Parallel computing is an architecture in which several processors execute or process an application or computation simultaneously. Parallel computing helps in performing extensive calculations by dividing the workload between more than one processor, all of which work through the calculation at the same time. The primary goal of parallel computing is to increase available computation power for faster application processing and problem solving. In sequential computing, all the instructions run one after another without overlapping, whereas in parallel computing instructions run in parallel to complete the given task faster. Dask is a free and open-source library used to achieve parallel computing in Python. It works well with all the popular Python libraries like Pandas, Numpy, scikit-learns, etc.

As businesses around the world are embarking on building innovative solutions, we're seeing a growing trend adopting data science workloads across various industries. Recently, we've seen a greater push towards reducing the friction between data engineers and data scientists. Data scientists are now enabled to run their experiments on their local machine and port to it powerful clusters that can scale without rewriting the code. You have many options for running data science workloads, such as running it on your own managed Spark cluster. Alternatively there are cloud options such as Amazon SageMaker, Amazon EMR and Amazon Elastic Kubernetes Service (Amazon EKS) clusters.

Every data scientist knows that data pre-processing and feature engineering is paramount for a successful data science project. Often, however, these steps are time-consuming and involve you waiting for computations to finish, keeping you from creating that awesome model. In this post we will look at a few tricks that intend to speed up your pandas data-crunching workflows by enabling Pandas to use your machine in an optimal way. Pandas is a powerful, versatile and easy-to-use Python library for manipulating data structures. For many data scientists like me, it has become the go-to tool when it comes to exploring and pre-processing data, as well as for engineering the best predictive features.

We will now have a look at some simple cases for creating arrays using Dask. As you can see here, I had 11 values in the array and I used the chunk size as 5. This distributed my array into three chunks, where the first and second blocks have 5 values each and the third one has 1 value. Dask arrays support most of the numpy functions. For instance, you can use .sum()