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    Exploring Design Patterns with Java

    Mastering Software Design
    This course provides a deep dive into object-oriented design patterns—essential tools for developing high-quality, maintainable software systems that meet requirements and ship on time. Geared towards developers and architects, the course blends formal design principles with real-world application, giving you both the conceptual foundation and the practical skills needed to build robust, reusable systems.

    Focusing primarily on the well-known Gang of Four (GoF) design patterns, the course explores the most important patterns across Creational, Structural, and Behavioral categories. You’ll also get exposure to additional patterns, including Dependency Injection (DI) and common Enterprise Architecture Patterns.

    While course examples are presented in Java, coding is not required during the labs. Instead, labs are hands-on and conceptual, focused on design thinking and applying patterns in practice through structured design exercises and projects.

    Note: Java experience is helpful, but the focus is on design—not language-specific syntax.

    Exploring Design Patterns with Java Objectives

    • By the end of this course you will:
    • Understand key object-oriented design pattern concepts and terminology
    • Apply patterns to design flexible scalable and maintainable software
    • Gain fluency with the most common GoF design patterns
    • Know when and how to use Creational Structural and Behavioral patterns
    • Understand key enterprise and architectural patterns
    • Design systems that reduce code duplication and improve modularity
    • Communicate effectively using the shared vocabulary of design patterns

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    Key Point of Training Programs

    We have different work process to go step by step for complete our working process in effective way.
    • Exploring Design Patterns with Java Prerequisites

      Java experience is helpful, but the focus is on design - not language-specific syntax.

    • Exploring Design Patterns with Java Training Format

      In-Person

      Online

    • Exploring Design Patterns with Java Outline

      Skills Covered
      Creational Patterns

      Factory Method – In-depth

      Abstract Factory – In-depth

      Singleton – In-depth

      Builder – Brief

      Prototype – Brief

      Structural Patterns

      Composite – In-depth

      Adapter – In-depth

      Proxy – In-depth

      Bridge – Brief

      Facade – Brief

      Decorator – Brief

      Behavioral Patterns

      Iterator – In-depth

      Strategy – In-depth

      Observer – In-depth

      Command – Introductory

      Chain of Responsibility – Introductory

      State, Visitor, Interpreter, Mediator – Brief

      Other Patterns

      Dependency Inversion (DI)

      Enterprise Patterns: MVC, DAO, Business Delegate, Value Object

      Session 1: Introduction to Design Patterns

      Overview of Object-Oriented Concepts & Interfaces

      Introduction to the Iterator Pattern (with Lab)

      History and Origins of Design Patterns

      Session 2: A Formal Approach to Design Patterns

      Introduction to UML for Pattern Representation

      Overview of the Gang of Four Patterns

      Navigating the GoF Patterns Catalog

      Session 3: Creational Patterns

      Factory Method Pattern (with Lab)

      Abstract Factory Pattern

      Singleton Pattern

      Brief look at Builder and Prototype Patterns

      Session 4: Behavioral Patterns

      Strategy Pattern (with Lab)

      Template Method Pattern

      Command & Chain of Responsibility Patterns

      Other Behavioral Patterns Overview

      Session 5: Structural Patterns

      Decorator Pattern

      Composite Pattern

      Proxy Pattern

      Façade and Bridge Patterns

      Session 6: Dependency Injection

      Overview of Dependency Inversion Principle

      Introduction to Dependency Injection

      Working with DI Containers

      Session 7: Enterprise Patterns

      Overview of Architectural Patterns

      Business Delegate

      Value Object

      DAO / Repository Pattern

      Model-View-Controller (MVC)

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    Introduction to Spark 3 with Python

    Master Distributed Data Processing with Spark
    This course provides an in-depth introduction to Apache Spark 3 for distributed computing. Designed for developers, data analysts, and architects, it focuses on leveraging Spark’s powerful engine for big data processing using Python (PySpark). The course covers core Spark concepts, Resilient Distributed Datasets (RDDs), DataFrames, Spark SQL, and Structured Streaming for real-time data processing.

    Through hands-on exercises, participants will learn how to interact with Spark efficiently, optimize queries, and integrate with Kafka for streaming data ingestion.

    Introduction to Spark 3 with Python Objectives

    • Participants will:
    • Understand Apache Spark’s architecture and its advantages over traditional big data frameworks.
    • Work with RDDs transformations and actions for distributed computations.
    • Utilize Spark SQL and the DataFrame API for structured data processing.
    • Leverage Spark’s Catalyst optimizer and Tungsten engine for query performance.
    • Process real-time streaming data with Spark Structured Streaming.
    • Integrate Kafka with Spark Streaming for event-driven data ingestion.
    • Optimize Spark applications using caching shuffling strategies and broadcast variables.
    • Develop standalone Spark applications using PySpark.

    Need Assistance Finding the Right Training Solution

    Our Consultants are here to assist you
    Arrange a consultation

    Key Point of Training Programs

    We have different work process to go step by step for complete our working process in effective way.
    • Introduction to Spark 3 with Python Prerequisites

      Who Should Attend: Developers, data engineers, and analysts working with big data.

      Prerequisites: Basic Python programming knowledge and familiarity with SQL.

    • Introduction to Spark 3 with Python Training Format

      In-Person

      Online

    • Introduction to Spark 3 with Python Outline

      Session 1: Introduction to Spark

      Overview of Apache Spark and its role in distributed computing

      Comparing Spark vs. Hadoop for big data processing

      Spark’s ecosystem and core components

      Setting up Spark and PySpark environment

      Lab: Running Spark in local and cluster mode

      Session 2: Understanding RDDs and Spark Architecture

      RDD concepts, transformations, and lazy evaluation

      Data partitioning, pipelining, and fault tolerance

      Applying map(), filter(), reduce(), and other RDD operations

      Lab: Creating and manipulating RDDs

      Session 3: Working with DataFrames and Spark SQL

      Introduction to Spark SQL and DataFrames

      Creating and querying DataFrames using SQL-based and API-based approaches

      Working with different data formats (JSON, CSV, Parquet, etc.)

      Lab: Querying structured data using Spark SQL

      Session 4: Performance Optimization in Spark

      Understanding shuffling and data locality

      Catalyst query optimizer (explain() and query execution plans)

      Tungsten optimizations (binary format, whole-stage code generation)

      Lab: Optimizing Spark queries for performance

      Session 5: Spark Structured Streaming

      Introduction to stream processing and event-driven architecture

      Working with Structured Streaming API

      Processing real-time data in a continuous query model

      Lab: Building a streaming data pipeline in Spark

      Session 6: Integrating Spark with Kafka

      Overview of Kafka and event-driven data streaming

      Using Spark to consume and process Kafka streams

      Configuring Kafka as a data source and sink

      Lab: Ingesting and processing real-time Kafka data with Spark

      Session 7: Advanced Performance Tuning

      Caching and data persistence strategies

      Reducing shuffling for efficient computation

      Using broadcast variables and accumulators

      Lab: Implementing caching and shuffling optimizations

      Session 8: Building Standalone Spark Applications

      Creating Spark applications using PySpark API

      Configuring SparkSession and application parameters

      Running Spark applications on local and cluster environments

      Lab: Developing a PySpark application and submitting jobs

      By the end of this course, participants will be able to develop scalable data processing applications using Apache Spark 3 and Python (PySpark) effectively.