2021.10
Collibra DIC Integration
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Overview

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Collibra DQ offers easy to use no (low) code options for getting started quickly. Alternatively, more technical users may prefer programmatic APIs.

Core Components

Collibra DQ offers a full DQ suite to cover the unique challenges of each dataset.
9 Dimensions of DQ
    1.
    Behaviors - Data observability
    2.
    Rules - SQL-based rules engine
    3.
    Schema - When columns are added or dropped
    4.
    Shapes - Typos and Formatting Anomalies
    5.
    Duplicates - Fuzzy matching, Identify similar but not exact entries
    6.
    Outliers - Anomalous records, clustering, time-series, categorical
    7.
    Pattern - Classification, cross-column & parent/child anomalies
    8.
    Record - Deltas for a given column(s)
    9.
    Source - Source to target reconciliation

Behavior

Imagine a column going null, automatic row count checks - does your data behave/look/feel the same way it has in the past.

Rules

Assures only values compliant with your data rules are allowed within a data object.

Schema

Columns add or dropped.

Shapes

Infrequent formats.

Dupes

Fuzzy matching to identify entries that have been added multiple times with similar but not exact detail.

Outliers

Data points that differ significantly from other observations.

Pattern

Recognizing relevant patterns between data examples.

Source

Validating source to target accuracy.

Record

Deltas for a given column.

Last modified 14d ago