Great-expectations

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0.8.2

* Add easier support for customizing data-docs css
* Use higher precision for rendering 'mostly' parameter in data-docs; add more consistent locale-based
formatting in data-docs
* Fix an issue causing visual overlap of large numbers of validation results in build-docs index
* Documentation fixes (thanks DanielOliver!) and improvements
* Minor CLI wording fixes
* Improved handling of MySql temporary tables
* Improved detection of older config versions

0.8.1

* Fix an issue where version was reported as '0+unknown'

0.8.0

Version 0.8.0 is a significant update to Great Expectations, with many improvements focused on configurability and usability. See the migrating versions guide for more details on specific changes, which include several breaking changes to configs and APIs.

Highlights include:

1. Validation Operators and Actions. Validation operators make it easy to integrate GE into a variety of pipeline runners. They offer one-line integration that emphasizes configurability. See the validation operators and actions feature guide for more information.

- The DataContext `get_batch` method no longer treats `expectation_suite_name` or `batch_kwargs` as optional; they must be explicitly specified.
- The top-level GE validate method allows more options for specifying the specific data_asset class to use.

2. First-class support for plugins in a DataContext, with several features that make it easier to configure and
maintain DataContexts across common deployment patterns.

- **Environments**: A DataContext can now manage `environment_and_secrets` more easily thanks to more dynamic and flexible variable substitution.
- **Stores**: A new internal abstraction for DataContexts, `stores_reference`, make extending GE easier by consolidating logic for reading and writing resources from a database, local, or cloud storage.
- **Types**: Utilities configured in a DataContext are now referenced using `class_name` and `module_name` throughout the DataContext configuration, making it easier to extend or supplement pre-built resources. For now, the "type" parameter is still supported but expect it to be removed in a future release.

3. Partitioners: Batch Kwargs are clarified and enhanced to help easily reference well-known chunks of data using a partition_id. Batch ID and Batch Fingerprint help round out support for enhanced metadata around data assets that GE validates. See `batch_identifiers` for more information. The `GlobReaderGenerator`, `QueryGenerator`, `S3Generator`, `SubdirReaderGenerator`, and `TableGenerator` all support partition_id for easily accessing data assets.

4. Other Improvements:

- We're beginning a long process of some under-the-covers refactors designed to make GE more maintainable as we begin adding additional features.
- Restructured documentation: our docs have a new structure and have been reorganized to provide space for more easily adding and accessing reference material. Stay tuned for additional detail.
- The command build-documentation has been renamed build-docs and now by default opens the Data Docs in the users' browser.

0.7.11

* Fix an issue where head() lost the column name for SqlAlchemyDataset objects with a single column
* Fix logic for the 'auto' bin selection of `build_continuous_partition_object`
* Add missing jinja2 dependency
* Fix an issue with inconsistent availability of strict_min and strict_max options on `expect_column_values_to_be_between`
* Fix an issue where expectation suite evaluation_parameters could be overriden by values during validate operation

0.7.10

* Fix an issue in generated documentation where the Home button failed to return to the index
* Add S3 Generator to module docs and improve module docs formatting
* Add support for views to QueryGenerator
* Add success/failure icons to index page
* Return to uniform histogram creation during profiling to avoid large partitions for internal performance reasons

0.7.9

* Add an S3 generator, which will introspect a configured bucket and generate batch_kwargs from identified objects
* Add support to PandasDatasource and SparkDFDatasource for reading directly from S3
* Enhance the Site Index page in documentation so that validation results are sorted and display the newest items first when using the default run-id scheme
* Add a new utility method, `build_continuous_partition_object` which will build partition objects using the dataset API and so supports any GE backend.
* Fix an issue where columns with spaces in their names caused failures in some SqlAlchemyDataset and SparkDFDataset expectations
* Fix an issue where generated queries including null checks failed on MSSQL (695)
* Fix an issue where evaluation parameters passed in as a set instead of a list could cause JSON serialization problems for the result object (699)

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