Monitor Data For AI Workloads, With or Without a database.
When it comes to ML models, you’ve got your work cut out for you. The last thing you need is unexpected drift from model training to production. Data Culpa’s powerful data monitoring platform enables you to monitor data for self-consistency and motion over time and space. Link together locations, compare different points in time, and do it all even if you are using multiple databases or no database at all. Data Culpa can monitor CSV and JSON data in files, Snowflake tables or Mongo collections for schema changes, data distribution changes, and even string formatting changes with automatically generated rules and models.
Easily see how your TrustScore moves over time.
Data Culpa Validator computes autometrics and adjusts over time, reducing alert noise.
Get graphical alerts when things go sideways so you can immediately see changes.
Works With Any Data Source
Data Culpa Validator can plug-in to the major big data warehouses and data lake platforms. It can also be integrated with APIs, monitor folders and files. Metadata is automatically computed and you can choose how much to sample or present to Data Culpa Validator without requiring specialized APIs or vendor support.
Tackle tricky JSON data schemas
- Automatically capture schema changes
- Detect new fields or relocated objects
- Integrate with APIs or file dumps
What people are saying
“Data Culpa gives us the rapid insights and the comprehensive context we need.”
“Our customers come to us for cutting-edge software solutions that will give them a competitive advantage. Speed and accuracy are critical. We use Data Culpa in our client projects because we can’t afford any surprises that compromise our clients’ business results. Data Culpa gives us the rapid insights and the comprehensive context we need.”
Partner, Too Symphony Solutions
“I know firsthand the importance of monitoring data…That’s why I’m an investor in Data Culpa.”
“Data is the lifeblood of every business today. When data goes wrong, it quickly affects the bottom line. In my decades of experience working with data in tech, I know firsthand the importance of monitoring data, controlling quality, and responding to data issues promptly. That’s why I’m an investor in Data Culpa.”
“…it’s exciting to see Data Culpa is tackling these problems”
Dirty data leads to inaccurate analytics results and incorrect business decisions; multiple surveys show that dirty data is the most common barrier faced by data teams. A significant portion of data resides in semi-structured formats, such as JSON, which remains a largely unexplored research topic, which is why it’s exciting to see Data Culpa is tackling these problems.
Dr. Xu Chu
Professor, author of Data Cleaning (ACM Press), Data Culpa science advisor