Systems | Development | Analytics | API | Testing

Enterprise product teams are squarely in the throes of a massive shift. New Gartner research crystalized that companies using AI tools for product management are reaping a significant 35% productivity gain and achieving a two to three-year time-to-market advantage over those who wait.

Bringing APIs and events together has always been a challenge. REST APIs give developers a familiar interface, while event brokers like Solace Broker excel at fan-out, filtering, and scalable, reliable event delivery. The tricky part? Bridging these two worlds without building a lot of custom glue. That’s exactly what the new Kong plugin for Solace upstream mediation does.

As organizations scale their AI initiatives from experimentation into production, CTOs face a pivotal architectural challenge as storage emerges as one of the most common—and most expensive—constraints. While organizations continue to invest aggressively in GPU compute, studies consistently show that infrastructure inefficiencies outside the GPU account for the majority of wasted AI spend.

Most enterprises have made the commitment to AI. They’ve approved the budgets, stood up the pilots, and named it a strategic priority. So why are 95% of them getting zero return on $30–40 billion in GenAI investment? According to MIT research cited in Hitachi Vantara’s 2025 State of Data Infrastructure Global Report — which surveyed more than 1,200 IT leaders across 15 markets — the failure isn’t the model. It’s the infrastructure underneath it.

As organizations embed Quality Assurance into their SDLC with Jira and Xray, the resulting test data becomes a strategic enterprise asset, vital for product quality, test case traceability, and regulatory compliance. Protecting this asset is paramount, and as its scale and importance grow, organizations require specialized data management capabilities that go beyond standard application features to ensure complete resilience and governance.

Enterprise adoption of large language models introduces a fundamental security challenge: how do you grant AI agents access to internal data without creating a backdoor that bypasses your existing access controls? Traditional database connections rely on service accounts with broad permissions, but when an LLM queries your customer records or financial data on behalf of a user, it must respect that user's specific entitlements.

The rapid proliferation of Artificial Intelligence (AI) agents and Large Language Models (LLMs) is transforming how businesses operate. From automating customer service to generating complex reports, AI agents are becoming indispensable. However, this explosion of AI-driven interactions brings with it significant challenges in management, security, and governance.

How Fivetran uses Activations internally to power integrations, operations, and product workflows.

How open, standards-based architecture gives you control over your data, costs, and AI future.

Data privacy laws continue to evolve and expand their reach, touching consumers, businesses, and regions of the world. The European Union’s General Data Protection Regulation (GDPR) has inspired many countries to establish their own regulations and set similar parameters for data collection. The Chile Data Protection Law is one of these regulations. While staying compliant isn’t always simple, it’s necessary for your operations and maintaining customer trust.