Course Details

This course is appropriate for a wide range of professionals but not limited to:

• Data Engineers
• Big Data Engineers
• ETL Developers
• Data Architects
• Analytics Engineers
• Database Administrators

• Expert-led sessions with dynamic visual aids
• Comprehensive course manual to support practical application and reinforcement
• Interactive discussions addressing participants’ real-world projects and challenges
• Insightful case studies and proven best practices to enhance learning

By the end of this course, participants should be able to:

• Design end-to-end data engineering architectures for big data environments.
• Build scalable data ingestion and transformation pipelines.
• Implement distributed storage solutions using Hadoop ecosystem components.
• Develop high-performance data processing applications with Apache Spark.
• Optimize data workflows for reliability, scalability, and fault tolerance.
• Monitor, troubleshoot, and maintain enterprise big data platforms.

DAY 1

Foundations of Data Engineering & Big Data

• Evolution of modern data platforms
• Data engineering lifecycle
• Big data concepts and architecture
• Hadoop ecosystem overview
• Distributed storage fundamentals
• Introduction to HDFS and YARN
   

DAY 2

Hadoop Architecture & Data Processing

• Hadoop cluster components
• HDFS administration and management
• MapReduce concepts and execution
• Data partitioning and replication
• Resource management with YARN
• Performance and scalability considerations

DAY 3

Apache Spark Fundamentals

• Spark architecture and execution model
• RDDs, DataFrames, and Datasets
• Spark SQL fundamentals
• Data transformations and actions
• Spark application development
• Batch processing workflows

DAY 4

Building Data Pipelines

• Data ingestion strategies
• ETL and ELT pipeline design
• Integration with databases and cloud storage
• Workflow orchestration concepts
• Streaming data pipelines with SparkData quality and validation techniques
   

DAY 5

Optimization, Monitoring & Best Practices

• Spark performance tuning
• Cluster monitoring and troubleshooting
• Data governance and security
• Fault tolerance and recovery strategies
• Designing production-ready pipelines
• Capstone case study and implementation workshop