Intermediate Big Data Training Program
Start at $299 and master big data engineering in 8-12 weeks. This hands-on training program teaches you to design, build, and optimize data pipelines that process massive datasets at scale.
What You'll Learn
Career-ready skills, mapped to real hiring outcomes.
- Build scalable data pipelines using Apache Spark and distributed computing frameworks
- Design and optimize data warehouses with Hadoop, cloud storage, and SQL optimization techniques
- Implement real-time data streaming solutions with Kafka and event-driven architectures
- Deploy and manage big data clusters using containerization and orchestration tools like Docker and Kubernetes
- Extract actionable insights by performing advanced analytics on terabyte-scale datasets
- Monitor data quality, troubleshoot performance bottlenecks, and implement automated data validation workflows
Course Curriculum
5 modules · structured, self-paced.
Module 01 Big Data Foundations & Ecosystem
- HDFS architecture
- MapReduce principles
- Data lake vs data warehouse
- Modern big data stack overview
Module 02 Spark & Distributed Processing
- PySpark fundamentals
- RDD vs DataFrame APIs
- Query optimization
- Partitioning strategies
- Cluster resource allocation
Module 03 Data Pipeline Design & ETL
- ETL vs ELT patterns
- Apache Airflow orchestration
- Data transformation best practices
- Error handling and recovery
- Production pipeline deployment
Module 04 Real-Time Streaming & Analytics
- Kafka topic design
- Stream processing with Spark Streaming
- Windowing and aggregations
- Lambda and Kappa architectures
Module 05 Capstone: Build Production Data Architecture
- End-to-end pipeline implementation
- Performance tuning and monitoring
- Scaling to production workloads
Who This Is For
Built for these professionals.
- 01 Data analysts ready to engineer pipelines and move beyond SQL into distributed computing frameworks.
- 02 Junior data engineers with 1-2 years experience seeking to master Spark, Kafka, and production-grade architecture patterns.
- 03 Software engineers transitioning into big data roles who need hands-on training with modern data stack tools.
- 04 Analytics professionals aiming for Big Data Engineer or Data Architect positions with salary growth beyond $120K.
Common questions about this program.
Do I need prior Spark experience to enroll in this intermediate program?
No, but you should have basic Python knowledge and familiarity with SQL queries. We assume you understand relational databases and can write simple scripts. The program starts with Spark fundamentals before progressing to advanced optimization and architecture patterns.
Are the live labs on real cloud infrastructure, and can I use them for my portfolio?
Yes. All hands-on exercises run on AWS or GCP infrastructure, and you'll build a capstone project that demonstrates production-ready pipeline design. You can showcase this work in interviews and on LinkedIn.
How does this training prepare me for Big Data Engineer or Data Architect roles?
The curriculum covers distributed systems design, ETL orchestration, real-time streaming, and performance optimization—all core competencies hiring managers expect. The capstone project simulates real-world scenarios you'll face in these roles, and instructor James Okafor includes industry best practices from 10 years of experience.
Can I complete this program while working full-time, and is it self-paced?
The program runs 8-12 weeks depending on your pace, with scheduled live labs and cohort-based learning to keep you accountable. Most working professionals complete modules over 6-8 hours per week. Recorded sessions are available if you miss live classes.
Get Started Today
Ready to start your
big data career?
Enroll now at $299 and join the next cohort—limited seats available for hands-on lab access.