Pressure to rigorously test software under strict launch times leaves over 54% of software teams struggling to meet demands for new and updated products. But even as the software development lifecycle shifts to incorporate testing, it’s hard to iterate quickly. Developers need a standard two-week cycle to ship code over the wall for QA teams to run with, but how long do QA teams need to release a final report?

Whether you’ve never heard of regex before, or use it in your code all the time, one thing is almost certain…it is not that easy to read! Case in point: /^#?({3})$/ Thankfully the Visual Studio team at Microsoft have included several handy features inside the code editor to help using regex a little less painful.

Businesses work with large amounts of data, which can be difficult to understand. Analyzing that data creates important business intelligence that gives leaders key insights into how their businesses perform. Change data capture (CDC) resolves long-standing problems that stop companies from turning raw data into refined business intelligence. Here is a deeper look at how change data capture works and how businesses use it to manage operations.

This blog post provides an overview of the HBase to CDP Operational Database (COD) migration process. CDP Operational Database enables developers to quickly build future-proof applications that are architected to handle data evolution. It helps developers automate and simplify database management with capabilities like auto-scale and is fully integrated with Cloudera Data Platform (CDP).

In this Blog Post we are going to look at the GraphQL HTTP Request sampler and look at how GraphQL requests can also be made using a HTTP Request sampler in case you are for some reason restricted to an earlier version of JMeter (the sampler was only introduced in JMeter 5.4). We will also look at some of the principles of GraphQL.

Building performance tests that conform to a very specific level of load and concurrency is a standard approach to performance testing. You determine your peak levels of load and concurrency, and you build a test that meets this. You build soak test and scalability tests that conform to pre-determined levels of load and concurrency, and you execute these alongside the other scenarios you build to meet your performance requirements.

However, the flexibility of exploratory testing comes with the risk of ad hoc, random outcomes with no structure or consistency between testers, test managers, and projects. While testers should not be limited in their exploratory testing efforts, having a disciplined approach is a fantastic tool for gathering feedback, detecting errors, and enhancing UX.

I'm excited to announce the Ably Control API GitHub Action! With this Action you can create Ably apps and API keys straight from your GitHub workflow, ensuring a reliable and repeatable process of creating your cloud infrastructure.

Most web applications use a data store of some kind, often a relational database. When a web app becomes successful, it can become all too easy to start "hoarding" data in the database. But hoarding data leads to the unbounded growth of database tables (both row count and stored data size). While this works fine to a certain point, it is very useful to prevent some data bloat — or, if you cannot prevent it, to plan for your infrastructure ahead of time to adequately manage growth.