docs: add quickstart section (#70)

* docs: add fabric overview

* docs: add image fabric flow

* docs: fix relative links

* docs: add quickstart section

* docs: update structure for Fabric quickstart section

* fix(linting): code formatting

* docs: Update upload CSV file quickstart.

* docs: update quickstart index

* docs: update create your first synthetic data quickstart guide.

* fix(linting): code formatting

* docs: fix sdk references

* docs: add new create lab quickstart.

* fix(linting): code formatting

* docs: update quickstart tutorial for first pipeline.

* fix(linting): code formatting

* docs: Add quickstart lab guide to index

---------

Co-authored-by: Fabiana Clemente <[email protected]>
Co-authored-by: Azory YData Bot <[email protected]>

docs/get-started/create_lab.md

@@ -0,0 +1,52 @@
+# How to create your first Lab environment
+
+Labs are code environments for a more flexible development of data-driven solutions while leveraging Fabric capabilities
+combined with already loved tools such as scikit-learn, numpy and pandas.
+To create your first **Lab**, you can use the **“Create Lab”** from Fabric’s home, or you can access it from the Labs
+module by selecting it on the left side menu, and clicking the **“Create Lab”** button.
+
+<div style="display: flex; justify-content: center;align-items: center;">
+    <img src="/assets/quickstart/create_lab/create_lab.webp" alt="Select create a lab from Home" style="width: 75%;">
+</div>
+
+Next, a menu with different IDEs will be shown. As a quickstart select *Jupyter Lab*. As labs are development environments
+you will be also asked what language you would prefer your environment to support: *R* or *Python*. Select Python.
+
+<div style="display: flex; justify-content: center;align-items: center;">
+    <img src="/assets/quickstart/create_lab/select_ide.webp" alt="Select an IDE" style="width: 50%;">
+    <img src="/assets/quickstart/create_lab/select_language.webp" alt="Python or R" style="width: 50%;">
+</div>
+
+Bundles are environments with pre-installed packages. Select YData bundle, so we can leverage some other Fabric features
+such as Data Profiling, Synthetic Data and Pipelines.
+
+<div style="display: flex; justify-content: center;align-items: center;">
+    <img src="/assets/quickstart/create_lab/select_bundle.webp" alt="Select the bundle for development" style="width: 75%;">
+</div>
+
+As a last step, you will be asked to configure the infrastructure resources for this new environment as well as giving it
+a *Display Name*. We will keep the defaults,
+but you have flexibility to select GPU acceleration or whether you need more computational resources for your developments.
+
+<div style="display: flex; justify-content: center;align-items: center;">
+    <img src="/assets/quickstart/create_lab/select_infrastructure.webp" alt="Select the computational resources" style="width: 75%;">
+</div>
+
+Finally, your Lab will be created and added to the "Labs" list, as per the image below. The status of the lab will be
+🟡 while preparing, and this process takes a few minutes, as the infrastructure is being allocated to your development environment.
+As soon as the status changes to 🟢, you can open your lab by clicking in the button as shown below:
+
+<div style="display: flex; justify-content: center;align-items: center;">
+    <img src="/assets/quickstart/create_lab/open_lab.webp" alt="Open your lab environment" style="width: 75%;">
+</div>
+
+Create a new notebook in the JupyterLab and give it a name. You are now ready to start your developments!
+
+<div style="display: flex; justify-content: center;align-items: center;">
+    <img src="/assets/quickstart/create_lab/notebook_creation.webp" alt="Create a new notebook" style="width: 50%;">
+    <img src="/assets/quickstart/create_lab/notebook_created.webp" alt="Notebook created" style="width: 50%;">
+</div>
+
+**Congrats!** 🚀 You have now successfully created your first **Lab** a code environment, so you can benefit from the most
+advanced Fabric features as well as compose complex data workflows.
+Get ready for your journey of improved quality data for AI.

