How to Fix Big Data Performance Issues

You know what really triggers me?

❌ Not politics.
❌ Not even slow Wi-Fi.

Its performance bottlenecks caused by small files.

And you know what's the best part?
✅ You can fix it.

Big data engines like Hadoop, Spark, and Trino are built for parallel processing.
They shine when they're chewing through large datasets.
But when you hit them with millions of tiny files, each one demands task scheduling, metadata operations, and resource allocation.
And that overhead adds up fast.

That’s why I always recommend working with larger files, typically between 128MB and 512MB.
By aggregating small files into larger ones (a process called compaction), you reduce overhead and unlock serious performance gains.

So here’s the takeaway:
In Big Data, size matters!

And we at DataFlint are committed to pushing the boundaries of big data performance.

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Видео How to Fix Big Data Performance Issues канала DataFlint