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The enterprise workhorse that refuses to quit. For data engineering, Java is a powerhouse for building robust, scalable systems like custom ETL pipelines, distributed frameworks (hello, Hadoop), and real-time processing engines like Kafka Streams. Its JVM ecosystem and libraries (e.g., Apache Spark, Flink) make it a cornerstone for high-performance data applications where reliability matters.
That said, Java’s verbosity can feel like writing a novel for every task, and its boilerplate-heavy style makes simple things unnecessarily painful. While its performance is solid and memory management reliable, it lacks the modern elegance of languages like Go or Python. Plus, debugging messy stack traces from poorly tuned JVMs is no picnic.
Java thrives in large-scale, structured environments where maintainability and backward compatibility are king. It’s not the coolest language at the party, but when your data stack needs to scale like a Fortune 500 company, Java delivers—with plenty of paperwork.
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Jobs in using Java for but please no
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data scientist
Full Stack Developer @ thetaray
ES | 2025-12-25
ThetaRay seeks a mid-senior Full Stack Developer skilled in Angular, Java, and microservices, with a notable emphasis on integrating AI tools like Copilot and Claude to boost productivity. The...
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AI/ML, Big Data, Analytics, Cloud Computing, React, SaaS, API, Java, Agile/Scrum, Management, SQL, NoSQL, Kafka, Linux, CI/CD, Git, Jenkins, Docker, Computer Science, DevOps | ||
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data scientist
Data Technical Expert (IA) - Híbrido Málaga @ ustglobal
ES | 2025-12-23
The role involves providing technical leadership in data and AI, designing and deploying complex data platforms, pipelines, and models, with a notable emphasis on architecture and best practices....
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AI/ML, Cloud Computing, Big Data, Data Streaming, MLOps, Python, SQL, Scala, Java, R, AWS, Azure, GCP, Spark, Databricks, Hadoop, Kafka, Airflow, Trino, NoSQL, Docker, Kubernetes, CI/CD, IaC |
