Systems | Development | Analytics | API | Testing

OKRs were designed to help companies turn strategy into execution. In practice, though, that’s not always the case. When I first started working on the OKRs feature at Databox, I interviewed customers to understand how they apply OKRs and similar frameworks in their companies today.

You collect massive amounts of data every day: streaming logs, transaction records, user interactions, and sensor data. The goal is transforming this into valuable near real-time analytics and business intelligence. But here’s the problem: most data lakes turn into data swamps where valuable insights get buried under poor organization and slow performance. Apache Iceberg and Trino provide the foundation for organized, high-performance data storage and querying.

Embedded analytics are vital for user engagement. Companies like Stripe, Uber Eats, and Intercom use them to provide real-time, in-app insights. Instead of building this complex functionality from scratch, a solution like Yellowfin allows you to quickly embed powerful, white-labeled analytics, providing a great user experience with less development effort. Don’t just take our word for it - try Yellowfin for yourself by requesting a free trial.

Generate a read-only API for your database, auto-build a dashboard, and chat with your data—without exposing raw SQL or credentials.

QA teams are more critical than ever but also more pressed for time. With faster development cycles and growing complexity, testers must keep speed and quality in balance. Yet test case creation is still slow, manual, and repetitive - consuming time, introducing errors, and risking missed scenarios. It’s time for something better.

Let’s say your team just announced an exciting product update. In staging, everything appeared to work perfectly. But, before you know it, customers flooded into support to complain, the checkout button does not work on iPhones, the animations lag in Chrome, and half the workflows broke in your CI/CD pipeline. That success story quickly turns into a customer support disaster overnight. Here’s the hard truth: modern web applications are too complex to leave testing to chance.

Volatility is the new default. Uncertainty rivals pandemic-era chaos. Go-to-market leaders are stuck between long-term planning cycles and short-term fire drills. That’s a lot of bad news… but Kathleen Booth, SVP of Marketing & Growth at Pavilion, says it doesn’t have to be that way.

Fivetran and Databricks combine to help financial services teams unify data, accelerate AI adoption, and drive real-time, personalized insights at scale.

The industry is rapidly moving towards deeper AI integration than ever before. What was once simply focused on chatbots or recommendation engines has pivoted significantly to AI systems communicating with other AI systems. These AI tools are leveraging multi-agent workflows to accomplish complex tasks that traditional systems have struggled with. Innovation without validation is a liability. Any developer worth their salt will know that these systems require ample testability and validation.

When I took over the forecasting feature at Databox, one thing was clear: users weren’t adopting it the way we’d hoped. To change that, we made several improvements based on user feedback. We added support for seasonality and holidays. Introduced a confidence score to help teams understand how reliable their projections were. And made it possible to save forecasts for future comparison. Each update made the feature more powerful, but even with all those changes, adoption barely moved.