ETL |Data Engineering |Load Data |Azure SQL Database to Azure Synapse Analytics | Synapse Pipeline

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Absolutely! this video on "ETL | Data Engineering | Load Data | Azure SQL Database to Azure Synapse Analytics Using Synapse Pipeline" would delve into the process of Extract, Transform, and Load (ETL) within the context of Azure's ecosystem.
Introduction to ETL: The video might begin by explaining the concept of ETL, emphasizing its importance in data engineering. It would touch upon the steps involved: extracting data from a source, transforming it to fit operational needs, and loading it into a target database.
Azure SQL Database and Synapse Analytics: It would likely showcase the Azure SQL Database as the source and Azure Synapse Analytics as the target, discussing their respective features and advantages for handling large-scale data.
Synapse Pipeline: The video would introduce Synapse Pipeline as the tool for orchestrating the ETL process. It might cover its capabilities, such as building scalable, automated pipelines for data movement and transformation.
Demonstration: The bulk of the video might be a hands-on demonstration, showing step-by-step how to set up the ETL process using Synapse Pipeline. This could involve:
Connecting to the Azure SQL Database.
Defining the data extraction process.
Transforming the data to meet analytical requirements.
Creating the Synapse Pipeline to load the transformed data into Azure Synapse Analytics.
Best Practices and Tips: The video could offer insights into best practices for efficient ETL processes within Azure. This might include considerations for data security, performance optimization, and cost-effectiveness.
Conclusion: Finally, the video would wrap up by summarizing the key points covered and possibly provide information on where to find additional resources or further learning on this topic.
Overall, the video would aim to guide viewers through the process of leveraging Azure services for seamless data movement and transformation, specifically focusing on transferring data from Azure SQL Database to Azure Synapse Analytics using Synapse Pipeline.
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[@snehitvaddi]

Good to see how you debugged the issue on spot. Loved it.

[@cloudquicklabs]

Thank you for wat have my videos.
Glad that it helped you.

[@calvinharris4771]

25:12 Could you pls specify more on the Remove duplicate option in the query (to prevent data-redundancy if we run the pipeline twice ) ?

[@cloudquicklabs]

Thank you for watching my videos.
I am a new video on incremental data load here.
Please wait for the same.

[@naren06938]

GCP having Most powerful BigQuery, Dataflow in a descent manner with Beam SDK pipelines too flexible for complex ETLs

[@cloudquicklabs]

Indeed , GCP seems to strong in Data engineering and AI space.

[@naren06938]

Your Explanation always awesome...but why Azure confusing people by introducing same Services with multiple names by just small Features adding....ADF simple & easy for this task, Again Synapse inside Same UI too, again Fabric same tasks PowerBI only addition....AWS, GCP never doing like this Mad things

[@cloudquicklabs]

Thank you for watching my videos.
Glad that you liked my videos and it helped you.
Indeed As pointed , Azure tries to provide variety and looses focus one grand product of one purpose.