Data quality for your data stack
Data quality for your data stack
Data quality for your data stack
Find, understand, and fix any data quality issue in seconds.From table to record-level.
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Airflow
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Amazon Redshift
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Airflow
Orchestration
Databricks
Data Source
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Data Source
Microsoft Azure
Infraestructure
PostgreSQL
Data Source
Amazon Web Services
Infraestructure
Google Cloud Platform
Infraestructure
Collibra
Data Catalog
Atlan
Data Catalog
Slack
Alerting
Tableau
Dashboarding
Microsoft Teams
Alerting
Looker
Dashboarding
Purview
Data Catalog
Power BI
Dashboarding
GitHub
Orchestration
Dbt
Orchestration
Okta
Security
Azure Active Directory
Security
Prefect
Orchestration
Dagster
Orchestration
Azure Data Factory
Orchestration
ServiceNow
Ticketing
PagerDuty
Ticketing
Opsgenie
Ticketing
Jira Software
Ticketing
DataGalaxy
Data Catalog
Data.world
Data Catalog
CastorDoc
Data Catalog
Vertica
Data Source
Trino
Data Source
Presto
Data Source
Pandas
Data Source
Oracle
Data Source
MySQL
Data Source
MotherDuck
Data Source
Microsoft SQL Server
Data Source
IMB DB2
Data Source
Google Big Query
Data Source
Duck DB
Data Source
Dremio
Data Source
Denodo
Data Source
Dask
Data Source
ClickHouse
Data Source
Azure Synapse Analytics
Data Source
Apache Spark
Data Source
Amazon Redshift
Data Source
Amazon Athena
Data Source
Airflow
Orchestration
Databricks
Data Source
Snowflake
Data Source
Microsoft Azure
Infraestructure
PostgreSQL
Data Source
Amazon Web Services
Infraestructure
Google Cloud Platform
Infraestructure
Collibra
Data Catalog
Atlan
Data Catalog
Slack
Alerting
Tableau
Dashboarding
Microsoft Teams
Alerting
Looker
Dashboarding
Purview
Data Catalog
Power BI
Dashboarding
GitHub
Orchestration
Dbt
Orchestration
Okta
Security
Azure Active Directory
Security
Prefect
Orchestration
Dagster
Orchestration
Azure Data Factory
Orchestration
ServiceNow
Ticketing
PagerDuty
Ticketing
Opsgenie
Ticketing
Jira Software
Ticketing
DataGalaxy
Data Catalog
Data.world
Data Catalog
CastorDoc
Data Catalog
Vertica
Data Source
Trino
Data Source
Presto
Data Source
Pandas
Data Source
Oracle
Data Source
MySQL
Data Source
MotherDuck
Data Source
Microsoft SQL Server
Data Source
IMB DB2
Data Source
Google Big Query
Data Source
Duck DB
Data Source
Dremio
Data Source
Denodo
Data Source
Dask
Data Source
ClickHouse
Data Source
Azure Synapse Analytics
Data Source
Apache Spark
Data Source
Amazon Redshift
Data Source
Amazon Athena
Data Source
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
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