Dask is a parallel computing library in Python that seamlessly integrates with existing Python data science tools like Pandas, NumPy, and Scikit-learn. It provides parallelization capabilities for Pandas-like operations, making it suitable for scalable data manipulation and analysis. Dask enables efficient handling of larger-than-memory datasets through parallel computing, enhancing the scalability of Python workflows.

PySpark is the Python API for Apache Spark, an open-source, distributed computing system for big data processing. PySpark allows Python developers to harness Spark's powerful capabilities for distributed data processing, machine learning, and graph processing. With support for both RDDs (Resilient Distributed Datasets) and DataFrames, PySpark provides a unified API for scalable data analytics and processing.

Vaex is a high-performance Python library designed for out-of-core, lazy DataFrames, particularly suitable for handling large datasets efficiently. It excels in accelerating data operations without loading the entire dataset into memory, making it a valuable tool for big data processing. Vaex integrates Pandas-like syntax for familiar workflows and introduces GPU acceleration for certain operations, further optimizing data manipulations on large datasets.

Dask-SQL is an extension of Dask that brings SQL querying capabilities to big data processing. It allows users to express complex data manipulations using SQL syntax while leveraging the parallel and distributed computing capabilities of Dask. With Dask-SQL, you can seamlessly integrate SQL operations into your Dask workflows, providing a familiar interface for those experienced with SQL querying and analysis. This library enhances the versatility of Dask for big data processing tasks.