Systems | Development | Analytics | API | Testing

Testing is the bottleneck of devops. 80% of testing is done manually. This is time-consuming, error-prone, tedious, and expensive. It's a hundred times more expensive to fix an error caught in production versus an error caught. Dev teams are being pushed to release code faster, release new features faster, and beat their competitors in every possible way. Automation is a cheaper and more accurate way to test. It lets everyone focus on most important goals.

The world is facing the highest inflation rate in ages, and Codemagic is reacting to it by… lowering its prices. These are tough times for everybody, and because we’ve benefited from some recent changes, we wanted to pass on these benefits to all developers so that you have one less thing to worry about.

In this post we are going to look at performance testing on large scale programmes. A few the posts we write define techniques and approaches based on a single application under test but sometimes you are faced with the prospect of performance testing: Now, especially in the case of migration of services, performance is key, and you cannot afford to see a degradation in performance as the business users will have already become accustomed to the software and how it performs.

Our six new models are aggregated on the most common granularities for ad-related metrics, so marketing analysts can hit the ground running.

Apache HBase has long been the database of choice for business-critical applications across industries. This is primarily because HBase provides unmatched scale, performance, and fault-tolerance that few other databases can come close to. Think petabytes of data spread across trillions of rows, ready for consumption in real-time.