Advanced Java Programming

Get ready to master advanced Java tools—functional programming, concurrency, NIO.2, JVM tuning—culminating in building LogHog, a high-performance log analysis tool.

Master functional programming in Java using Lambda expressions and Streams for readable, efficient data pipelines, null safety, and best practices in production-ready code.

Master functional programming in Java by processing data with Streams and Lambdas—write declarative, resilient code using map, filter, reduce, and debug with peek.

Master safe, efficient file I/O in Java using NIO.2: separate file paths from operations, lazily stream data, enforce charsets, buffer streams, and manage resources with try-with-resources.

Learn to process large files efficiently in Java using NIO.2, lazy streaming with Files.lines(), safe resource management, and build a file-based ETL pipeline for security log analysis.

Understand essential design patterns (Builder, Singleton, Factory, Strategy, Observer), avoid "Patternitis," and learn to apply the right pattern only when business needs require it.

Learn to implement thread-safe Singletons using double-checked locking and build immutable, validated objects with the Builder pattern in Java applications.

Master safe, efficient concurrent programming in Java: avoid manual threads, handle shared state, use thread pools, atomic variables, and build async pipelines with CompletableFuture.

Learn to build fast, resilient concurrent Java apps using thread pools, CompletableFuture, and scatter-gather to handle latency and failures without blocking or crashes.

Understand Java's parallel streams and Fork/Join framework—how work is split, risks of the common pool, and when to use parallelism safely and efficiently.

Learn when and how to use Java parallel streams and Fork/Join for performance: benchmark with JMH, avoid shared state, and understand when parallelization helps or hurts.

Learn how Java's Vector API enables SIMD operations for high-performance computing, optimizing data analytics, ML, and scientific tasks with portable, hardware-accelerated vector processing.

Unlock Java high-performance computing by using the Vector API for SIMD operations to massively accelerate array tasks and handle non-divisible data with vector and tail loops.

Understand JVM memory architecture, generational GC mechanics, observability techniques, and tools to tune memory usage and detect memory leaks for reliable Java microservices.

Learn to diagnose and fix Java memory leaks by analyzing heap dumps, profiling GC behavior, and tuning memory management for robust application performance.

Explore Java Reflection API to inspect, instantiate, and manipulate code at runtime, powering frameworks like Spring but with major safety and performance trade-offs.

Explore Java's Reflection API to inspect, access, and manipulate class members at runtime—enabling dynamic utilities and breaking encapsulation for advanced programming tasks.

Learn how custom annotations and meta-annotations enable declarative meta-programming in Java, and how reflection is used to process annotations for dynamic configuration at runtime.

Learn to create custom Java annotations and process them at runtime using reflection to enforce validation and build reusable, metadata-driven frameworks.

Master advanced Java exception handling: translate and chain exceptions, use centralized handlers, manage async errors, and ensure robust, fail-safe system design for resilient APIs.

Master robust Java exception handling by wrapping low-level errors in custom exceptions, preserving stack traces, and maintaining clean architectural boundaries.

Review and celebrate key skills gained: Java lambdas, streams, file I/O, design patterns, reflection, and JVM performance tuning, ready for real-world application.