Data Lakehouse Implementation

 

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

Data Lakehouse Implementation

A Data Lakehouse implementation combines the flexibility of a data lake with the structured management of a data warehouse, enabling organizations to store, process, and analyze all types of data—structured, semi-structured, and unstructured—in one unified platform. In Google Cloud, this can be achieved using Cloud Storage for raw data storage, BigQuery for analytics, and tools like Dataproc or Dataflow for processing. The architecture typically involves ingesting data from multiple sources (databases, streaming platforms, APIs), storing it in its native format in the lake layer, and then organizing curated datasets in the warehouse layer for analytics.

Key steps include setting up scalable storage, defining metadata with Dataplex, applying governance and security controls via IAM and DLP, and integrating with BI tools for visualization. Benefits of a lakehouse include reduced data silos, lower costs, and support for advanced analytics and AI/ML on the same dataset. This approach eliminates the need for separate ETL pipelines between lakes and warehouses, offering faster insights and a more streamlined data architecture.

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ETL/ELT Project with ADF

End-to-End Data Pipeline Project

Projects, Labs & Interview Prep

DAX Basics for Data Engineers

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