Systems | Development | Analytics | API | Testing

Fivetran helps you turn unstructured data into rich context in several ways.

Running Apache Kafka on Amazon Managed Streaming for Apache Kafka (MSK) gives you a managed broker with no ZooKeeper to operate, automated patching, and multi-AZ replication handled by the service. What it doesn't give you is a safe, governed way to expose Kafka access beyond your VPC boundary. That problem looks simple on the surface. It isn't. And how you solve it has significant implications for security posture, operational complexity, and monthly cost.

AI is becoming part of every stage of the testing lifecycle. Teams are using it to analyze requirements, design test cases, generate automation scripts, and accelerate execution activities that previously required significant manual effort. Within Xray, AI already helps transform Jira requirements into actionable test cases with AI Test Case Generation.

Most enterprises are spending on AI faster than they’re becoming ready to run it. Over the next two years, IT leaders plan to grow their AI investment by 70%, according to our 2025 State of Data Infrastructure Global Report. The capability keeps arriving. The readiness to operate it does not keep pace.

Spend enough time in the data management world, and you’ll quickly encounter a flood of terminology: semantic layers, knowledge graphs, unified metadata, governance fabrics, data meshes, and, of course, agentic AI. Most organizations know these aspects matter, yet many still struggle to understand how they fit together. The problem with traditional data architecture is that it is often treated as purely technical.

Last month, a two-line bug fix took down three unrelated features in a colleague’s app. The fix itself was correct — it patched a null check on a checkout API. Nobody re-ran the tests for the inventory service that depended on it, and by Monday, support tickets were stacking up. That gap is exactly what maintenance testing exists to close. Maintenance testing is the QA work you do after software ships — testing every bug fix, upgrade, patch, or migration to confirm nothing else broke.

You have tested hundreds of features. You know the drill. Open the test case, write the preconditions, list the steps, fill in the expected result, run it, compare. Pass or fail. Move on. Then someone hands you an AI feature. A chatbot. A "summarize this ticket" button. A search box that answers in full sentences instead of returning a list of links. You open your test case template, you get to the "expected result" field, and you stop.