docs/get-started/create_pipeline.md

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+# How to create your first Pipeline
+
+:fontawesome-brands-youtube:{ .youtube }
+Check this quickstart video on <a href="https://youtu.be/_zZBt2nWiH8"><u>how to create your first Pipeline</u></a>.
+
+The best way to get started with Pipelines is to use the interactive Pipeline editor available in the Labs with Jupyter Lab set as IDE.
+If you don't have a **Lab** yet, or you don't know how to create one, check our <a href="create_lab"><u>quickstart guide on how to create your first lab</u></a>.
+
+Open an already existing lab.
+
+A Pipeline comprises one or more nodes that are connected (or not!) with each other to define execution dependencies. Each pipeline node
+is and should be implemented as a component that is expected to manage a single task, such as read the data, profiling the data, training a model,
+or even publishing a model to production environments.
+
+In this tutorial we will build a simple and generic pipeline that use a **Dataset** from Fabric's **Data Catalog** and profile to check it's quality.
+We have the notebooks template already available. For that you need to access the *"Academy"* folder as per the image below.
+
+<div style="display: flex; justify-content: center;align-items: center;">
+    <img src="/assets/quickstart/create_pipeline/academy_folder.webp" alt="Academy folder" style="width: 75%;">
+</div>
+
+Make sure to copy all the files in the folder "3 - Pipelines/quickstart" to the root folder of your lab, as per the image below.
+
+<div style="display: flex; justify-content: center;align-items: center;">
+    <img src="/assets/quickstart/create_pipeline/copy_files.webp" alt="Select your pipeline editor" style="width: 75%;">
+</div>
+
+Now that we have our notebooks we need to make a small change in the notebook "1. Read dataset". Go back to your **Data Catalog**, from one of the datasets
+in your Catalog list, select the three vertical dots and click in **"Explore in Labs"** as shown in the image below.
+
+<div style="display: flex; justify-content: center;align-items: center;">
+    <img src="/assets/quickstart/create_pipeline/explore_in_labs.webp" alt="Explore the dataset in the labs" style="width: 70%;">
+</div>
+
+The following screen will be shown. Click in copy.
+
+<div style="display: flex; justify-content: center;align-items: center;">
+    <img src="/assets/quickstart/create_pipeline/code_snippet.webp" alt="Dataset code snippet" style="width: 35%;">
+</div>
+
+Now that we have copied the code, let's get back to our **"1. Read data.ipynb"** notebook, and replace the first code cell by with the new code. This will allow us to use a
+dataset from the Data Catalog in our pipeline.
+
+<div style="display: flex; justify-content: center;align-items: center;">
+    <img src="/assets/quickstart/create_pipeline/og_code.webp" alt="Dataset code snippet" style="width: 50%;">
+    <img src="/assets/quickstart/create_pipeline/replaced_code.webp" alt="Dataset code snippet" style="width: 50%;">
+</div>
+
+With our notebooks ready, we can now configure our **Pipeline**.
+For this quickstart we will be leveraging an already existing pipeline - double-click the file *my_first_pipeline.pipeline*. You should see a pipeline
+as depicted in the images below.
+To create a new Pipeline, you can open the lab launcher tab and select **"Pipeline Editor"**.
+
+<div style="display: flex; justify-content: center;align-items: center;">
