Data is everywhere, but access to it doesn’t always translate to value. Organizations have invested heavily in collecting data and deploying analytics platforms; however, for many teams, day-to-day reality still resembles a flood of ad hoc requests and delayed roadmaps. That’s where data productization comes in, not as a buzzword, but as a practical approach
Organizations today are sitting on more data than ever before, yet access to this data remains a persistent challenge. Teams often spend more time searching for data—or recreating what’s already available—because the path to existing assets is unclear. This isn’t a problem of infrastructure but a problem of experience. Data productization offers a new way
Designing secure data solutions isn’t inherently difficult. But implementing them in a modern enterprise? That’s a different story. It requires collaboration across data engineering, infrastructure, identity, security, DevOps, and cloud operations teams—each with their own priorities, vocabulary, and processes. We’ve seen this disconnect play out time and time again: a data engineering team engages the
The Data + AI conference in early June brought together data practitioners from across the globe both in-person and virtually. Over the course of four days attendees had the opportunity to complete trainings, compete in hackathons, attend informational sessions, and hear about the new features coming to the Databricks Data Intelligence Platform. This year it
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