Releases: databrickslabs/discoverx
Releases · databrickslabs/discoverx
v0.0.9
06 May 06:18
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Added Delta housekeeping
Fixed serverless compatibility
v0.0.8
15 Jan 13:50
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Fixed bug for tables containing - character in the table name
Added example for cloning all catalog/schema content
Added filtering for table format (exclude views from queries by default)
Added support for PII detection on non-string columns
Updated LICENSE file
v0.0.7
13 Nov 13:29
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Added filtering for speedup intro message checks
Added tags metadata in table info
Added map function for arbitrary python code table processing support
Added AI example notebooks
v0.0.6
03 Oct 07:22
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Refactored scan() in order to be chainable with from_tables()
Improved metadata fetching speed for table information
Refactored to remove duplicated SQL code from scanner class
Updated intro messages and documentation
Added example for detecting tables with many small files
v0.0.5
29 Aug 01:05
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Added support for multi-table SQL execution dx.from_tables(...).apply_sql(...)
Added example of VACUUM command to multiple tables
Added example of PII detection using Presidio over multiple tables
v0.0.4
03 Aug 07:36
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Removed pydantic dependency
Fixed issues with special characters in column names
Fixed readme docs
Added integer and decimal rules
Fixed case insensitive regex expressions
v0.0.3
05 Jul 14:39
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Upgraded pydantic dependency to 2.0
Added support for special characters in column names
Updated readme
v0.0.2
03 Jul 08:14
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Improved Readme and examples
Added System tables permissions check with friendly message
Refactored save and load methods after customer feedback
v0.0.1
03 Jul 08:09
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First release of DiscoverX.
It includes:
Lakehouse scanning with REGEX rules on string columns for 16 class types (email, IP v4, IP v6, URLs, MAC address, FQDNs, credit card numbers, credit card expiry date, ISO date, ISO datetime, US mailing address, US phone number, US social security number, US state, US state abbreviation, US zip code
Save and load scan result
Cross-table query based on semantic types of columns (rather than column names)