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

===================================================================
1. SUBSCRIBE FOR MORE LEARNING :
   / @cloudquicklabs  
===================================================================
2. CLOUD QUICK LABS - CHANNEL MEMBERSHIP FOR MORE BENEFITS :
   / @cloudquicklabs  
===================================================================
3. BUY ME A COFFEE AS A TOKEN OF APPRECIATION :
https://www.buymeacoffee.com/cloudqui...
===================================================================



*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.



📌 *Don’t forget to like, share, and subscribe to the channel for more cloud tutorials!*
✨ **Comments and Questions**: Drop your queries in the comment section, and we’ll be happy to assist you!

#azure
#etl
#azuredatabricks
#azureblobstorage
#azuretablestorage
#adfpipeline
#datafactory
#azuretutorial
#cloudcomputing
#dataengineering
#azuredatafactory
#cloudstorage
#azureetl
#datapipeline
#azurestorage
#azuredataflow
#cloudtools
#dataanalytics
#azureservices
#cloudtutorial
#cloudquicklabs


Disclaimer: Unauthorized copying, reproduction, or distribution of this video content, in whole or in part, is strictly prohibited. Any attempt to upload, share, or use this content for commercial or non-commercial purposes without explicit permission from the owner will be subject to legal action. All rights reserved.