Mon.Nov 12, 2018

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Expanding the Cloud – The Second AWS GovCloud (US) Region, AWS GovCloud (US-East)

All Things Distributed

Today, I'm happy to announce that the AWS GovCloud (US-East) Region, our 19th global infrastructure Region, is now available for use by customers in the US. With this launch, AWS now provides 57 Availability Zones, with another 12 zones and four Regions in Bahrain, Cape Town, Hong Kong SAR, and Stockholm expected to come online by 2020. The AWS GovCloud (US-East) Region is our second AWS GovCloud (US) Region, joining AWS GovCloud (US-West) to further help US government agencies, the contract

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Data Migration Testing Tutorial: A Complete Guide

Software Testing Help

Overview of Data Migration Testing: It is quite often heard that an application is moved to a different server, the technology is changed, it is updated to the next version. Read more Data Migration Testing Tutorial: A Complete Guide. The post Data Migration Testing Tutorial: A Complete Guide appeared first on Software Testing Help.

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Building Amazon Alexa Skills With Node.js, Revisited

The Polyglot Developer

A little more than two years ago, when the Amazon Echo first started picking up steam and when I was first exposed to virtual assistants, I had written a tutorial around creating a Skill for Amazon Alexa using Node.js and simple JavaScript. In this tutorial titled, Create an Amazon Alexa Skill Using Node.js and AWS Lambda , we saw how to create intent functions and sample utterances in preparation for deployment on AWS Lambda.

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Working with fio “distribution /pereto” parameter

n0derunner

The fio Pareto parameter allows us to create a workload, which references a very large dataset, but specify a hotspot for the access pattern. Here’s an example using the same setup as the ILM experiment, but using a Pareto value of 0:8. My fio file looks like this. [global]. ioengine=libaio. direct=1. time_based. norandommap. random_distribution=pareto:0.8.

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