About This Program
Master building production-grade, real-time data streaming pipelines on Azure with services like Event Hubs, Stream Analytics, and Databricks. Become job-ready in 1 month with live classes, hands-on projects, and expert mentorship.
Course Overview
This 1-month advanced internship program is designed to equip learners with hands-on expertise in real-time data streaming and analytics using Microsoft Azure. Unlike traditional training academies, this program provides a professional, project-based learning environment with mock interviews and industry-standard evaluation. By the end, participants will be able to ingest, process, and analyze streaming data using Azure tools, building end-to-end pipelines that integrate IoT, event hubs, and Power BI.
What You Will Learn
Hands-on Projects
Apply your learning through real-world projects that build your portfolio
Unique method to make you professionally rich
Progress through our comprehensive program which makes qualified professional to earn their awards
Bronze Award
Begin your journey with foundational skills and basic certification.
Course Curriculum
Technologies You Will Learn
Master the most in-demand tools and technologies for data analytics
Microsoft Azure
Azure Event Hubs, Azure IoT Hub, Azure Stream Analytics
Azure Databricks (Structured Streaming with Spark), Azure Synapse Analytics, Azure Data Lake Storage Gen2
Power BI
Azure Functions, Logic Apps, GitHub Actions for CI/CD
SQL, Python (PySpark), C# (optional)
What Our Students Say
Hear from our students who have transformed their careers
Why Choose Edjamon?
Discover the difference between Edjamon and traditional platforms
| Features & Criteria | EdJAMON | Others |
|---|---|---|
Duration | 1 Month (intensive, job-ready, real-world focused) | 4–6 Months (longer, more generic, less deployment-focused) |
Learning Format | Live Classes + Mentor Support + Real Projects + Code Reviews | Mostly self-paced or pre-recorded content |
Core Cloud Platform | Microsoft Azure (Event Hubs, IoT Hub, Stream Analytics, Data Lake, Synapse, Power BI) | Focus often on AWS/GCP, Azure streaming rarely covered in depth |
Data Ingestion & Processing | IoT Hub + Event Hubs + Stream Analytics (real-time ingestion & queries) | Basic Event Hub demos, limited real-world ingestion |
Advanced Processing | Complex Event Processing (CEP), windowing, joins, error handling, monitoring | CEP usually skipped or only theoretical |
Big Data Integration | Databricks Structured Streaming + Data Lake Gen2 + Synapse integration | Rarely combines Databricks with Synapse/Data Lake |
Visualization Tools | Power BI dashboards integrated with live streaming pipelines | Mostly Tableau/Excel dashboards, static, not real-time |
Automation & Orchestration | Azure Functions + Logic Apps for automation workflows | Rarely included |
CI/CD & Deployment | GitHub Actions for deployment pipelines | Manual deployment, CI/CD often skipped |
Monitoring & Optimization | Azure Monitor + Log Analytics + Cost Optimization strategies | Monitoring & cost control rarely taught |
Capstone Project | End-to-End streaming solutions (Smart City, Fraud Detection, E-Commerce, IoT Health) | Mini projects or toy examples only |
Mentorship & Code Reviews | Weekly reviews, 1:1 guidance, doubt clearing | Very limited or none |
Career & Placement Support | Portfolio projects, GitHub repos, mock interviews, resume prep | No structured career support |
Cost & Value | Affordable, outcome-driven, short-duration with industry relevance | Higher cost, longer duration, generic content |
Job Roles After Program
Explore exciting career opportunities
Azure Data Engineer (Streaming Focus)
Big Data Engineer / Data Streaming Engineer
Cloud Data Engineer
IoT Data Engineer
Analytics Engineer (Real-time BI)
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Power BI for Data Analytics Internship Program
Become a job-ready Power BI Developer/Analyst in 1 month. Master ETL with Power Query, advanced DAX (Time Intelligence, KPIs), and performance-oriented data modeling. Build and deploy end-to-end interactive dashboards with Row-Level Security (RLS).
Resources & References
Access curated external resources to deepen your understanding
LLMs & Generative AI
Official guide for GPT models and API usage
Pre-trained models, datasets, and community resources
Open-source LLaMA models and implementation guides
Claude AI research papers and documentation
RAG & Framework Docs
Complete guide to building LLM applications
Data indexing framework for LLM applications
Step-by-step RAG implementation
Efficient open-source LLM models
Vector Databases
Cloud-native vector database for AI applications
Facebook's efficient similarity search library
Open-source ML-first vector database
Open-source embeddings database
Best Practices & Learning
OpenAI's official prompt engineering best practices
Academic foundation for RAG architectures
Real-world RAG implementation patterns
Security and ethical considerations for AI apps
Deployment & DevOps
Containerization and deployment guide
CI/CD automation and workflow automation
Build data apps with simple Python scripts
Cloud deployment and infrastructure setup
Python & Development
Complete Python language reference
Modern Python web framework for APIs
Numerical computing with Python
Data manipulation and analysis library
Frequently Asked Questions
Find answers to common questions