Data infrastructure has always lived close to the hard parts of software, even when it doesn’t get much credit for holding everything together. That work is now getting tangled up with AI in very practical ways. Teams need data that moves quickly, stays trustworthy, and carries enough context to support retrieval, recommendations, copilots, analytics, and real-time decisions. That puts pressure on both sides of the house: the databases serving applications and the data engineering systems that move, shape, govern, and prepare the information those applications depend on.