near real-time, ensuring dashboards reflect live data as it flows in. But beyond solving technical challenges, we wanted to spark a cultural shift - empowering the entire organization to make decisions grounded in accurate, timely data.
Gu Xie
Head of Data Engineering
4.4 of 5
Start trusting your data. Today.
Find, understand, and fix any data quality issue in seconds.
From table to record-level.
Trusted by
Case studies
Trusted by the world’s leading enterprises
Real stories from companies using Soda to keep their data reliable, accurate, and ready for action.
At the end of the day, we don’t want to be in there managing the checks, updating the checks, adding the checks. We just want to go and observe what’s happening, and that’s what Soda is enabling right now.
Sid Srivastava
Director of Data Governance, Quality and MLOps
Investing in data quality is key for cross-functional teams to make accurate, complete decisions with fewer risks and greater returns, using initiatives such as product thinking, data governance, and self-service platforms.
Mario Konschake
Director of Product-Data Platform
Soda has integrated seamlessly into our technology stack and given us the confidence to find, analyze, implement, and resolve data issues through a simple self-serve capability.
Sutaraj Dutta
Data Engineering Manager
Our goal was to deliver high-quality datasets in near real-time, ensuring dashboards reflect live data as it flows in. But beyond solving technical challenges, we wanted to spark a cultural shift - empowering the entire organization to make decisions grounded in accurate, timely data.
Gu Xie
Head of Data Engineering
4.4 of 5
Start trusting your data. Today.
Find, understand, and fix any data quality issue in seconds.
From table to record-level.
Trusted by
Solutions
Case studies
Trusted by the world’s leading enterprises
Real stories from companies using Soda to keep their data reliable, accurate, and ready for action.
At the end of the day, we don’t want to be in there managing the checks, updating the checks, adding the checks. We just want to go and observe what’s happening, and that’s what Soda is enabling right now.
Sid Srivastava
Director of Data Governance, Quality and MLOps
Investing in data quality is key for cross-functional teams to make accurate, complete decisions with fewer risks and greater returns, using initiatives such as product thinking, data governance, and self-service platforms.
Mario Konschake
Director of Product-Data Platform
Soda has integrated seamlessly into our technology stack and given us the confidence to find, analyze, implement, and resolve data issues through a simple self-serve capability.
Sutaraj Dutta
Data Engineering Manager
Our goal was to deliver high-quality datasets in near real-time, ensuring dashboards reflect live data as it flows in. But beyond solving technical challenges, we wanted to spark a cultural shift - empowering the entire organization to make decisions grounded in accurate, timely data.
Gu Xie
Head of Data Engineering
4.4 of 5
Start trusting your data. Today.
Find, understand, and fix any data quality issue in seconds.
From table to record-level.
Trusted by
Solutions
Company