Java DataFrames: The Missing Tool in Your Data-Oriented Toolkit скачать в хорошем качестве

Java DataFrames: The Missing Tool in Your Data-Oriented Toolkit

In this #InfoQ video, Vladimir Zakharov demonstrates why Data-Oriented Programming (DOP) and Java DataFrames are the secret to high-performance, maintainable data processing. Using the "One Billion Row Challenge" as a benchmark, Vladimir compares DataFrame-EC, Tablesaw, and Kotlin DataFrame against Python/Pandas. Discover how to achieve Python-like expressiveness with Java-level performance - all while keeping your architecture clean and externalizable. ⏱️ Video Timestamps (For Navigation) 0:00 — Meet Vladimir Zakharov (Java Expert & JSR 335 Group) 1:15 — What is Data-Oriented Programming (DOP)? 3:42 — The Cost of Records and Streams in Java 5:20 — Why Use DataFrames in a Java Ecosystem? 7:45 — The One Billion Row Challenge: Results & Analysis 10:30 — Code Comparison: Pandas vs. DataFrame-EC vs. Tablesaw 14:15 — Kotlin DataFrames: Pros and Cons 17:40 — Under the Hood: Primitive Collections & Object Pooling 21:10 — Advanced Use Cases: Joins, Pivots, and External DSLs 26:30 — Final Takeaways: When to Choose DataFrames over Databases 🔗 Transcript available on InfoQ: https://bit.ly/4rwe3hg #Java #SoftwareArchitecture #DataEngineering #JVM