Summary
We've moved! To improve customer experience, the Collibra Data Quality User Guide has moved to the Collibra Documentation Center as part of the Collibra Data Quality 2022.11 release. To ensure a seamless transition, dq-docs.collibra.com will remain accessible, but the DQ User Guide is now maintained exclusively in the Documentation Center.
Collibra DQ offers easy to use no (low) code options for getting started quickly. Alternatively, more technical users may prefer programmatic APIs.
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
Imagine a column going null, automatic row count checks - does your data behave/look/feel the same way it has in the past.
Assures only values compliant with your data rules are allowed within a data object.
Columns add or dropped.
Infrequent formats.
Fuzzy matching to identify entries that have been added multiple times with similar but not exact detail.
Data points that differ significantly from other observations.
Recognizing relevant patterns between data examples.
Validating source to target accuracy.
Deltas for a given column.
Last modified 4mo ago