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Java's Role in Modern Data Engineering

·3 mins
Java DataEngineering SouJava OotBD

What is modern data engineering? And how does it connect with Java?

In the March 25, 2025 episode of the “The Out of the Box Developer”, on the transformation of data engineering and its future with Igor Souza, a Java and data engineering expert.

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The Journey from Batch Processing to Real-Time Streaming #

The data engineering landscape has undergone significant transformation over the past two decades. Igor Souza, a veteran in the field, walked us through this evolution and shared valuable insights on where the industry is headed.

The Hadoop Era

In the early 2010s, Hadoop dominated the big data landscape. “When I started with Hadoop in 2012, it was at the top of every CV,” recalls Igor. However, what was considered “big data” then isn’t necessarily big today, and the technology landscape has evolved significantly.

Key Takeaways from the Hadoop Era:

  • External tables concept revolutionized data access
  • MapReduce was the primary processing paradigm
  • File system distribution was crucial for handling large datasets

The Streaming Revolution

The industry has progressively moved from batch processing to real-time streaming solutions. “People are nowadays pushing for everything to be real-time,” Igor explains. This shift has led to the rise of tools like Apache Kafka and the concept of stream processing.

Modern Streaming Architecture Benefits:

  • Near real-time data processing
  • Reduced latency in data pipelines
  • More efficient resource utilization
  • Better integration with modern cloud infrastructure

The Role of Java in Modern Data Engineering #

Despite Python’s popularity in data science, Java remains crucial in data engineering. Igor emphasizes that many fundamental data tools are Java-based:

  • Apache Kafka
  • Apache Spark
  • Apache Flink

“At Netflix, everything is Java. They use Apache, Kafka, Flink, and various data pipeline tools, all based on Java and JVM.”

AI Integration

Igor identifies several emerging trends:

  1. AI-assisted data operations
  2. Automated data quality management
  3. Stream processing with integrated AI capabilities

Data Mesh and Architecture

The industry is moving toward:

  • Decentralized data architectures
  • Domain-driven design in data platforms
  • Real-time processing as the default approach

Advice for Aspiring Data Engineers #

Igor recommends focusing on three key areas:

  1. Core Fundamentals:

    • Database concepts
    • Data modeling
    • SQL proficiency

  2. Modern Tools:

    • Stream processing frameworks
    • Cloud platforms
    • Container orchestration

  3. Programming Skills:

    • Java/JVM languages
    • Python for data processing
    • Architecture patterns

“Don’t try to learn everything at once. Focus on one area, master it, and then expand your knowledge gradually.”

Essence #

The data engineering field continues to evolve rapidly, with real-time processing and AI integration becoming increasingly important. While tools and technologies change, solid fundamentals and continuous learning remain crucial for success in this dynamic field.

Connect with Igor Souza on social media or visit his blog at igfasouza.com for more insights on data engineering and Java development.

YouTube link to the original session