AWS Lambda Functions - a Quick Start guide

Lambda functions from AWS sit at the heart of its serverless compute services. It lets you run code without you as a developer having to procure, host, maintain and secure servers on the cloud. All you need to worry about is just the code and the business logic. This guide will help you get started creating simple lambda functions.

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A Gentle Introduction to Cloud Computing

Computing mindset for the 2010s

Cloud computing took off in the decade of 2010s. Up until then, when people wanted to run an application, they had to buy computers, databases, switches, network, domains, software, hire IT staff to deploy and maintain anything on the internet. This is similar to learning everything about electricity before you can learn to turn on and off the power switch. Cloud computing changed all of this and allowed developers to build things on the internet without having to worry about hardware and networking.

What is cloud computing?

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Design principles behind RESTful APIs

RESTful stands for “Representational State Transfer”. Consider this as a concept and a pattern of building client-server APIs. I have been building Python APIs that consume some popular RESTful APIs for the past 5+ years. This article outlines the aspects of a thoughtful and well-designed REST API. Some of the aspects here are from the perspective of a consumer, not the maker of RESTful APIs.

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Coding Standards for Jupyter Notebooks

Jupyter Notebook has become incredibly popular among data scientists and general users of Python and R. While the Jupyter framework is liberal and lets you be creative, it would benefit you, your team, and your readers if you define a structure and follow it. Based on my experience as developer evangelist and the author of public-facing notebooks for the last three years, I share in this article the patterns I recommend for writing data science samples using Jupyter Notebook. Read more here

House hunting — the data scientist way

At some point in time, each of us would have went through the process of either renting or buying a house. Whether we realize or not, a lot of factors we consider important are heavily influenced by location. In this article, we apply the data wrangling capabilities of scientific Python ecosystem and geospatial data visualization & analysis capabilities of the ArcGIS platform to build a model that will help shortlist good properties (houses). Read more here