Data Engineering with Apache Spark

Course Overview

This comprehensive program covers Apache Spark architecture, RDDs, DataFrames, and Spark SQL for processing large-scale datasets efficiently. Participants develop skills in building production-grade ETL pipelines that handle millions of records with optimized performance characteristics.

The curriculum includes stream processing with Structured Streaming, machine learning workflows using MLlib, and graph processing with GraphX. Students learn performance tuning techniques, memory management strategies, and cluster deployment configurations for enterprise environments.

Hands-on projects focus on implementing data quality frameworks, optimizing shuffle operations, and integrating Spark with data lakes. The course addresses real-world challenges in distributed computing, including fault tolerance, resource allocation, and monitoring complex data pipelines.

Technical sessions cover Catalyst optimizer internals, Tungsten execution engine, and advanced DataFrame operations. Participants work with production scenarios involving data partitioning, broadcast variables, and accumulator patterns for building scalable analytics systems.

Expected Outcomes

Pipeline Development

Design and implement ETL pipelines that process terabytes of data with appropriate partitioning and caching strategies

Performance Optimization

Apply tuning techniques to reduce processing time and resource consumption in distributed computing environments

Stream Processing

Build real-time analytics systems using Structured Streaming with windowing operations and stateful computations

Data Quality

Implement validation frameworks and monitoring solutions to maintain accuracy throughout processing workflows

Technical Stack

Core Framework

Apache Spark 3.5
Scala and PySpark APIs
Spark SQL engine
DataFrame operations

Data Processing

Structured Streaming
MLlib algorithms
GraphX processing
Delta Lake integration

Infrastructure

Cluster deployment
Resource managers
Monitoring tools
Data lake connectors

Development Environment

Students work with professional development setups including Databricks Community Edition, local Spark clusters, and cloud-based environments. The curriculum emphasizes version control with Git, code review practices, and collaborative development workflows used in engineering teams.

Projects incorporate CI/CD pipelines for automated testing and deployment of Spark applications. Participants gain experience with Jupyter notebooks for exploratory analysis, unit testing frameworks for data transformations, and logging patterns for production debugging.

Who Should Attend

Ideal For

Data engineers working with large-scale processing systems
Software developers transitioning to distributed computing
Data analysts expanding into pipeline development
Technical team leads planning big data implementations

Prerequisites

Programming experience in Python or Scala
Understanding of SQL and relational databases
Familiarity with Linux command line operations
Basic knowledge of distributed systems concepts

Skill Development Tracking

Technical Assessments

Progress evaluations through coding assignments that require implementing specific Spark functionality. Students receive detailed feedback on code structure, performance characteristics, and adherence to development patterns. Weekly exercises build incrementally toward a final project demonstrating pipeline architecture skills.

6

Programming assignments

3

Pipeline projects

1

Capstone implementation

Performance Metrics

Track development through measurable indicators including code efficiency, processing optimization, and architecture decisions. Students benchmark their implementations against industry standards and receive guidance on areas requiring additional practice. The curriculum emphasizes continuous improvement through iterative refinement.

Job execution time improvements
Resource utilization efficiency
Data quality validation scores
Code review feedback integration

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Join data engineers building production-grade processing pipelines for enterprise analytics systems in Tokyo

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