Dask
What it is: Parallel computing library that scales Python to clusters. Provides pandas-like DataFrames that work on datasets larger than memory. Integrates with PyData ecosystem.
What It Does Best
Out-of-core processing. Handle datasets bigger than RAM. Processes data in chunks. 100GB CSV on 16GB laptop? Dask handles it.
Familiar APIs. dask.dataframe mimics pandas. dask.array mimics NumPy. dask-ml mimics scikit-learn. Familiar syntax, distributed execution.
Flexible scaling. Single machine to multi-node cluster. Same code runs on laptop and 100-node cluster. Dynamic task scheduling with live dashboard.
Pricing
Free. Open source, BSD license.
When to Use It
✅ Data doesn't fit in memory
✅ Need to scale pandas/NumPy/scikit-learn
✅ Already using Python data stack
✅ Want to avoid JVM (Spark alternative)
When NOT to Use It
❌ Data fits comfortably in memory (use pandas/Polars)
❌ Need SQL interface (DuckDB better)
❌ Team already on Spark (switching cost high)
❌ Single-threaded operations (no parallelism to exploit)
Bottom line: Python's answer to Spark. Less mature but more Pythonic. Perfect for scaling pandas beyond single machine. If your data outgrows memory, Dask is the natural next step. Great dashboard for debugging.