Systems | Development | Analytics | API | Testing

There's a conversation happening in a lot of data teams right now. It goes something like this: the lakehouse is clearly where things are heading, Apache Iceberg is becoming the format everyone wants to land in, and the AI use cases the business is asking for need clean, open, queryable data.

Enterprise software organizations allocate most of their total IT budgets to software validation and quality maintenance. According to the Consortium for Information and Software Quality (CISQ), poor software quality drains over $2.41 trillion annually from the US economy in operational failures, technical debt, and unmitigated production incidents.

The device in your pocket easily runs most practical analytics workloads.

If you want to avoid API rate limiting headaches during load testing, start by proactively monitoring usage patterns. Use real-time metrics to catch spikes before they trigger throttling. Tools like LoadFocus make it easier to visualize these patterns and pinpoint problematic clients or endpoints. Review how you structure requests – batching similar calls together and caching predictable responses can cut down on redundant traffic and ease pressure on the API.

As part of my work, I regularly consult with teams comparing Unreal Engine version control performance and weighing whether Perforce P4 is the right fit for their projects. Based on these conversations, I decided to develop a repeatable framework that others can use to objectively benchmark P4 against other version control systems.

Bots make up more than a third of internet traffic, and API endpoints attract more of it than web pages do. Scanners hunting for.env files, credential-stuffing scripts, LLM training crawlers, and the long tail of low-effort probes all hit your gateway before they ever reach a service. Every one of those requests costs you — in upstream load, in noise across your analytics, and in the bill.

Performance analysis only happens when someone has the time to do it. Every team has performance reviews that need to happen on a regular cadence, whether that is reviewing channel performance each week or reporting to clients every month. The focus may change, but the work behind them is often very similar. Someone needs to understand what changed, why it changed, and whether anything needs attention. Then they need to turn those findings into an update and get it to the people who need it.

“Customers can apply a discount code” looks ready for testing. Give that sentence to an AI model and it can produce cases for valid, invalid, expired, and reused codes in seconds. The trouble starts when someone asks what a valid code should do. Which basket is eligible? Does the discount change the displayed total before or after tax? Should the saved order carry the same amount?

In one release test, my AI agent triaged 43 backlog items into 134 executable cases in Katalon True Platform. The first pass at the steps caused trouble. We had bundled setup, navigation, action, and judgment into long instructions. The runner returned verdicts, but a failed step left me unsure which expectation it had checked. We split the cases into one action and one checkable expected result per step. The failures became specific enough to review against the screenshots and session recording.

WebSockets and Server-Sent Events (SSE) solve the same immediate problem: getting data from a server to a browser without the client asking for it first. Most comparisons stop at how their connection types and browser support compare, to help you decide which one to use. This one covers that ground too, but goes further, into what neither protocol solves for you and whether to build that yourself or hand it to a managed platform.