ETL | Azure Data Factory | Migrate Azure Blob Storage to Azure Table Storage With ADF Data Pipeline

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### **Video Description**
In this step-by-step tutorial, we explore how to build an **Extract, Transform, Load (ETL)** process using **Azure Data Factory (ADF)** to move data from **Azure Blob Storage** to **Azure Table Storage**. Whether you're a beginner or an experienced Azure user, this video offers a clear and concise guide to mastering ETL workflows in Azure.

### **Key Highlights**
1. **Introduction to ETL and Azure Data Factory**
- Understand the basics of ETL and why it’s critical in modern data workflows.
- Learn how Azure Data Factory serves as a powerful tool for building scalable data pipelines.

2. **Setting Up Azure Blob Storage**
- Demonstration of creating an Azure Blob Storage account and uploading sample data files (e.g., CSV or JSON).
- Overview of blob containers and their use in storing unstructured data.

3. **Configuring Azure Table Storage**
- Learn how to create an Azure Table Storage account for storing structured data.
- Explanation of rows, partition keys, and row keys in Azure Table Storage.

4. **Creating an Azure Data Factory Pipeline**
- Setting up a new Azure Data Factory instance.
- Step-by-step guide to creating an ETL pipeline to extract data from Azure Blob Storage, transform it, and load it into Azure Table Storage.

5. **Pipeline Configuration and Transformation Logic**
- Configuring linked services to connect Azure Blob Storage and Azure Table Storage.
- Using ADF activities like Copy Data, Mapping Data Flow, or Wrangling Data Flow for data transformation.
- Designing transformation logic to clean, filter, or modify the data before loading it into Table Storage.

6. **Testing and Monitoring the Pipeline**
- Running the ADF pipeline and validating the results.
- Using the ADF monitoring dashboard to track pipeline execution and debug errors.

7. **Best Practices for ADF Pipelines**
- Tips for optimizing pipeline performance and reducing costs.
- Security considerations when handling sensitive data in Azure.

### **Target Audience**
- Data Engineers looking to streamline their ETL processes in Azure.
- Cloud Architects and Developers interested in learning Azure Data Factory.
- Professionals seeking a practical example of integrating Azure Blob and Table Storage.

### **Takeaways**
By the end of this video, you’ll be able to:
1. Set up Azure Blob Storage and Azure Table Storage.
2. Design and deploy an ADF pipeline for ETL processes.
3. Transform and load data efficiently between Azure services.

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✨ **Comments and Questions**: Drop your queries in the comment section, and we’ll be happy to assist you!

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