From 852f27dde120c3d0b17ac1b93f3819d943f6c1b4 Mon Sep 17 00:00:00 2001 From: Fabiana <30911746+fabclmnt@users.noreply.github.com> Date: Wed, 29 Nov 2023 20:56:45 -0800 Subject: [PATCH] docs: fix images (#74) Co-authored-by: Fabiana Clemente --- docs/get-started/create_lab.md | 18 ++++++++++-------- docs/get-started/upload_csv.md | 18 ++++++++++-------- docs/index.md | 2 -- 3 files changed, 20 insertions(+), 18 deletions(-) diff --git a/docs/get-started/create_lab.md b/docs/get-started/create_lab.md index 49414b94..cd9d8850 100644 --- a/docs/get-started/create_lab.md +++ b/docs/get-started/create_lab.md @@ -6,22 +6,24 @@ To create your first **Lab**, you can use the **“Create Lab”** from Fabric module by selecting it on the left side menu, and clicking the **“Create Lab”** button.
- Select create a lab from Home +

+ Select create a lab from Home +

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.
- Select an IDE - Python or R + Select an IDE + Python or R
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.
- Select the bundle for development + Select the bundle for development
As a last step, you will be asked to configure the infrastructure resources for this new environment as well as giving it @@ -29,7 +31,7 @@ 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.
- Select the computational resources + Select the computational resources
Finally, your Lab will be created and added to the "Labs" list, as per the image below. The status of the lab will be @@ -37,14 +39,14 @@ Finally, your Lab will be created and added to the "Labs" list, as per the image As soon as the status changes to 🟢, you can open your lab by clicking in the button as shown below:
- Open your lab environment + Open your lab environment
Create a new notebook in the JupyterLab and give it a name. You are now ready to start your developments!
- Create a new notebook - Notebook created + Create a new notebook + Notebook created
**Congrats!** 🚀 You have now successfully created your first **Lab** a code environment, so you can benefit from the most diff --git a/docs/get-started/upload_csv.md b/docs/get-started/upload_csv.md index 3e86c12d..05297bf1 100644 --- a/docs/get-started/upload_csv.md +++ b/docs/get-started/upload_csv.md @@ -7,21 +7,21 @@ To create your first dataset in the **Data Catalog**, you can start by clicking Or click to **Data Catalog** (on the left side menu) and click **“Add Dataset”**.
- Add dataset from Home + Add dataset from Home
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”**.
- Select connectors to storage + Select connectors to storage
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.
- Upload file area - Upload CSV file + Upload file area + Upload CSV file
With the *Connector* created, you'll be able to add a dataset and specify its properties: @@ -31,29 +31,31 @@ With the *Connector* created, you'll be able to add a dataset and specify its pr - **Data Type:** Whether your dataset contains tabular or time-series (i.e., containing temporal dependency) data.
- Upload file area + Upload file area
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.
- Upload file area + Upload file area
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. +

Data-Centric AI Approach

+
- Upload file area + Upload file area
Or perhaps, you want to further explore through visualization, the profile of your data with both univariate and multivariate of your data.
- Upload file area + Upload file area
**Congrats!** 🚀 You have now successfully created your first **Connector** and **Dataset** in Fabric’s Data Catalog. diff --git a/docs/index.md b/docs/index.md index 3d91373e..8e3ea2c4 100644 --- a/docs/index.md +++ b/docs/index.md @@ -9,11 +9,9 @@ acceleration to empower data science, analytics, and data engineering teams.

Data-Centric AI Approach

- ### Try Fabric - Get started with Fabric Community - ## Why adopt YData Fabric? With Fabric, you can standardize the understanding of your data, quickly identify data quality issues, streamline and