Python is one of the most popular programming languages among data professionals. Python data science libraries such as Numpy, Pandas, Scipy, and Scikit-learn can sequentially perform data science tasks. However, with large datasets, these libraries will become very slow due to not being scalable beyond a single machine. This is where Dask comes into the picture. Dask helps data professionals handle datasets that are larger than the RAM size on a single machine. Dask utilizes the multiple cores of a processor or uses it as a distributed computed environment. Dask has the following qualities:
- It is familiar with existing Python libraries
- It offers flexible task scheduling
- It offers a single and distributed environment for parallel computation
- It performs fast operations with lower latency and overhead
- It can scale up and scale down
Dask offers similar concepts to pandas, NumPy, and Scikit-learn, which makes it easier to learn. It is an open source parallel...