Create profiles based on a table, view, or file
Profile is on by default and is part of onboarding a dataset
Collibra Data Quality automatically profiles datasets over time to enable drill-in for detailed insights an automated data quality. A profile is just the first step towards an amazing amount of auto discovery. Visualize segments of the dataset and how how the dataset is changing over time.
Collibra DQ offers click or code options to run profiling.
Collibra Data Quality creates a detailed profile of each dataset under management. This profile will later be used to both provide insight and automatically identify data quality issues.
Collibra DQ can compute the Profile of a dataset either via Spark (default) or the Data Warehouse (Profile Pushdown) where the data lives as the engine. When the Profile is computed using the datasource DBMS the user can choose two levels of pushdown:
- Full Profile - Perform full profile calculation except for TopN
- Count - Only perform row and column counts
By gathering a variety of different statistics, Collibra DQ's profile can provide a great deal of insight about a data set.
To see the difference between baseline (historical) and current values, Collibra DQ provides a Delta % change column. In the Delta % change column, data is represented in a pie chart for quick visualization of the changes.
To elaborate on the quality metrics:
The profile can discover attributes then helps delineate the relative metrics around numeric v. non-numeric discovered.
- Filled -  Integer - The percentage of data that is numeric (or non-numeric) in a numeric (or non-numeric) discovered column.
- Mixed - [String] Integer - The percentage of data that is non-numeric (or numeric) in a numeric (or non-numeric) discovered column.
- Null -  - The percentage of data that has no value at all.
- Empty - [""] - The percentage of data that has a string instance of zero length.
Collibra Data Quality can automatically identify any type of common PII columns.
Once detected, Collibra Data Quality will tag the column in the Profile as the discovered type as well as automatically apply a rule. If the user can choose to decline any discovered tag by simply clicking on it and confirming the delete action. This action can also remove the rule associated with the tag.
Discover hidden relationships and measure the strength of those relationships.
Often the first step in a data science project is to segment the data. Collibra Data Quality automatically does this using histograms.
After profiling the data, for those users with appropriate rights, Collibra Data Quality provides a glimpse of the dataset. The Data preview tab also provides a some basic insights such as highlights of Data Shape issues and Outliers (if enabled), and Column Filtergram visualization.