For our latest specialist interview in our series speaking to technology leaders from around the world, we’ve welcomed Bill Kunneke, Chief Technology Officer at Leasecake. Bill has over twenty years of experience as an IT professional and a proven technical leader who delivers large and often transformative IT projects while communicating complex technical solutions to key stakeholders and executing strategic IT functions.

So you want to move data to Amazon Redshift? You're not the only one. Every day, thousands of organizations worldwide migrate data to Redshift, which Amazon claims is the world's fastest cloud data warehouse. These organizations have one mission: To move data to Redshift so they can generate business intelligence (BI) from the latest metrics. Amazon's data warehouse makes it easy to produce metrics that power data-driven organizations like yours.

As the enterprise IT landscape becomes larger and more complex, many organizations are finding it difficult to manage all of their systems and services. According to a study by the access management company Okta, businesses use an average of 73 different enterprise software applications — and 1 in 10 businesses use more than 200 apps. On-premise business APIs can help connect these applications, but they come with several noteworthy drawbacks.

Advances in the performance and capability of Artificial Intelligence (AI) algorithms has led to a significant increase in adoption in recent years. In a February 2021 report by IDC, they estimate that world-wide revenues from AI will grow by 16.4% in 2021 to USD $327 billion. Furthermore, AI adoption is becoming increasingly widespread and not just concentrated within a small number of organisations.

In my main position, as a data scientist at SIL International, I work on expanding language possibilities with AI. Practically this includes applying recent advances in Natural Language Processing (NLP) to low resource and multilingual contexts. We work on things like spoken language identification, multilingual dialogue systems, machine translation, and translation quality estimation.

This post does not look at a particular aspect of JMeter nor does it give a detailed overview of how to use a particular tool that will compliment your performance testing with JMeter. What it is about is the principles of push to production pipelines and performance testing and while I have stated that this post is not specifically about JMeter in my experience JMeter is one of the best performance testing tools for this type of pipeline integration.

In this post we are going to look at WebSockets, specifically how JMeter can be used to test them. Web Sockets are not supported natively by JMeter but there are a couple of Plugins that you can use that work very nicely. One of them is called JMeter WebSocket Sampler by Maciej Zaleski and information on the library can be found here. The second and the one we will use for our post is also called JMeter WebSocket Sampler and is by Peter Doornbosch, more information on this Plugin can be found here.

There are many samplers that JMeter provides but I am willing to bet that the HTTP Request samplers is the most frequently used and, in this Blog Post, we are going to look at how this works and how it can be configured.

We’ve raised a $60m Series C funding round to help us deliver on that goal. Bitrise is already core to the mobile engineering processes of many of the world’s most successful businesses, but — with this funding — we’ll build out our team and technology to make you even faster, more efficient, and more successful on mobile.

Performance is one of the most important aspects of web application development. A fast application will make its users, developers, and business stakeholders happy, while a slow one is sure to frustrate all three parties. In this article, we will consider some practices that you should adopt to scale your Node.js servers. Your servers will then be able to handle high traffic workloads without a degraded user experience.