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
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