+    <img src="/assets/quickstart/create_pipeline/open_pipeline.webp" alt="Open pipeline" style="width: 40%;">
+    <img src="/assets/quickstart/create_pipeline/my_first_pipeline.webp" alt="My first pipeline" style="width: 60%;">
+</div>
+
+Before running the pipeline, we need to check each component/step properties and configurations. Right-click each one of the steps, select *"Open Properties"*, and a
+menu will be depicted in your right side. Make sure that you have *"YData - CPU"* selected as the **Runtime Image** as show below.
+
+<div style="display: flex; justify-content: center;align-items: center;">
+    <img src="/assets/quickstart/create_pipeline/open_properties.webp" alt="Open pipeline" style="width: 50%;">
+    <img src="/assets/quickstart/create_pipeline/runtime_image.webp" alt="My first pipeline" style="width: 50%;">
+</div>
+
+We are now ready to create and run our first pipeline. In the top left corner of the pipeline editor, the run button
+will be available for you to click.
+
+<div style="display: flex; justify-content: center;align-items: center;">
+    <img src="/assets/quickstart/create_pipeline/run_pipeline.webp" alt="Select your pipeline editor" style="width: 75%;">
+</div>
+
+Accept the default values shown in the run dialog and start the run
+
+<div style="display: flex; justify-content: center;align-items: center;">
+    <img src="/assets/quickstart/create_pipeline/pipeline_default_dialog.webp" alt="Pipeline configuration confirm dialog" style="width: 30%;">
+</div>
+
+If the following message is shown, it means that you have create a run of your first pipeline.
+
+<div style="display: flex; justify-content: center;align-items: center;">
+    <img src="/assets/quickstart/create_pipeline/pipeline_creation_success.webp" alt="Select your pipeline editor" style="width: 60%;">
+</div>
+
+Now that you have created your first pipeline, you can select the **Pipeline** from Fabric's left side menu.
+
+<div style="display: flex; justify-content: center;align-items: center;">
+    <img src="/assets/quickstart/create_pipeline/pipelines_menu.webp" alt="Select Fabric Pipelines" style="width: 70%;">
+</div>
+
+Your most recent pipeline will be listed, as shown in below image.
+
+<div style="display: flex; justify-content: center;align-items: center;">
+    <img src="/assets/quickstart/create_pipeline/my_pipeline_record.webp" alt="My first pipeline listed" style="width: 70%;">
+</div>
+
+To check the run of your pipeline, jump into the **"Run"** tab. You will be able to see your first pipeline running!
+
+<div style="display: flex; justify-content: center;align-items: center;">
+    <img src="/assets/quickstart/create_pipeline/my_first_pipeline_run.webp" alt="My first pipeline listed" style="width: 70%;">
+</div>
+
+By clicking on top of the record you will be able to see the progress of the run step-by-step, and visualize the outputs of each and every
+step by clicking on each step and selecting the **Visualizations** tab.
+
+<div style="display: flex; justify-content: center;align-items: center;">
+    <img src="/assets/quickstart/create_pipeline/pipeline_progress.webp" alt="My first pipeline listed" style="width: 70%;">
+</div>
+
+**Congrats!** 🚀 You have now successfully created your first **Pipeline** a code environment, so you can benefit from Fabric's
+orchestration engine to crate scalable, versionable and comparable data workflows.
+Get ready for your journey of improved quality data for AI.

