Systems | Development | Analytics | API | Testing

Teams using TestComplete face a common problem: one small test change can produce a wide set of modified files, and not all of them deserve the same level of scrutiny. The fix is not to review everything equally – it is to classify TestComplete artifacts by risk, then standardize how your team reviews, stages, and merges them. This article outlines this process and offers best practices for using Git effectively with TestComplete projects.

The definition of sustainability is being re-written in the age of AI. Yes, the current discourse that focuses on green IT considerations, including resource efficiency, carbon accounting and water use, is necessary. But it is incomplete. Sustainability in the age of AI implies sustaining the long-term flourishing of people, businesses, societies, and planetary systems together, not just minimizing energy use or carbon.

Many e-commerce teams leave load testing feeling reassured, only to watch their sites falter when real customers arrive. This gap stems from traditional testing methods that generate misleading results, often concealing the actual risks beneath the surface.

If you’re building a SaaS platform or data product, it’s important to consider what BI tools your customers are already using. They want to connect Tableau, Power BI, Logi Symphony, or their own analytics stack directly to your data. They want SQL access, and to query your platform the way they query everything else. But expectations don’t quite meet reality once as tickets start flooding in.

Key takeaways: For AI workloads, pooling must handle long connection hold times and heavy traffic. DreamFactory is a secure, self-hosted enterprise data access platform that provides governed API access to any data source, connecting enterprise applications and on-prem LLMs with role-based access and identity passthrough. Combined with tools like PgBouncer, these solutions free connections faster and improve scalability. Simple tweaks, such as segmenting pools and setting timeouts, can boost efficiency.

Take this scenario: Your team ships a release with 91% code coverage. Every test in the suite passes. The pipeline is green, and leadership signs off. But two days later, a critical defect surfaces in production. Upon investigation, you find that the changed code was never actually tested, and the tests that were run covered different paths entirely. That 91% was real, but it was just measuring the wrong thing. And as AI tools generate more of the code inside those pipelines, the gap widens.

Leadership keeps asking for more dashboards, faster answers, and tighter compliance. The data team hears a different message: do more with the same staff (or, fewer). That is where the difficulty evaluating on-prem and private cloud deployment models for corporate data analytics and visualization solutions starts to bite.

BigQuery unveils new capabilities for lakehouse, knowledge graph, built-in AI, and agentic-first experiences.

Unaccounted data copies within non-production environments can make enterprises vulnerable to cyber theft. Non-production environments — which are often less secure than production environments — are treasure troves for hackers seeking to steal customer data. How many copies of test data are currently floating around your organization’s non-production environments?