The second part of the dashboard allows you to analyze it using the table stage and table priority sections. Learn more in our Ebook (dqops.com/best-practices…). #machinelearning #AI #datascience #dataengineering #dataquality #dqops

Are you sure that your tables are available and actually exist? Use table_availability quality check to ensure that all your tables exist. Learn more in our documentation (dqops.com/docs/checks/ta…). #machinelearning #AI #datascience #dataengineering #dataquality #dqops

Identify any tables that have been fixed or the data quality checks that have been updated. Learn more about the efficient data quality process in our Ebook (dqops.com/best-practices…). #machinelearning #AI #datascience #dataengineering #dataquality #dqops

Are you sure that your tables are available and actually exist? Use profile_table_availability quality check to ensure that all your tables exist. Learn more in our documentation (dqops.com/docs/checks/ta…). #machinelearning #AI #datascience #dataengineering #dataquality #dqops

The rule is a set of conditions against which sensor readouts are verified, described by a list of thresholds. Learn more in our documentation (dqops.com/docs/dqo-conce…). #machinelearning #AI #datascience #dataengineering #dataquality #dqops

The first part of the dashboard ‘Current completeness issues on columns’ demonstrates the connection, schema, table, and check type sections. Learn more in our Ebook (dqops.com/best-practices…). #machinelearning #AI #datascience #dataengineering #dataquality #dqops

Data Engineer's Lunch! Join meetup (meetup.com/data-wranglers…) on April 29, 12:00 PM and learn more about data engineering! #machinelearning #AI #datascience #dataengineering #dataquality #dqops

Here you can see the profile_valid_uuid_format_percent data quality check result in the DQOps app. The actual value is 75, meaning we have a valid result. Learn more in our documentation (dqops.com/docs/checks/co…). #machinelearning #AI #datascience #dataengineering #dataquality

Alternatively, the Data Owner or the Data Engineering Team may decide that some data quality checks are not relevant and should be disabled or removed. Learn more in our Ebook (dqops.com/best-practices…).
#machinelearning #AI #datascience #dataengineering #dataquality #dqops

The last part of the ‘KPIs scorecard - summary’ dashboard allows you to analyze it using the distribution of checks results per month sections. Learn more in our Ebook (dqops.com/best-practices…). #machinelearning #AI #datascience #dataengineering #dataquality #dqops

Do you have invalid emails in your data? Verify the number of invalid email values using the profile_invalid_email_format_found data quality check. Learn more in our documentation (dqops.com/docs/checks/co…). #machinelearning #AI #datascience #dataengineering #dataquality #dqops

The Data Owner or the Data Engineering Teams may determine that some data quality checks are incorrect or that the alerting thresholds must be adjusted. Learn more in our Ebook (dqops.com/best-practices…).
#machinelearning #AI #datascience #dataengineering #dataquality #dqops

Are you sure that your column contains valid UUID values? Use profile_valid_uuid_format_percent to verify the percentage of valid UUID values. Learn more in our documentation (dqops.com/docs/checks/co…). #machinelearning #AI #datascience #dataengineering #dataquality #dqops

The DQO Command-Line Interface enables you to interact with DQO using commands in Linux shells or Windows command prompt. DQO CLI is an alternative to the DQO graphical user interface. Learn more (dqops.com/docs/dqo-conce…). #machinelearning #AI #dataengineering #dataquality #dqops

The second part of the dashboard demonstrates the Passed data quality checks, Percentage of executed checks, and failed data quality checks section. Learn more in our Ebook (dqops.com/best-practices…). #machinelearning #AI #datascience #dataengineering #dataquality #dqops

An Evening of Python Coding! Join meetup (meetup.com/dcpython/event…) on April 17, 8:00 PM and learn more about Python! #machinelearning #AI #datascience #dataengineering #dataquality #dqops #Python

Here you can see the result of null_percent #checkrun . The actual value is 0, which means that the data has no duplicates and we have a valid result. Try DQOps now (dqops.com/docs/getting-s……). #machinelearning #AI #datascience #dataengineering #dataquality #dqops

Contact the Data Owner and Data Engineering Teams. Learn more about the efficient data quality process in our Ebook (dqops.com/best-practices…).
#machinelearning #AI #datascience #dataengineering #dataquality #dqops

The first part of the ‘KPIs scorecard - summary’ dashboard allows you to analyze it using the percentage of passed checks and KPIs history by month sections. Learn more in our Ebook (dqops.com/best-practices…). #machinelearning #AI #datascience #dataengineering #dataquality #dqops

Do you have invalid emails in your data? Verify the number of invalid email values using the profile_string_invalid_email_count data quality check. Learn more in our documentation (dqops.com/docs/checks/co…). #machinelearning #AI #datascience #dataengineering #dataquality #dqops