Continuous, Autonomous Data Lake & Warehouse Observability Automate AWS S3, Azure, GCP and Snowflake Observability using AI/ML
How to monitor and catch data errors before it spreads deep into multiple Cloud data storage systems?
Good data health is essential for accurate analytics and trustworthy reporting. Monitoring for data issues starts at ingestion. Unfortunately Cloud Data Engineers cannot be expected to understand every column of every table, nor what data to monitor and certify for accuracy. As a result, most companies pick and choose to monitor less than 5% of their data. The other 95% is dark data, it is unvalidated, unreliable and highly risky.
FirstEigen’s DataBuckMonitor offers operational and transactional data monitoring functionalities without coding. With just a few clicks and the power of AI/ML, 7 key data monitoring functionalities can be operationalized on AWS S3. It increases the scalability of continuously monitoring data to thousands of incoming data sets. It eliminates months of labor by using Machine Learning.
- Data Quality Health Score of data set
- Freshness of data set
- Format deviations
- Record Count
- Data Profile
What do you gain?
Every step a data error propagates and flows downstream the cost to fix it is 10x more. Save cost and increase productivity by a giant leap.
- Monitor data autonomously with AI/ML
- Cut data maintenance work and cost by over 50%
- Eliminate all dark data. Don’t have to pick and choose what
- 10x efficient in scaling Data Quality to 1,000’s of data sets