Profile (no-code)

Users have the option to scan the entire dataset or users can apply custom filtering to select the depth (row filtering) and width (columns). 
See selecting the scope section of Explorer to see how.
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 
Profile Insights
By gathering a variety of different statistics, Owl's profile can provide a great deal of insight about the dataset.  
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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Explorer (advanced)

AutoProfile

Last modified 16d ago

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Contents

Automatically Profile

Dataset Profile

Pushdown Profiling

Profile Insights

Sensitive Data Detection (Semantic)

Correlation Matrix (Relationship)

Histograms

Data Preview