docs/get-started/create_syntheticdata_generator.md

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+# How to create your first Synthetic Data generator
+
+:fontawesome-brands-youtube:{ .youtube }
+Check this quickstart video on <a href="https://youtu.be/GsfggG9PhgE?si=ixlCaesd3cLFOCZm"><u>how to create your first Synthetic Data generator</u></a>.
+
+To generate your first synthetic data, you need to have a Dataset already available in your Data Catalog.
+Check this tutorial to see how you can <a href="upload_csv"><u>add your first dataset to Fabric’s Data Catalog</u></a>.
+
+With your first dataset created, you are now able to start the creation of your Synthetic Data generator. You can either
+select **"Synthetic Data"** from your left side menu, or you can select **"Create Synthetic Data"** in your project Home
+as shown in the image below.
+
+<div style="display: flex; justify-content: center;align-items: center;">
+    <img src="/assets/quickstart/synthetic_data/create_synthetic_data.webp" alt="Create Synthetic Data" style="width: 75%;">
+</div>
+
+You'll be asked to select the dataset you wish to generate synthetic data from and verify the columns you'd like to
+include in the synthesis process, validating their *Variable* and *Data Types*.
+
+!!! Tip "Data types are relevant for synthetic data quality"
+    Data Types are important to be revisited and aligned with the objectives for the synthetic data as they can highly impact the quality
+    of the generated data. For example, let's say we have a column that is a "Name", while is some situations it would make sense
+    to consider it a String, under the light of a dataset where "Name" refers to the name of the product purchases, it might be more
+    beneficial to set it as a Category.
+
+<div style="display: flex; justify-content: center;align-items: center;">
+    <img src="/assets/quickstart/synthetic_data/synthetic_data_columns_sel.webp" alt="Configure Metadata" style="width: 75%;">
+</div>
+
+Finally, as the last step of our process it comes the **Synthetic Data** specific configurations, for this particular case we
+only need to define a *Display Name,* and we can finish the process by clicking in the **"Save"** button as per the image below.
+
+<div style="display: flex; justify-content: center;align-items: center;">
+    <img src="/assets/quickstart/synthetic_data/synthetic_data_configuration.webp" alt="Save Synthetic Data configurations" style="width: 75%;">
+</div>
+
+Your **Synthetic Data** generator is now training and listed under **"Synthetic Data"**. While the model is being trained, the *Status* will be
+🟡, as soon as the training is completed successfully it will transition to 🟢 as per the image below.
+
+<div style="display: flex; justify-content: center;align-items: center;">
+    <img src="/assets/quickstart/synthetic_data/trained_synthetic_data.webp" alt="Synthetic data generator trained successfully" style="width: 75%;">
+</div>
+
+Once the Synthetic Data generator has finished training, you're ready to start generating your first synthetic dataset.
+You can start by exploring an overview of the model configurations and even download a PDF report with a comprehensive overview of your
+Synthetic Data Quality Metrics. Next, you can generate synthetic data samples by accessing the *Generation* tab or click on *"Go to Generation"*.
+
+<div style="display: flex; justify-content: center;align-items: center;">
+    <img src="/assets/quickstart/synthetic_data/synthetic_data_overview.webp" alt="Synthetic data generator overview" style="width: 75%;">
+</div>
+
+In this section, you are able to generate as many synthetic samples as you want.
+For that you need to define the number rows to generate and click *"Generate"*, as depicted in the image below.
+
+<div style="display: flex; justify-content: center;align-items: center;">
+    <img src="/assets/quickstart/synthetic_data/set_generation.webp" alt="Generate synthetic data records" style="width: 75%;">
+</div>
+
+A new line in your *"Sample History"* will be shown and as soon as the sample generation is completed you will be able to
+*"Compare"* your synthetic data with the original data, add as a Dataset with *"Add to Data Catalog"* and last but not the least
+download it as a file with *"Download csv"*.
+
+<div style="display: flex; justify-content: center;align-items: center;">
+    <img src="/assets/quickstart/synthetic_data/generated_synthetic_sample.webp" alt="Synthetic data generator trained" style="width: 75%;">
+</div>
+
+**Congrats!** 🚀 You have now successfully created your first **Synthetic Data** generator with Fabric.
+Get ready for your journey of improved quality data for AI.

docs/get-started/index.md

@@ -0,0 +1,10 @@
+# Get started with Fabric
+
+The get started is here to help you if you are not yet familiar with YData Fabric or if you just want to learn more about
+data quality, data preparation workflows and how you can start leveraging synthetic data.
+<a href="fabric_community"><u>Mention to YData Fabric Community</u></a>
+
+### 📚 <a href="upload_csv"><u>Create your first Data with the Data Catalog</u></a>
+### ⚙️ <a href="create_syntheticdata_generator"><u>Create your first Synthetic Data generator</u></a>
+### 🧪 <a href="create_lab"><u>Create your first Lab</u></a>
+### 🌀 <a href="create_pipeline"><u>Create your first data Pipeline</u></a>

