https://github.com/betkh/azuerdatabricks-setupguide
https://github.com/betkh/azuerdatabricks-setupguide
Last synced: over 1 year ago
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- Host: GitHub
- URL: https://github.com/betkh/azuerdatabricks-setupguide
- Owner: BeTKH
- Created: 2024-12-19T21:58:53.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-12-19T22:05:10.000Z (over 1 year ago)
- Last Synced: 2024-12-19T23:18:14.703Z (over 1 year ago)
- Language: Python
- Size: 4.42 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Azure Databricks Setup Guide
## Overview
The Azure Databricks setup guide demonstrates how to create a Databricks workspace, link it to Azure, and effectively manage data pipelines. This setup is ideal for anyone looking to establish a cloud-based analytics environment and streamline data processing tasks.
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## Key Highlights
### Setting Up Databricks and Azure Integration
This guide covers the essential steps needed to link Databricks with an Azure storage account:
- **Step 1: Databricks Workspace Setup** - set up a new Databricks workspace in Azure, configure security settings, and link it to your Azure storage following `DataBricksSetup.pdf` file.
- **Step 2: Uploading Data** - Upload sample data files from the `InputData` folder in the repo to Azure storage.
- **Step 3: Configuring GitHub Integration** - Integrate Databricks with GitHub for version control and seamless data management. Clone the git repo, manipulate data using Python `Setup.py`, and push changes back to GitHub.
### Data Manipulation and Analysis
The guide includes:
- Instructions for manipulating data using Databricks notebooks.
- Steps for saving cleaned data files back to Azure.
- Capture of evidence through screenshots and renamed CSV files to demonstrate data processing.
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This guide is a personal documentation to show how to set up an Azure Databricks workspace which can be used in a data engineering project.