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Devops and the need for cloud based solutions

DevOps and cloud-based computing have existed in our life for some time now. These both can be regarded as the latest techs in the arsenal of information technology. Thinking back on how SDLC started and what it is today, the only reasons for its success can be accounted to efficiency, speed and most importantly automation – DevOps and cloud-based solutions can be considered major contributors here (after all DevOps is 41% less time-consuming than traditional ops).

Moving an Application from VM to Kubernetes

Containerization and orchestration are becoming increasingly popular. According to a recent survey conducted by Market Watch, the global container market will exceed $5 billion by 2026. In 2019, that number was under 1 billion. These statistics show that the world is moving more towards containers and orchestration faster and faster each day. One example of this is moving from VM to Kubernetes.

The Node.js Application Maintainer Guide

TLWR: maintaining a Node.js code base is essential to ensure its success in the long term. There are three main categories to maintain an application: dependencies maintenance, Node.js versions maintenance, and Main dependencies and architecture maintenance. There is excellent content for starting the journey as a Node.js developer; you could find a handful of articles, courses, tutorials, and videos to help developers kickstart a project and guide them through the developing process.

Partnering with Appian to Put Patients First: Ignyte's Mental Health Case Management Solution

A year of virtual meetings, homeschooling, and social distancing has taken a toll on all of us. According to a 2021 article from the Kaiser Family Foundation, around 40% of Americans have reported symptoms of depression and anxiety during the COVID-19 pandemic. Mental health providers worldwide are struggling to meet increased patient demand while navigating increased telehealth needs.

Automated Anomaly Detection: The next step for CSPs

Today’s telecom engineers are expected to handle, manage, optimize, monitor and troubleshoot multi-technology and multi-vendor networks, in a competitive and unforgiving market with minimal time to resolution and high costs for errors. With the ongoing growth in operational complexities, effectively managing radio networks, current and legacy core networks, services, and transport and IT operations is becoming a radical challenge.

Testlio Outperforms on G2 in '21 Report

Testlio named a high performer in the G2 Spring ‘21 Software Testing Grid ® Report May 19, 2021. Austin, TX and Tallinn, Estonia. Testlio has been named a High Performer in G2’s 2021 Grid ® Report for Software Testing. G2 ranks the world’s best software companies, services, and tools based on how verified customers measure performance. G2 reviewed data from 1000s of user reviews to create recommendation grids.

3 lessons learned from app testing for Fortune 500 companies

Software testing for Fortune 500 companies and large enterprises has many similarities to testing for mid-market companies – primarily that it’s focused on providing a quality experience to the end-user. Most of the world’s largest companies are broken down into small agile teams, so working with massive enterprises can often feel like working with a startup team. However, there are some key differences.

Achieving a Cloud-First Strategy with APIs

As many organizations push forward in adopting cloud-first strategies, issues often arise with addressing how their suite of applications and systems can remain integrated securely and efficiently. Whilst the movement to the cloud was a huge step in technological advancement, advancements in API technology now make “hybrid cloud” models possible, and businesses exist both in the cloud and on premises.

NVIDIA RAPIDS in Cloudera Machine Learning

In the previous blog post in this series, we walked through the steps for leveraging Deep Learning in your Cloudera Machine Learning (CML) projects. This year, we expanded our partnership with NVIDIA, enabling your data teams to dramatically speed up compute processes for data engineering and data science workloads with no code changes using RAPIDS AI.