I’m a data engineer working on (briefly describe your current project or area of focus). I’m evaluating Python and Java for data processing tasks and pipeline development.

Here’s what I’m considering:

Ease of Use: Python is known for its readability and beginner-friendliness. How does Java’s verbosity compare in terms of development speed and code maintainability for data pipelines?
Libraries and Frameworks: Python has a rich ecosystem like Pandas, NumPy, and Spark. Does Java offer comparable data manipulation and big data processing libraries?
Performance: Java is often considered faster due to compilation. However, in data engineering workflows, is Python’s speed a significant drawback, or can it be mitigated with efficient coding practices?
Scalability: How do Python and Java handle large datasets and complex data pipelines as project requirements grow?
Community and Industry Adoption: Both languages have strong communities. Is Python’s popularity in data science a major advantage for collaboration and finding solutions?
I’m particularly interested in insights from experienced data engineers on:

Real-world use cases where Python or Java shines in data engineering.
Best practices for optimizing performance in Python for data processing.
Java libraries and frameworks that effectively address common data engineering challenges.

I analyzed the pros and cons of Python vs Java for data engineering and created a question for Stack Overflow that highlights key points for data engineers to consider when choosing between the two languages.