Stick around if you’re for a complete guide to set up a PySpark environment for data science applications; PySpark functionality, and the best platforms to be explored.

What is Pyspark?

Pyspark, a robust language that must be considered to learn if you’re into the idea of creating more scalable pipelines and analyses. According to Chris Min, a data engineer, Pyspark basically enables writing Spark apps in Python and makes data processing efficient in a distributed fashion. Python is not just a great language, but an all-in-one ecosystem to perform exploratory data analysis, create ETLs for data platforms, and build ML pipelines. …

NFTs are digital directories that are powered by a blockchain system which is the same infrastructure that characterizes common cryptocurrency. However, an NFT is a unique kind of cryptocurrency, and the blockchain database on which it is stored authenticates whoever the legitimate holder of that cryptocurrency.

The NFTs are considered an element of the Ethereum blockchain that is like any other cryptocurrency. Before we understand what NFT is, let’s look at the underlying technology behind NFTs which is Ethereum.

What is Ethereum blockchain?

After Bitcoin, Ethereum is the second-largest blockchain by trading volume. However, it wasn’t even designed to serve only as an electronic…

Many data scientists develop, train, and deploy ML models within a hosted environment. Regrettably for them, they do not have the convenience and facility for scaling up or scaling down resources as and when required based on their models.

This is where AWS SageMaker comes into picture! It solves the issue by facilitating developers to build and train models in order to get faster production with bare minimum efforts at an economical cost.

But first…what is AWS you ask?

For enterprises in the big data domain, it is imperative to have data warehouses that are agile, scalable, and at the same time cost-effective. Given how modern businesses are increasingly looking at big data as a solution to enhance in all areas; from customer support to production pace, analytical data warehouses have become critical to most business needs.

While the world of data analytics is still blooming, the large fishes have successfully established their hold in the market with their own data warehouses. Industry giants Amazon and Google — companies at the core of the big data boom, offer their…

A user-friendly tool that helps deploy any machine learning model and any Python project with ease by turning data scripts into shareable apps in minutes? Yep, it is true. And it’s here!

Video on Getting Started with Streamlit

Decoding Streamlit

Created by Adrien Treuille, Thiago Teixeira, and Amanda Kelly, Streamlit is an open-source Python library that enables you to effortlessly build beautiful, custom web apps for machine learning and data science without worrying about the front end, for free. Astutely developed keeping the data scientists and ML engineers in mind, this tool allows them to…

Choosing the right GCP Database depends on a lot of factors including your workload and the architecture involved. Today, I’m going to provide you all with an overview of popular Google cloud database services, including key considerations when assessing and choosing a service.

Know Thy Database

Google Cloud Platform (GCP) was built to provide an array of computing resources, database services being one of them. Competent and capable of handling modern data, bound with efficiency, flexibility, and great performance, GCP is a hosted platform solution for disseminated data across geography.

When choosing a Google database service, one should consider a lot of things…

Breaking (Data)brick By Brick!

Founded in 2013 by the real OGs… the creators of Apache Spark, Delta Lake, and MLflow, Databricks is a single platform for all your data needs. It is a software (Data + AI) company that offers a Unified Data Analytics Platform (UDAP) and is basically built on a modern Lakehouse architecture in the cloud.

At present, Databricks is one of the fastest-growing data services on AWS and Azure with its headquarters in San Francisco and offices around the world serving over 5000 customers and over 450 partners worldwide. …

For data scientists, big data is an ever-increasing pool of information and to comfortably handle the input and processing, robust systems are always a work-in-progress. To deal with the large inflow of data, we either have to resort to buying faster servers that adds to the costs or work smarter and build custom libraries like Dask for parallel computing.

Before I go over Dask as a solution for parallel computing, let us first understand what this type of computing means in the big data world. By the very definition, parallel computing is a type of computation where many calculations or…