docs/get-started/upload_csv.md

@@ -0,0 +1,60 @@
+# How to create your first Dataset from a CSV file
+
+:fontawesome-brands-youtube:{ .youtube }
+Check this quickstart video on <a href="https://youtu.be/1zYreRKsNGE"><u>how to create your first Dataset from a CSV file</u></a>.
+
+To create your first dataset in the **Data Catalog**, you can start by clicking on **"Add Dataset"** from the Home section.
+Or click to **Data Catalog** (on the left side menu) and click **“Add Dataset”**.
+
+<div style="display: flex; justify-content: center;align-items: center;">
+    <img src="/assets/quickstart/upload_csv/welcome_add_dataset.png" alt="Add dataset from Home" style="width: 75%;">
+</div>
+
+After that the below modal will be shown. You will need to select a connector. To upload a CSV file, we need to select **“Upload CSV”**.
+
+<div style="display: flex; justify-content: center;align-items: center;">
+    <img src="/assets/quickstart/upload_csv/data_catalog_connectors.png" alt="Select connectors to storage" style="width: 45%;">
+</div>
+
+Once you've selected the **“Upload CSV”** connector, a new screen will appear, enabling you to upload your file and designate a name for your connector.
+This file upload connector will subsequently empower you to create one or more datasets from the same file at a later stage.
+
+<div style="display: flex; justify-content: center;align-items: center;">
+    <img src="/assets/quickstart/upload_csv/loading_area.png" alt="Upload file area" style="width: 45%;">
+    <img src="/assets/quickstart/upload_csv/load_csv_file.png" alt="Upload CSV file" style="width: 45%;">
+</div>
+
+With the *Connector* created, you'll be able to add a dataset and specify its properties:
+
+- **Name:** The name of your dataset;
+- **Separator:** This is an important parameter to make sure that we can parse your CSV correctly. The default value is “,”.
+- **Data Type:** Whether your dataset contains tabular or time-series (i.e., containing temporal dependency) data.
+
+<div style="display: flex; justify-content: center;align-items: center;">
+    <img src="/assets/quickstart/upload_csv/add_dataset_details.png" alt="Upload file area" style="width: 45%;">
+</div>
+
+Your created Connector *(“Census File”)* and Dataset *(“Census”)* will be added to the Data Catalog.
+As soon as the status is green, you can navigate your Dataset. Click in **Open Dataset** as per the image below.
+
+<div style="display: flex; justify-content: center;align-items: center;">
+    <img src="/assets/quickstart/upload_csv/open_dataset.png" alt="Upload file area" style="width: 75%;">
+</div>
+
+Within the **Dataset** details, you can gain valuable insights through our automated data quality profiling.
+This includes comprehensive metadata and an overview of your data, encompassing details like row count, identification
+of duplicates, and insights into the overall quality of your dataset.
+
+<div style="display: flex; justify-content: center;align-items: center;">
+    <img src="/assets/quickstart/upload_csv/dataset_overview.png" alt="Upload file area" style="width: 75%;">
+</div>
+
+Or perhaps, you want to further explore through visualization, the profile of your data with both univariate
+and multivariate of your data.
+
+<div style="display: flex; justify-content: center;align-items: center;">
+    <img src="/assets/quickstart/upload_csv/dataset_profiling.png" alt="Upload file area" style="width: 75%;">
+</div>
+
+**Congrats!** 🚀 You have now successfully created your first **Connector** and **Dataset** in Fabric’s Data Catalog.
+Get ready for your journey of improved quality data for AI.

docs/stylesheets/extra.css

@@ -40,3 +40,7 @@
     --md-footer-fg-color--light: hsla(0, 0%, 100%, 0.7);
     --md-footer-fg-color--lighter: hsla(0, 0%, 100%, 0.3);
   }
+
+.youtube {
+  color: #EE0F0F;
+}

mkdocs.yml

@@ -6,7 +6,14 @@ dev_addr: 0.0.0.0:1235
 site_dir: "static/docs"
 nav:
   - Welcome: 'index.md'
-  - Get started with Fabric: "get-started/fabric_community.md"
+  - Get started with Fabric:
+      - "get-started/index.md"
+      - Quickstart:
+          - How to create your first Dataset from a CSV file: "get-started/upload_csv.md"
+          - How to create your first Synthetic Data generator: "get-started/create_syntheticdata_generator.md"
+          - How to create your first Lab: "get-started/create_lab.md"
+          - How to create your first Pipeline: "get-started/create_pipeline.md"
+      - Fabric Community: "get-started/fabric_community.md"
   - SDK:
       - Overview: "sdk/index.md"
       - Installation: 'sdk/installation.md'