Big Data Engineering
Build the skills needed to design, manage, and optimize large-scale data systems used by modern enterprises. This track equips learners with expertise in distributed computing, real-time data processing, cloud-based analytics, and monitoring tools essential for handling high-volume, high-velocity data.
With hands-on training across leading big data platforms, you will learn to architect powerful pipelines that process and analyze data at scale.
What You Will Learn
A comprehensive blend of theory and practical sessions covering:
Distributed Data Processing
Master Hadoop, Spark, and other distributed frameworks for large-scale data computation.
Real-Time Data Streaming
Work with Kafka, Flink, and Beam to build fast, event-driven, real-time analytics pipelines.
Cloud Big Data Platforms
Gain experience in Azure Databricks for scalable data engineering and machine learning workflows.
Monitoring & Observability
Use Splunk, Grafana, and Prometheus to monitor systems, logs, and performance metrics.
Troubleshooting & Optimization
Solve real-world pipeline issues, optimize performance, and ensure reliability in production environments.
Courses Offered
- Big Data Hadoop Ecosystem
- Spark, Scala & Kafka
- Azure Databricks
- Splunk Training
- Apache Flink & Beam
- Elastic Stack & Logging
- Grafana & Prometheus
- Troubleshooting & Performance Optimization
Who Can Study
This track is ideal for:
- Data Engineers & Developers working with distributed environments
- Cloud Professionals managing enterprise-scale data platforms
- Analysts & Professionals specializing in streaming and real-time analytics
- Students & IT Graduates aiming for careers in big data engineering