Systems | Development | Analytics | API | Testing

Establishing a data warehousing system that meets all your business intelligence targets is by no means an easy task. It traditionally involves profiling source systems, designing a dimensional model by hand, writing the DDL to deploy it, building the load pipelines, and scheduling them to run, work that can take weeks. Astera's manual, step-by-step approach to this is covered in Building a Data Warehouse – A Step by Step Approach.

You Can't Govern What You Can't See A decade ago, cloud and API sprawl got ahead of governance, and enterprises spent years trying to account for costs they'd never tracked. Today, we're seeing the same pattern around AI, with hundreds of customers proxying traffic via Kong AI Gateway, which includes LLM, MCP, and agent connectivity. *AI spending will reach $2.59 trillion in 2026.* I regularly like to share what we're seeing in production at Kong.

You've probably seen the demos. An AI model kicks off a task, hands pieces of it to other AI models, and somehow the whole thing gets done. Emails drafted, code reviewed, reports summarized — all without a human in the loop. While a single agent doing one thing is impressive, the true paradigm shift occurs when transitioning from single-agent to multi-agent AI systems. It looks like magic. It isn't.

Software workloads that process large volumes of real-time data are becoming common. Decades working in this domain has taught me that building and operating reliable and maintainable real-time data products requires permissive access to the context of the environment. This article explains how to approach agentic engineering and apply it when building real-time data products inside an agentic software factory.

The first payment is often the easiest part of the subscription lifecycle. A customer chooses a plan, enters a payment method, completes the transaction, and gains access. For the product team, that successful sign-up can feel like proof that the payment experience works.

Ask a PropTech team how many systems their platform connects to and you’ll usually get a number they’re a little proud of. A typical platform ties into an MLS feed, a couple of CRMs, a payments provider, an e-signature vendor, and a scatter of data sources, yet it still converts and retains about the way it did a year ago. So the question worth asking about real estate platform integrations isn’t how many you can support.

Many teams assume that if an API passes functional tests, it’s ready for production. Functional testing, however, only verifies that endpoints return correct results for individual requests. It does not reveal how the API behaves when subjected to heavy traffic or sustained load. Under real-world conditions – such as a surge of users logging in simultaneously – APIs that pass functional checks may still experience latency, errors, or outages.

An outdated Selenium or Appium build can look exactly like a flaky test … until you check the version number.

The pull request looked perfect. Two approvals, clean diff, all checks green. The team shipped it Thursday afternoon. By Friday morning, support tickets were coming in. On a popular mid-range Android device, the new checkout button rendered behind a promotional banner: visually present but physically untappable. Every reviewer read that code, yet none of them could have caught it, because the defect was never visible in the diff. It only ever appeared on the device.

Node.js 24 is the latest Long-Term Support release, and Node.js 26 is the Current release line. Node.js 18 and Node.js 20, meanwhile, have both reached End-of-Life (EOL). That should make the direction of travel obvious. Instead, Matteo Collina’s analysis of Node.js download data showed that even discontinued Node.js 12, 14, and 16 release lines were still generating tens of millions of downloads per month, highlighting how slowly the ecosystem moves away from unsupported versions.