2021.10
Collibra DIC Integration
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Profile (no-code)
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).
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.
Last modified 16d ago