Azure Event Hubs

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

Azure Event Hubs

 Azure Event Hubs is a big data streaming platform and event ingestion service that can receive and process millions of events per second. It acts as a front door for real-time data pipelines, enabling high-throughput data ingestion from websites, applications, sensors, devices, and more.

Event Hubs is ideal for telemetry data and event stream processing scenarios. Data sent to an Event Hub can be transformed and stored using services like Azure Stream Analytics, Azure Functions, or even Apache Kafka-compatible clients.

Event Hubs supports partitioning, allowing data to be divided across multiple consumers to scale the processing. It also ensures durability with data retention for up to 90 days and replayability so consumers can reprocess events.

There are two main models:

Standard: Suited for most production workloads.

Dedicated: For extremely high-throughput scenarios with complete isolation.

Key features include:

Real-time analytics integration.

Capture feature for automatic storage of event streams to Azure Blob Storage or Data Lake.

Kafka support: Allows Kafka producers and consumers to connect without changing protocols.

Common use cases are application logging, live dashboards, telemetry processing, and anomaly detection. It integrates well with the Azure ecosystem and offers seamless scalability for enterprise-level event streaming.

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Real-Time Data & Streaming

Machine Learning with Databricks

Filestore (NFS)

Transfer Service

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