In a world where GenAI, LLMs, and unstructured data dominate headlines, two major acquisitions recently made by industry giants signal a more grounded truth:

• Snowflake acquired Crunchy Data — a company known for hardened, enterprise-grade PostgreSQL.
• Databricks acquired Neon — a startup redefining Postgres with serverless, AI-native capabilities.

At first glance, this may seem counterintuitive. Why would two cloud leaders in the cutting-edge AI space double down on a decades-old relational database?

The answer lies in a deeper realization: structured data, transactional logic, and real-time access are critical to AI’s next evolution.

1. Structured Data Is Still the Bedrock of Enterprise AI

Despite the rise of LLMs trained on the open web, the most valuable business insights live in structured data:

• Financial transactions
• CRM entries
• Supply chain records
• Product telemetry

This data is:

• Trusted: collected over years with domain-specific context.
• Governed: with clear ownership and lineage.
• Timestamped: ideal for predictive modelling and business intelligence.

For GenAI applications to work reliably in the enterprise — copilots, agents, or autonomous systems — they need a steady stream of high-quality, structured data. PostgreSQL and other RDBMSs remain the anchor point.

2. Real-Time AI Needs Transactional Access

Modern AI systems aren’t just performing analysis. They’re increasingly:

• Executing actions autonomously
• Interacting with live systems (inventory, users, databases)
• Writing back outcomes to support closed-loop learning

This demands low-latency, safe transactional access — a feature unstructured data lakes and vector databases don’t provide.

Postgres, especially in serverless and scalable forms like Neon, becomes a foundation for:

• Live retrieval-augmented generation (RAG)
• Agent-based orchestration
• Embedded AI in operational workflows

3. HTAP: The Convergence of OLTP + OLAP

The traditional split between:

• OLTP (Transactional) systems for capturing real-time operations
• OLAP (Analytical) systems for generating insights

…is now blending into HTAP (Hybrid Transactional/Analytical Processing). AI workloads demand this convergence:

• They read live data to make decisions.
• They write back to databases.
• They trigger workflows and updates.

Postgres, with its evolving capabilities, is uniquely positioned to serve as this hybrid platform.

4. What It Means for Snowflake and Databricks

Snowflake: Doubling Down on Trust and Compliance

Crunchy Data offers hardened Postgres deployments trusted in government, healthcare, and financial sectors.

By acquiring it, Snowflake:

• Strengthens its governance and security story
• Brings Postgres closer to its secure Data Cloud
• Positions itself as the go-to platform for regulated AI adoption

This is not a pivot. It’s a fortification of its structured data core for the AI-native world.

Databricks: Speed, Developer Experience, and AI-Nativeness

Neon is fast, serverless, and built with a modern developer experience in mind.

For Databricks, this means:

• A Postgres engine designed for experimentation and branching
• Instant sandboxing of AI workloads
• Integration with vector search, notebooks, and ML pipelines

This aligns with their mission to be the AI-native Lakehouse.

Final Thought: Postgres Is Not Legacy — It’s the Future, Reimagined

These acquisitions aren’t about going backward.

They signal that:

The next generation of AI requires more than large models and unstructured embeddings. It needs reliable, real-time, structured systems to connect intelligence with action.

Snowflake and Databricks see this clearly — and they’re reshaping the AI infrastructure stack accordingly.

We’re entering an era where Postgres isn’t just a database — it’s the AI agent’s trusted brain and memory.

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