History of Cloud Computing

Cloud computing is a buzzing word nowadays. It appeared as early as 1996, with the first known mention in a Compaq internal document (per Wiki). Many companies are joining the cloud computing journey and archived a lot of benefits.

I have been working on various cloud computing projects from start to finish; involved in the full project lifecycle, including design, planning, development, deployment, testing, maintenance, and support. Some clients prefer to use their on-premise data centers, but most of my projects used the cloud – Amazon Web Services, Microsoft Azure and Heroku. So, I wanted to write this post to share my cloud computing journey. My hope is to encourage you to build secure, scalable, highly available and cost-effective cloud applications. Learn to share and share to learn will help us all grow, don’t you agree?

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In the previous article, I have introduced the basic fundamentals of Serverless and its uses cases. This article will help you understand how to build, test, and deploy Serverless functions to the public clouds. Currently, we have a couple of public cloud vendors that provide service to run serverless functions such as Amazon Web Service, Azure, IBM Bluemix. But in this article, we only focus on AWS one of the largest public cloud providers on the market.

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Since November 2014 when AWS introduced AWS Lambda, the term “serverless” is becoming more popular, a lot of people are talking about serverless and how to apply it to your software development.

I’ve been working with AWS for a couple of years and successfully delivered a couple of serverless projects for our customers including backend services, complex web app, microservice, data processing, event streaming. I think serverless will be one of the trends of the software development trends 2019 in relation to microservice and automation.

This article will help you understand some fundamentals of serverless and steps of how to build, test, and deploy your code automatically.

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ETL with AWS Glue

In this article, we will explore the process of extracting data from an AWS RDS database, and then publishing it to S3 with AWS Glue. We will cover the following details:

  • The ability to support data stored in Amazon Aurora and all other Amazon RDS engines, Amazon Redshift, and Amazon S3 (such as XML, CSV format)
  • Big data size
  • Different data schemas
  • A solution to easily switch environments from Development → Test → User Acceptance Testing (UAT) → Staging → Production
  • Autoscale hardware related to data size

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