Systems | Development | Analytics | API | Testing

In our rapidly-changing digital world, companies are always searching for tools to enable automation for testing. Due to the increase in popularity of test automation, it is important to understand the pricing for testing tools. Whether you’re selecting a platform to help you simplify your QA process or determining how much to invest in test automation, pricing is an important factor.

This is the eighth post in a series about reasons to attend API Summit 2025. Check out the previous post here. The first time you push an API to production, you’re excited it’s finally live. You’re relieved that you can move on to your next big project. You’re also nervous. You’ve tested as much as you thought you should, but somewhere in the back of your mind is a whisper of, “What if I missed something? What if the commits are messy?

Agentic testing isn’t just about faster execution - it’s about uncovering what’s missing from your test suite. This blog explores a forward-looking model where AI agents help identify gaps in traditional, deterministic systems through techniques like scenario delta detection, data condition surfacing, edge path exploration, and test case obsolescence tracking.

In the Atlassian ecosystem, every team has its own way of approaching testing: some are just starting out and need the basics; others are scaling automation, managing complex pipelines, or working under strict compliance standards. Until now, Xray offered a core application with the base set of features, and users could add Xray Enterprise on top of that to gain access to additional functionalities.

Enterprise data warehouses have reached an inflection point. For decades, organizations treated them as static projects—build once, maintain constantly, rebuild when requirements change. But as data volumes surge and business needs accelerate, this approach no longer scales. The modern enterprise needs something fundamentally different — a modern data warehouse that behaves like an autonomous ecosystem and sustains itself.

Embedded business intelligence (BI) isn't just about adding charts to your app; it's about making your product experience — and your users — more valuable and competitive. If you’d like to try an embedded BI solution for your SaaS platform, get a free trial. Blog Contents show No more separating analytics from your SaaS Embedded BI: A quick recap Most common embedded BI use cases 1. Empowering customers with self-service analytics 2. Unlocking embedded BI for SMBs 3.

It’s 3 a.m., and an alert fires: Your critical Kafka Streams application is lagging. The frantic troubleshooting begins. Is it a consumer group rebalance? You start searching through application logs across multiple pods. Is it a problem with the Apache Kafka cluster itself? You switch to your cluster monitoring dashboards to check broker health. Or is there a silent bottleneck hidden deep in your application code? Without the right instrumentation, you're flying blind.

In engineering, we talk a lot about technical debt — the shortcuts and compromises made in code that pile up over time. But there’s another kind of debt that’s just as dangerous and far more invisible: QA debt. QA debt is what happens when testing isn’t given the same attention as features, architecture, or performance. It’s the accumulation of missed edge cases, outdated test suites, incomplete automation, or skipped regression checks.

Every boardroom in any company that is transforming its systems should be asking the same question: How long before our next ERP release? When that timeline stretches because of QA delays, the consequences are not just technical – they are strategic. Release schedules slip, budgets escalate, and stakeholders begin to lose faith. Quality assurance is not merely a phase. It can be the difference between being a market leader and lagging.