ETL/ELT Project with ADF

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

ETL/ELT Project with ADF 

An ETL/ELT Project with Azure Data Factory (ADF) involves building data integration workflows to extract, transform, and load (ETL) or extract, load, and transform (ELT) data across various sources and destinations. In ETL, ADF pipelines extract data from sources like SQL databases, APIs, or blob storage, transform it using Data Flows or Azure Databricks, and load it into targets like Azure SQL Data Warehouse or Data Lake. In ELT, data is first loaded into the target system (e.g., Synapse Analytics) and transformed using SQL-based processing. ADF offers over 90 built-in connectors for on-premises and cloud data, enabling hybrid integration. Pipelines in ADF can be triggered on a schedule, event, or manually, with activities controlling data movement, transformation, and orchestration. Integration Runtimes handle execution in self-hosted or cloud environments. Features like parameterization, logging, and monitoring ensure flexibility and governance. This makes ADF ideal for scalable, automated, and maintainable enterprise data pipelines.

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End-to-End Data Pipeline Project

Projects, Labs & Interview Prep

DAX Basics for Data Engineers

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