About This Program
Master building production-grade ETL pipelines on AWS with Python, Spark, and key AWS services. Become job-ready in 1 month with hands-on projects and expert mentorship.
Course Overview
This 1-month advanced internship program focuses on building ETL pipelines on AWS. Unlike conventional training providers, this program emphasizes hands-on learning, professional readiness, and real-world project execution. By the end, participants will be capable of designing, building, and deploying production-grade ETL pipelines using a comprehensive suite of AWS services.
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
AWS Services (Glue, S3, Redshift, Lambda, Kinesis, Step Functions, CloudWatch, DynamoDB)
Python
SQL
Apache Spark
Pandas
Git & GitHub
AWS CodePipeline
AWS CodeBuild
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, project-focused, job-ready) | 4–6 Months (longer, less focused, often theory-heavy) |
Learning Format | Live Classes + Mentor Support + Real Projects + Code Reviews | Mostly self-paced, recorded videos |
Core Skills & Languages | Python + SQL + PySpark (hands-on from Day 1) | Often limited to SQL basics or only theoretical coverage |
AWS Services | Glue, S3, Redshift, Lambda, Kinesis, Step Functions, DynamoDB, CloudWatch | Covers only a subset (commonly S3 + Redshift, Glue basics) |
ETL Pipeline Development | Batch + Real-time pipelines, automation with Lambda, orchestration with Step Functions | Typically batch-only, little/no real-time focus |
Version Control & CI/CD | Git/GitHub + AWS CodePipeline + CodeBuild | Rarely covered, deployments manual or skipped |
Capstone Project | End-to-end ETL pipeline (Batch + Real-time) deployed on AWS | Case studies or toy datasets only |
Mentorship & Code Reviews | Weekly reviews, 1:1 mentoring, professional practices | Very limited or none |
Career & Placement Support | Portfolio projects, GitHub-ready code, mock interviews, resume prep | No structured support |
Job Roles After Program
Explore exciting career opportunities
Data Engineer
ETL Developer
Cloud Data Engineer
Big Data Engineer
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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