Databricks’ $134B Signal: Why Data Platforms Now Matter Most
Enterprise data platforms are software environments that let organisations collect, organise, govern, and operationalise structured and unstructured information so AI systems, analytics tools, and automated agents can use data safely and at scale across business functions. Databricks has become the clearest indicator of how vital this layer has become for enterprise AI adoption. The company’s recent funding round of more than USD 7 billion (approx. RM32.2 billion), including USD 5 billion (approx. RM23 billion) in equity at a USD 134 billion (approx. RM617.6 billion) valuation, shows investors now see data infrastructure as central to AI value creation. Databricks built its position by solving problems around unstructured data and then expanding into structured data through its Lakehouse concept. With an annual revenue run rate of USD 5.4 billion (approx. RM24.9 billion) and more than 65% year-over-year quarterly revenue growth, its rise underlines a structural shift in AI infrastructure spending away from standalone model bets toward the data foundations that make those models useful.
From Models to Infrastructure: How AI Budgets Are Being Rewritten
Databricks and Snowflake are vying to own the layer where corporate data becomes usable for analytics, automation, and agentic AI. While model providers dominate headlines, most enterprises are finding that the bigger constraint on AI outcomes is data readiness, not access to another model. This is driving AI infrastructure spending into platforms that can unify ERP data, documents, logs, images, and messages under shared governance and security. ERP and application leaders now face a practical question: do they orient their AI roadmaps around these emerging data hubs or try to build their own data stacks? As agentic AI requires current operational context and reliable permission controls, data platforms are becoming strategic control points inside enterprise architectures. The new AI battleground is less about which foundation model is best and more about who can prepare, protect, and operationalise data for dozens of evolving models and agents.
Snowflake and Nutanix: Growth, Gaps, and Competitive Pressure
Snowflake reached its initial lead around structured enterprise data in the cloud, making it easier to feed reporting and SaaS applications. When demand shifted toward unstructured, AI-ready data, that strength became a gap the company has been racing to close. Snowflake is now expanding into open-source databases, unstructured data, and AI workloads, while stressing simplicity, governance, and privacy. According to Wolfe Research’s Alex Zukin, Snowflake is growing about 30% annually and has managed to accelerate that growth despite its scale. At the same time, its falling share price shows public market unease with software vendors adjacent to AI rather than clearly positioned at its core. Nutanix, focusing on hybrid-cloud data infrastructure, faces similar pressure to prove that its platforms are where AI work will run, not just where legacy virtual machines sit. Together, these results show how crowded and contested the enterprise data platform category has become.
MongoDB’s AI Tailwind and the Rise of Operational Data for AI
MongoDB’s latest results add another angle to the enterprise data platforms story: operational databases are also being pulled into the AI infrastructure race. The company reported 25% revenue growth, driven by demand tied to AI applications and cloud-native projects, signalling that developers are standardising on databases that can support both transactional workloads and AI-enriched experiences. As teams embed retrieval, recommendations, and generative features directly into applications, the line between systems of record and AI systems of engagement is blurring. That pushes AI infrastructure spending toward platforms and databases that can handle high-volume transactions while keeping data ready for model consumption. For CIOs, MongoDB’s performance highlights that AI-ready data is not only a warehouse or lakehouse concern; it now starts at the operational layer, where schemas, performance, and governance decisions can either accelerate or constrain later AI projects.
Acquisitions, Agentic AI, and What Comes Next for Enterprise Buyers
Databricks and Snowflake are using acquisitions to widen their roles from data storage to full AI infrastructure. Databricks bought MosaicML for about USD 1.3 billion (approx. RM6 billion) to add generative model training and customisation, and acquired Arcion, Neon, Mooncake Labs, and Tabular to bring in real-time data access and broader interoperability. Snowflake acquired Neeva for roughly USD 185 million (approx. RM851 million), gaining its own large language model work and its current CEO, Sridhar Ramaswamy. These moves show that data platform valuations are now tied to how well vendors can support model choice, governance, and agentic AI, not just how cheaply they can store data. For enterprise buyers, the strategic question is which platforms will remain open, interoperable foundations as AI stacks evolve—and how tightly to anchor ERP, analytics, and automation roadmaps to a small set of these emerging data hubs.
