In this video, we walk through a complete Azure Data Engineering Project from start to finish.
We begin by moving on-premises data to the Azure Cloud using Azure Data Factory with a Self-hosted Integration Runtime. Once the data is in Data Lake Storage, we perform data transformations in Azure Databricks, securely connected via Azure Key Vault.
Finally, we visualize the cleaned and structured data by building an interactive Power BI dashboard. Throughout the project we implement Medallion Architecture to ensure efficient data processing and management in the project.
This end-to-end pipeline covers real-world Azure services and is perfect for your portfolio or interview preparation.
Technologies Used:
1. Azure Data Factory
2. Self-hosted Integration Runtime
3. Azure Data Lake Storage Gen2
4. Azure Databricks
5. Azure Key Vault
6. Power BI
Timecodes
0:00 - Coming up
0:30 - Introduction
1:40 - Architecture Diagram
3:48 - Downloading dataset from Maven Analytics
7:12 - Create an Azure Resource Group
8:45 - Create an Azure Storage account
16:30 - Create an Azure Data Factory
19:18 - How to move on-premises data to Azure cloud using Self Hosted Integration Runtime (SHIR)
26:10 - Creating Pipeline in Azure Data Factory
41:30 - Recap
43:08 - How to connect Azure Databricks with Azure Storage Account
49:38 - How to create an Azure Databricks scope
50:50 - Create an Azure Key Vault
54:35: Create an Azure Key Vault Secret
56:50 - How to create an Azure Databricks Cluster
59:30 - Transformations in Azure Databricks
1:21:00 - How to move transformed data in Azure Databricks to Azure Storage Account
1:42:30 - How to connect Power BI to Azure Storage account using Account keys
1:52:50 - Developing a dashboard in Power BI
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