Collibra DQ User Guide
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2022.05
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Profile (automatic)
Create profiles based on a table, view, or file
Users have the option to scan the entire dataset or users can apply custom filtering to select the depth (row filtering) and width (columns).
Select the Scope
You can find detailed instructions about selecting the scope in the Explorer section. You can run limits, by time, or full table scans if you have enough resources.
Select Options (or leave defaults)
Save / Run
Profile is on by default and is part of onboarding a dataset
View the Results
Automatically Profile
Owl 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.
Dataset Profile
Owl 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.
Pushdown Profiling
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
The following DBMS systems are supported for "Profile Pushdown"
- Impala
- Hive
- Snowflake
- Presto
- Teradata
- SQL Server
- Postgres
- Redshift
- Mysql
- Oracle
- DB2
Pushdown and parallel JDBC cannot be used together. If you are using pushdown, do not select the parallel JDBC option.
Profile Insights
By gathering a variety of different statistics, Collibra DQ's profile can provide a great deal of insight about the dataset.
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.
Profile includes a range of statistics
- Actual Datatype
- Discovered Datatypes
- Percent Null
- Percent Empty
- Percent Mixed Types
- Cardinality
- Minimum
- Maximum
- Mean
- TopN / BottomN
- Value Quartiles
Sensitive Data Detection (Semantic)
Owl can automatically identify any types of common PII columns.
Owl is able to detect the following types of PII
- EMAIL
- PHONE
- ZIP CODE
- STATE CD
- CREDIT CARD
- GENDER
- SSN
- IP ADDRESS
- EIN
Once detected, Owl 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.
Correlation Matrix (Relationship)
Discover hidden relationships and measure the strength of those relationships.
Histograms
Often the first step in a data science project is to segment the data. Owl automatically does this using histograms.
Data Preview
After profiling the data, for those users with appropriate rights, Owl 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.
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Last modified 13d ago