Data Observability for Azure Data Lakes

Data Observability for Azure Data Lakes

Microsoft Azure

Use Data Culpa Validator to inspect and analyze data entering and leaving your Azure Data Lakes.

  • Detect changes in data schemas, volumes, and values
  • Compare any day’s data to any other day’s data
  • Compare any day’s data to the gold standard for a particular use case
  • Receive alerts through Slack and PagerDuty when data changes in important ways.
Data Culpa alerts data teams about changes in Azure Data Lake files

Configurable to Meet Your Needs

We believe “the best UI is no UI,” so we have built our solution to stay out of the way, behind the scenes, except when data quality issues needs to be addressed.

You can configure Watchpoint alarms from the GUI or from a YAML file sourced in GitHub. With numerous controls for frequency and severity, you can tune Data Culpa’s behavior for what you care about and how and when you want to hear from it.

One of the biggest challenges with data quality is that it is contextual. One user doesn’t care about zip codes. Another doesn’t care about phone numbers. Data Culpa enables your users to configure self-service alerts and warnings based on their needs for their data processing.

Support for Major Data Formats

Data Culpa supports structured, semi-structured, and unstructured data.

Data Culpa Validator supports CSV and JSON out of the box and warns you when passed data isn’t parsable. Maybe you have CSVs with extra delimiters or screwy quotes in your data lake. Perhaps someone wrote a JSON dumper that breaks on a new record. Data Culpa helps you zero in on errors like these right away.

Try Data Culpa in a Docker Container or as a SaaS Application Today

Installation is fast and easy. We offer a free open-source Azure Data Lake connector on GitHub.