Systems | Development | Analytics | API | Testing

Key Takeaways Enterprise data teams spend millions on warehouse infrastructure while still designing schemas the way they did in 1995—one entity at a time, one relationship at a time, hoping the model survives its first encounter with production data. The irony runs deep: organizations racing to deploy real-time analytics are bottlenecked by modeling processes that take six to eight weeks before a single pipeline runs. Data warehouses succeed or fail on design.

https://www.trustwave.com/hubfs/Blogs/Trustwave_Blog/Headers/Cybersecurity_Awareness_Month.png.

Enterprise data storage comprises a patchwork of systems: ERP databases, CRM platforms, spreadsheets, cloud apps, and legacy files. These systems do their own jobs well individually, but collectively they create a fragmented landscape. For anyone tasked with building a migration, an integration, or even a simple report, the first challenge is not moving data. It’s understanding what exists and how it all connects.

In our previous blog post, we introduced two ways that Confluent Cloud can integrate with AWS Lambda. One option is using Lambda’s Event Source Mapping (ESM) for Apache Kafka, wherein Lambda creates a consumer group, consumes records off the provided topic, and triggers the Lambda function. The record is polled by the ESM, and the consumed record subsequently acts as the event data provided to (and processed by) the Lambda function.

Traditional test metrics like automation %, pass/fail rates, and defect counts don’t reflect the impact of introducing agents into the QA process. This blog explores a new class of KPIs designed to measure how well your virtual test team is performing including Agent Assist Rate, Human Override Rate, Scenario Coverage Delta, and Review Time Saved.

As organizations explore more advanced uses of agentic testing, a compelling vision emerges: a modular virtual test team composed of AI agents, each playing a focused role like Test Architect, Test Designer, Executor, and Summary Agent. While still early in real-world adoption, this model offers a way to coordinate intelligence at scale, with humans guiding the system and autonomy granted based on task risk and maturity.

‍ In today’s connected world, every enterprise depends on networks- from internal servers and employee devices to cloud platforms and hybrid systems. However, as these networks grow in size and complexity, they become prime targets for cybercriminals. By 2025, cybercrime will cost businesses $10.5 trillion annually, making it one of the most significant economic risks worldwide.

Enterprise data warehouses face a fundamental challenge. For decades, organizations treated them as static projects—build once, maintain constantly, rebuild when requirements change. As data volumes surge and business needs accelerate, this approach creates bottlenecks. Organizations need autonomous data warehouses: self-sustaining ecosystems that adapt and evolve with minimal manual intervention.

Modernizing AS/400 systems and legacy ERP platforms is no longer optional - it’s essential for businesses to stay competitive. These systems, while reliable, struggle to meet modern demands like real-time data access, cloud integration, and mobile compatibility. The solution? REST API generators. REST API generators allow businesses to connect outdated systems with modern applications without overhauling the entire infrastructure.

We’re excited to announce that NodeSource has joined the OpenJS Foundation’s Ecosystem Sustainability Program (ESP), a strategic partnership designed to sustain the health and reliability of the JavaScript ecosystem. Through the ESP, NodeSource will help provide security support for organizations running older, unsupported versions of Node.js, giving teams more time and flexibility to transition to newer releases while maintaining a secure posture.