Compare Monte Carlo and Soda 
- Users report that Monte Carlo excels in data quality monitoring with a score of 8.9, while Soda shines in real-time analytics, scoring 8.0. Reviewers mention that Monte Carlo's robust data quality features help ensure accurate insights, which is crucial for data-driven decision-making.
- Reviewers mention that Monte Carlo's quality of support is rated highly at 9.3, compared to Soda's 8.7. Users on G2 appreciate the responsive customer service and the helpful resources provided by Monte Carlo, making it easier to resolve issues quickly.
- Users say that Monte Carlo offers superior anomaly identification capabilities with a score of 8.8, while Soda follows closely with 8.6. Reviewers highlight that Monte Carlo's anomaly detection features are particularly effective in identifying data issues before they escalate.
- G2 users report that Soda has a better score in correction functionality at 8.3 compared to Monte Carlo's 6.2. Users mention that Soda's correction features allow for more efficient data rectification processes, which is essential for maintaining data integrity.
- Reviewers mention that Monte Carlo provides a comprehensive single pane view with a score of 7.9, while Soda offers a slightly better score of 8.3. Users appreciate Soda's dashboard for its intuitive layout, which simplifies monitoring and management tasks.
- Users on G2 report that Monte Carlo's automated workflows score 7.9, while Soda's automation capabilities are rated higher at 8.6. Reviewers say that Soda's automation features streamline repetitive tasks, enhancing overall productivity for teams.
- Monitoring for Data Warehouse, Business Intelligence, ETL
- Incident triaging, troubleshooting and root cause analysis
- Lineage
- Soda AI, the generative AI for data quality translating natural language into production-ready data quality checks.
- SodaCL, a human-readable and -writable language to test and monitor data as-code in CI/CD and data pipelines.
- Soda Library, the Python library and CLI tool for data quality testing with built-in checks and automated suggestions.
Custom alerts allow users complete flexibility in what they would like to monitor. Their integration with Slack allows us to know when a problem occurs without checking the dashboard constantly. The product team actually listens to feedback and takes...
Completeness checks need to be improved. Alerts with new message after creating incidents is not currently supported. Some of the search option for filters in the UI for assets doesn't work often
One of the advantages that the tool offers is its flexibility to incorporate quality checks in the data. This allows the company's decision-making process to be improved. In addition, among other advantages we can mention its ease of use, even for...
It is hard to integrate soda-core with dagster. Both, especially so Dagster changed a lot during the last year and finding up to date documentation is difficult. As integration is the first step I suggest creating more complete integration samples for...
Custom alerts allow users complete flexibility in what they would like to monitor. Their integration with Slack allows us to know when a problem occurs without checking the dashboard constantly. The product team actually listens to feedback and takes...
One of the advantages that the tool offers is its flexibility to incorporate quality checks in the data. This allows the company's decision-making process to be improved. In addition, among other advantages we can mention its ease of use, even for...
Completeness checks need to be improved. Alerts with new message after creating incidents is not currently supported. Some of the search option for filters in the UI for assets doesn't work often
It is hard to integrate soda-core with dagster. Both, especially so Dagster changed a lot during the last year and finding up to date documentation is difficult. As integration is the first step I suggest creating more complete integration samples for...
