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# README | ||
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See [Wiki](https://github.com/dataiku/lca/wiki). | ||
## Plugin documentation | ||
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The documentation website for Visual Edit is at [https://dataiku.github.io/dss-visual-edit/](https://dataiku.github.io/dss-visual-edit/). Quick Links: | ||
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* For Data Experts: | ||
* [Installation](install-plugin) | ||
* User guides: | ||
* [Use Case #1: Correcting Source Data](get-started) | ||
* [Use Case #2: Reviewing Machine-Generated Data](reviewing) | ||
* [Deploying in Production](deploy) | ||
* Advanced features: | ||
* [Linked Records](linked-records) | ||
* [Editschema](editschema) | ||
* [Python API](get-started-crud-python-api) | ||
* [FAQ](faq) | ||
* For Business Users: | ||
* [How to use the data table](data-table-features) | ||
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## Contents of this repository | ||
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* `dash_tabulator`: source code for the Dash component powering the plugin's "data table", based on the [Tabulator](http://tabulator.info) Javascript library. | ||
* `plugin-dss-visual-edit`: actual plugin code; when installing the plugin in Dataiku, we would provide this directory only. | ||
* `docs/`: source code in Markdown for the documentation website | ||
* `backend/`: technical documentation generated from docstrings by Mkdocs (following [this tutorial](https://realpython.com/python-project-documentation-with-mkdocs/)). Updates are manual. They require running `mkdocs build` from `plugin-dss-visual-edit/python-lib/` and moving the output (in `site/`) to the repo's `docs/backend/`. | ||
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In addition to the documentation website, check out these directories' README files for additional explanations of how the plugin works. |
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# Data table features | ||
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## Rich editing experience | ||
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* **Automatic input validation**: Formatting and editing adapt to the detected data types: | ||
* Numerical cells don’t allow characters | ||
* Boolean cells are edited via a checkbox | ||
* Date cells are edited via a date picker | ||
* Linked records restrict selection to entries in a linked dataset. | ||
* **Airtable-like editing of [linked records](linked-records)** (aka foreign keys) via dropdown, with the ability to **search and display lookup columns** in real time, from a linked dataset of any size. | ||
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## Powerful data browsing | ||
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* **Filtering, sorting and grouping** in real time. | ||
* Re-ordering, re-sizing and hiding columns, so you can focus on content that's important to you. | ||
* Pagination of large datasets. | ||
* **Automatic detection of changes** in the source dataset. | ||
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## How to use these features | ||
# How to use the data table | ||
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* Right-clicking on the column name will show a menu with an option to hide the column, and an option to group rows by value. | ||
* Filtering: | ||
* The default filter is a textual one. | ||
* In the case of a display-only boolean column, the filter is a tri-state checkbox (or a simple toggle if you specified the column type to be "boolean_tick" via the advanced settings ([editschema](editschema)). | ||
* Editable boolean columns have a textual filter that you can use by typing "true" or "false". | ||
* Sorting is done by clicking on the arrow next to column name. | ||
* Filtering widgets are located below each column name. | ||
* All of this can be reset by clicking on the "Reset View" button in the bottom-left corner. | ||
* Linked records are edited via dropdowns which include the ability to search through all options: when there are many of them, only options that start with the search term are presented. | ||
* Changes in the original dataset are automatically detected and signaled. The user needs to refresh the webpage in order to see them. | ||
* Changes in the original dataset are automatically detected and signaled. The user needs to refresh the webpage in order to see them. | ||
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## Troubleshooting | ||
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If the webapp isn't functioning as expected, clear the browser's cached images and files. | ||
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If you get a "backend" error, get in touch with your admin. |
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# Plugin: Visual Edit | Dataiku | ||
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## Build apps to edit data and write it back to the Flow | ||
## Build apps to edit records of data within pipelines | ||
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<iframe src="https://www.loom.com/embed/7b79e45e755544f8baf1ff3ed1bf60ee" frameborder="0" webkitallowfullscreen="" mozallowfullscreen="" allowfullscreen="" style="height: 400px; width: 600px"></iframe> | ||
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[Book a demo!](https://calendar.google.com/calendar/u/0/appointments/schedules/AcZssZ1cgQ-IQ2k2eJMm6mUrZxabQgtPSSwaZ9TgNcHcnaUDvrqfweAkf-B7xzZbTSNyYeSRc2smgLbp){: .btn} | ||
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## Use cases | ||
Use Cases: enable domain experts to... | ||
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* Human-in-the-loop analytics and AI: | ||
* **Entity Resolution** (aka record linkage): review and correct fuzzy matches between 2 or more datasets representing the same entities (e.g. customers, suppliers, products) but with differing IDs and names. [Demo project →](sample-project-company-resolution) | ||
* **AI Validation and Improvement**: continuously provide feedback on AI outputs in production, and ingest back into the AI's training data. [Demo project →](sample-project-ai-feedback-app) | ||
* **Forecast Override**: machine-made forecasts (e.g. sales) need to be adjusted by a human, to take into account contextual information that wasn't available to the machine, or to correct low-confidence predictions. | ||
* **Automation Reviewing**: deal with exceptions by overriding values in the output of business rules, or filling in missing values. | ||
* **Data Quality Campaigns**: fix incorrect records and improve downstream analytics. | ||
* Make tactical corrections on source data, to ensure correctness of an analytics project. | ||
* Review the output of rules or AI used for | ||
* Mass corrections/enrichment of source data: **entity resolution, deduplication, categorization**, etc. | ||
* Operational processes: **log decisions on alerts, validate suggestions of amounts to pay or orders to place**, etc. | ||
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## Features | ||
Features: | ||
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* **No-code webapp**: | ||
* Powerful data browsing (filtering, sorting, grouping in real-time) | ||
* Rich editing experience (automatic input validation, linked records, lookup columns) | ||
* [Learn more →](data-table-features) | ||
* **Dataset of edited rows**: use as input to a recipe that writes back to a source system, or for the implementation of a feedback loop (e.g. stacking with an AI training dataset). | ||
* **Governance**: built-in audit trail of all edits made by end-users. | ||
* **Reviewing**: when marking a row of generated data as reviewed, generated values are automatically persisted (even if they weren't edited). | ||
* **Continuous updates**: seamless integration of new edits into production data pipelines, and of new data into the webapp (changes are detected automatically and previous edits are applied to the new data). | ||
* **API to Create, Read, Update, Delete rows (CRUD)**, for coders building custom webapps. | ||
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## [Get started right away!](get-started) | ||
* Powerful data table with rich editing experience and a special Review checkbox. | ||
* Easy webapp setup (no-code and low-code options) and integration of edits in your pipeline. | ||
* Ability to leverage human feedback for continuous AI improvement. | ||
* Audit trail of all edits. | ||
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## Quick links | ||
* [Plugin installation](install-plugin) | ||
* [Data table features](data-table-features) | ||
* [Linked records](linked-records) | ||
* [Editschema](editschema) | ||
* [Python library](get-started-crud-python-api) | ||
* [Deploying in production](deploy) | ||
* [FAQ](faq) | ||
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* For Data Experts: | ||
* [Installation](install-plugin) | ||
* User guides: | ||
* [Use Case #1: Correcting Source Data](get-started) | ||
* [Use Case #2: Reviewing Machine-Generated Data](reviewing) | ||
* [Deploying in Production](deploy) | ||
* Advanced features: | ||
* [Linked Records](linked-records) | ||
* [Editschema](editschema) | ||
* [Python API](get-started-crud-python-api) | ||
* [FAQ](faq) | ||
* For Business Users: | ||
* [How to use the data table](data-table-features) |
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