A data platform decision is a multi-year commitment. The migration costs, the talent you hire around it, the integrations you build on top of it: they all follow from the initial call. Microsoft Fabric, Databricks, and Snowflake are converging fast, but they still serve fundamentally different architectural bets. Knowing which bet aligns with your business is what this comes down
GPU infrastructure has been the bottleneck for enterprise ML teams for years. Provisioning clusters takes days. Configuring distributed training environments takes weeks. Debugging data loading bottlenecks consumes engineering time that should be going into modeling. The result is that deep learning teams spend more time fighting infrastructure than building the models their organizations need. Databricks launched AI Runtime in March
Businesses that get predictive analytics right stop reacting altogether. They see churn before it happens, price with precision, and allocate resources against demand curves. Gartner estimates 60% of AI projects lacking AI-ready data will be abandoned through 2026, and S&P Global found 42% of companies scrapped most of their initiatives in 2025 alone. The bottleneck is rarely the technology. Choosing the right
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