Less firefighting, more building
Automatically detect when data stops loading, schema changes, or new categories emerge.
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Build reliable pipelines
Detect issues instantly when upstream APIs change, external data arrives incomplete, or dbt models drift from expected outputs.
Detect issues before business complains
Automatically monitor pipelines for stale tables, schema changes, volume drops, and unexpected anomalies. Catching problems long before they reach dashboards.
Faster debugging and root-cause analysis
Eliminate ad-hoc validation scripts and manual checks. Centralize quality enforcement through data contracts and reusable rules across your architecture.
Build reliable pipelines
Detect issues instantly when upstream APIs change, external data arrives incomplete, or dbt models drift from expected outputs.
Detect issues before business complains
Automatically monitor pipelines for stale tables, schema changes, volume drops, and unexpected anomalies. Catching problems long before they reach dashboards.
Faster debugging and root-cause analysis
Eliminate ad-hoc validation scripts and manual checks. Centralize quality enforcement through data contracts and reusable rules across your architecture.
Build reliable pipelines
Detect issues instantly when upstream APIs change, external data arrives incomplete, or dbt models drift from expected outputs.
Detect issues before business complains
Automatically monitor pipelines for stale tables, schema changes, volume drops, and unexpected anomalies. Catching problems long before they reach dashboards.
Faster debugging and root-cause analysis
Eliminate ad-hoc validation scripts and manual checks. Centralize quality enforcement through data contracts and reusable rules across your architecture.
Why Data Engineers choose Soda
Find, understand and fix data quality issues before they break your pipelines.
Data Observability
Data Observability
Data Observability
Smarter and faster data observability
Metrics Observability continuously tracks key dataset and metadata metrics. From record counts and update frequency to schema changes. It automatically detects deviations that traditional monitoring tools miss.
Its high-precision anomaly detection algorithm adapts to each dataset’s natural behavior, learning what “normal” looks like over time. This means fewer false positives, faster detection of real issues, and more confidence that your pipelines are performing exactly as expected.
Metrics Observability continuously tracks key dataset and metadata metrics. From record counts and update frequency to schema changes. It automatically detects deviations that traditional monitoring tools miss.
Its high-precision anomaly detection algorithm adapts to each dataset’s natural behavior, learning what “normal” looks like over time. This means fewer false positives, faster detection of real issues, and more confidence that your pipelines are performing exactly as expected.
Metrics Observability continuously tracks key dataset and metadata metrics. From record counts and update frequency to schema changes. It automatically detects deviations that traditional monitoring tools miss.
Its high-precision anomaly detection algorithm adapts to each dataset’s natural behavior, learning what “normal” looks like over time. This means fewer false positives, faster detection of real issues, and more confidence that your pipelines are performing exactly as expected.
Collaborative Data Contracts
Collaborative Data Contracts
Collaborative Data Contracts
Prevent breaking changes with quality contracts
Data Contracts act as safeguards between pipeline stages, verifying that schema changes, data completeness, freshness requirements or standard data quality checks stay intact as data moves downstream.
When upstream teams modify formats or transformations evolve, contracts validate dependencies in real time, whether scheduled or triggered through CI/CD pipelines and pull requests.
Integrated directly with Git, engineers can propose, review, and approve data quality changes in their workflow, ensuring reliability by design without slowing development.
Data Contracts act as safeguards between pipeline stages, verifying that schema changes, data completeness, freshness requirements or standard data quality checks stay intact as data moves downstream.
When upstream teams modify formats or transformations evolve, contracts validate dependencies in real time, whether scheduled or triggered through CI/CD pipelines and pull requests.
Integrated directly with Git, engineers can propose, review, and approve data quality changes in their workflow, ensuring reliability by design without slowing development.
Data Contracts act as safeguards between pipeline stages, verifying that schema changes, data completeness, freshness requirements or standard data quality checks stay intact as data moves downstream.
When upstream teams modify formats or transformations evolve, contracts validate dependencies in real time, whether scheduled or triggered through CI/CD pipelines and pull requests.
Integrated directly with Git, engineers can propose, review, and approve data quality changes in their workflow, ensuring reliability by design without slowing development.
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