Systems | Development | Analytics | API | Testing

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

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.

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.

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.

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

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.

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.

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.

When we first introduced Teams in Databox, the goal was to give every team a “home base”, a shared space where they could collaborate around the metrics, Databoards, and Reports that mattered most. Executives could monitor company-wide performance at a glance. Team leaders could use it to organize dashboards around key initiatives and keep their teams aligned. And individual contributors could quickly access the data most relevant to their role. For many of you, that was the case.

Turn quality into a strategic advantage—accelerate outcomes with Sauce AI.