Copy Activity (Blob to SQL etc.)

  Azure Cloud Data Engineering Training in Hyderabad – Quality Thoughts

Quality Thoughts offers one of the best Azure Cloud Data Engineering courses in Hyderabad, ideal for graduates, postgraduates, working professionals, or career switchers. The course combines hands-on learning with an internship to make you job-ready in a short time.

Our expert-led training goes beyond theory, with real-time projects guided by certified cloud professionals. Even if you’re from a non-IT background, our structured approach helps you smoothly transition into cloud roles.

The course includes labs, projects, mock interviews, and resume building to enhance placement success.

Why Choose Us?

     1. Live Instructor-Led Training

     2. Real-Time Internship Projects

     3.Resume & Interview Prep

    4 .Placement Assistance

    5.Career Transition Support

Join us to unlock careers in cloud data engineering. Our alumni work at top companies like TCS, Infosys, Deloitte, Accenture, and Capgemini.

Note: Azure Table and Queue Storage support NoSQL and message handling for scalable cloud apps 

Copy Activity (Blob to SQL etc.)

Copy Activity in Azure Data Factory (ADF) or Synapse Pipelines is used to move data from a source to a destination (sink). It supports over 100 connectors, including Azure Blob Storage, Azure SQL Database, Data Lake, REST API, Amazon S3, Google Cloud, and more.

For example, when copying data from Azure Blob Storage to Azure SQL Database, the process involves:

Source Dataset: Defines the format and location of the data (e.g., CSV file in a Blob container).

Sink Dataset: Defines where the data will be written (e.g., a table in Azure SQL).

Copy Activity Settings: Control data mapping, performance (parallelism, batch size), fault tolerance, and more.

The Copy Activity supports schema mapping, column transformations, and data type conversions. It can handle structured, semi-structured (JSON, Parquet), or unstructured data.

It also includes monitoring, logging, and performance metrics via Azure Monitor. You can configure retries, logging, and alerts for better operational control.

This activity is ideal for ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) workflows where data is moved from storage to analytics systems. It's secure, scalable, and can handle large volumes efficiently using integration runtimes—Azure-hosted or self-hosted—for cloud and on-prem connectivity.

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Triggers and Scheduling

Mapping & Wrangling Data Flows

Linked Services and Datasets

Cloud Functions

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