Metadata-Version: 2.1
Name: tableau-hyper-management
Version: 1.2.15
Summary: Wrapper to ease data management into Tableau Hyper format from CSV files
Home-page: https://github.com/danielgp/tableau-hyper-management/releases
Author: Daniel Popiniuc
Author-email: danielpopiniuc@gmail.com
License: LGPL3
Project-URL: Documentation, https://github.com/danielgp/tableau-hyper-management/blob/master/README.md
Project-URL: Issue Tracker, https://github.com/danielgp/tableau-hyper-management/issues?q=is%3Aissue+is%3Aopen+sort%3Aupdated-desc
Project-URL: Source Code, https://github.com/danielgp/tableau-hyper-management
Description: # Tableau-Hyper-Management
        
        
        ## What is this repository for?
        
        Based on [Tableau Hyper API](https://help.tableau.com/current/api/hyper_api/en-us/) this repository is intended to manage importing any CSV file into Tableau-Hyper format (to be used with Tableau Desktop/Server) with minimal configuration (as column detection, content type detection and reinterpretation of content are part of the included logic), therefore speed up the process of building extract.
        
        Even better, can be used in conjunction with [Tableau Server Client](https://tableau.github.io/server-client-python/) to take resulted Tableau Hyper file and publish it to a Tableau Server, therefore automating the tedious task to refresh data on the server side (of course, only relevant in case no direct connection from Tableau Server to data source is possible or nature of content is not supported directly by data source: one real-life example can be daily snapshot of a dynamically changing content to capture big variations in time).
        
        
        ## Who do I talk to?
        
        Repository owner is: [Daniel Popiniuc](mailto:daniel.popiniuc@honeywell.com)
        
        
        ## Installation
        
        Installation is very easy. Either install it from PyPI:
        ```
            $ pip3|pip3(.exe) install -U tableau-hyper-management
            $ pip3|pip3(.exe) install -U git+https://github.com/danielgp/tableau-hyper-management
        ```
        or directly from GitHub:
        ```
            $ git clone https://github.com/danielgp/tableau-hyper-management
            $ python3|python(.exe) setup.py install
        ```
        
        
        ## Usage
        
        
        ### Converting CSV file into Tableau Extract (Hyper format)
        ```
            $ python3|python.exe <local_path_of_this_package>/tableau_hyper_management/converter.py --input-file <full_path_and_file_base_name_to_file_having_content_as_CSV>(.txt|.csv) --csv-field-separator ,|; --output-file <full_path_and_file_base_name_to_generated_file>(.hyper) (--output-log-file <full_path_and_file_name_to_log_running_details>) (--unique-values-to-analyze-limit 100|200=default_value_if_omitted|500|1000)
        ```
        - conventions used:
            - (content_within_round_parenthesis) = optional
            - <content_within_html_tags> = variables to be replaced with user values relevant strings
            - single vertical pipeline = separator for alternative options 
        
        
        ### Publishing a Tableau Extract (Hyper format) to a Tableau Server
        ```
            $ python3|python.exe <local_path_of_this_package>/tableau_hyper_management/publish_data_source.py --input-file <full_path_and_file_base_name_with_tableau_extract>(.hyper) --tableau-server <tableau_server_url> --tableau-site <tableau_server_site_to_publish_to> --tableau-project <tableau_server_project_to_publish_to> --publishing-mode Append|CreateNew|Overwrite==default_if_omitted --input-credentials-file %credentials_file% (--output-log-file <full_path_and_file_name_to_log_running_details>)
        ```
        - conventions used:
            - (content_within_round_parenthesis) = optional
            - <content_within_html_tags> = variables to be replaced with user values relevant strings
            - single vertical pipeline = separator for alternative options 
        
        
        ## Implemented features
        
        - dynamic fields detection based ont 1st line content and provided field separator (strategic advantage);
        - dynamic advanced content type detection covering following data types: integer, float-dot, date-iso8601, date-DMY-dash, date-DMY-dot, date-DMY-slash, date-MDY, date-MDY-medium, date-MDY-long, time-12, time-12-micro-sec, time-24, time-24-micro-sec, datetime-iso8601, datetime-iso8601-micro-sec, datetime-MDY, datetime-MDY-micro-sec, datetime-MDY-medium, datetime-MDY-medium-micro-sec, datetime-MDY-long, datetime-MDY-long-micro-sec, string;
        - support for empty field content for any data type (required re-interpreting CSV to be accepted by Hyper Inserter to ensure INT or DOUBLE data types are considered);
        - use Panda package to benefit of Data Frames speed and flexibility;
        - log file to capture entire logic details (very useful for either traceability but also debugging);
        - most of the logic actions are not timed for performance measuring so you can plan better your needs;
        - publishing a Tableau Extract (Hyper format) to a Tableau Server now is supported (since end of March 2020).
        
        ## Change Log / Releases detailed
        
        see [CHANGE_LOG.md](CHANGE_LOG.md)
        
        ## Planned features to add (of course, when time will permit / help would be appreciated / votes|feedback is welcomed)
        
        - additional formats to be recognized, like:
            - float-USA-thousand-separator,
            - float-EU,
            - float-EU-thousand-separator;
            - geographical identifiers (Country, US - Zip Codes)
        
        
        ## Features to request template
        
        Use [feature_request.md](.github/ISSUE_TEMPLATE/feature_request.md)
        
        
        ## Required software/drivers/configurations
        
        see [readme_software.md](readme_software.md)
        
        
        ## Used references
        
        see [readme_reference.md](readme_reference.md)
        
        
        ## Code quality analysis
        [![Scrutinizer Code Quality](https://scrutinizer-ci.com/g/danielgp/tableau-hyper-management/badges/quality-score.png?b=master)](https://scrutinizer-ci.com/g/danielgp/tableau-hyper-management/?branch=master)
        
        
        ## Build Status
        [![Build Status](https://scrutinizer-ci.com/g/danielgp/tableau-hyper-management/badges/build.png?b=master)](https://scrutinizer-ci.com/g/danielgp/tableau-hyper-management/build-status/master)
        
Keywords: tableau,hyper,csv
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Environment :: Console
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Information Technology
Classifier: Intended Audience :: System Administrators
Classifier: License :: OSI Approved :: GNU Lesser General Public License v3 (LGPLv3)
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Requires-Python: >=3.6
Description-Content-Type: text/markdown
