Love Databricks? Why You Should Still Consider Preset for AI Analytics
Databricks is a powerful platform. If your team has invested in the Lakehouse, Unity Catalog, and the broader Databricks ecosystem, you've made a solid bet — and Genie is a natural next step that Databricks makes very easy to adopt.
But "easy to adopt" and "best fit for your analytics needs" aren't always the same thing. Before defaulting to Genie, it's worth understanding how a purpose-built analytics platform like Preset complements your Databricks investment — and in some cases, serves your team better.
Genie Is Good. Here's Where It Gets Complicated.
Genie works by generating SQL dynamically in response to natural language questions, using metadata and context from Unity Catalog to guide that process. For exploratory, ad-hoc analysis — especially across the rich data you've already curated in the Lakehouse — it's a genuinely compelling experience.
Where it gets harder is with the questions your business users ask every day. How you define "monthly active users," what counts as "revenue," which date logic applies to which metrics — these are nuanced, organization-specific definitions that are difficult for any model to infer reliably, even with great schema context. This is the core challenge of bringing AI into BI — and it's one that every platform is still working through. Databricks has invested in tooling to address this: semantic metadata in Unity Catalog, query inspection, evaluation workflows, and feedback mechanisms. These help, but they require ongoing curation to keep Genie accurate as your data and business evolve.
Preset Starts from Your Validated Logic — Not a Fresh Guess
If your team already uses Preset or Apache Superset™, you have something valuable: a library of charts and datasets that your data team has already built, validated, and governs. Those artifacts encode your business logic — your semantic layer — in a form that's been reviewed and trusted.
When an AI agent queries Preset via MCP (Model Context Protocol), it can prioritize retrieving answers from those existing, validated charts and datasets — falling back to query execution only when needed. Rather than re-deriving your business metrics from raw tables, it starts from what your team already knows is correct. This is the same approach that powers Preset AI Assist, our text-to-SQL experience built directly into Superset.
This isn't a knock on Genie. It's a reflection of a simple architectural reality: retrieval from trusted, pre-validated outputs is more consistent than dynamic generation, especially for the high-stakes questions business users rely on every day. Of course, not every question has a pre-built chart — and in those cases, both systems generate SQL. The difference is where they start.
Cost Predictability: A Practical Consideration at Scale
Your Databricks SQL warehouses are already doing a lot of work. Genie adds natural language querying on top of that — which is great for productivity, but worth thinking through at scale.
Genie increasingly operates in an agentic mode, performing multi-step reasoning and exploratory sub-queries to arrive at an answer. This dynamic approach can bypass result caches even for routine questions, making consumption harder to predict as more business users start asking questions throughout the day.
Preset's caching layer means that your most frequently accessed charts — the ones your team looks at every morning — are often served without touching the warehouse at all. For teams looking to optimize dashboard performance, this architecture pays dividends. The more a metric is used, the more predictable and cost-efficient it becomes to serve. That's a meaningful complement to your Databricks investment, not a replacement for it.
Your Databricks Data, Everywhere — With More Flexibility
Here's where Preset and Databricks actually work well together. Preset connects natively to Databricks, so your Lakehouse data is immediately available as the foundation for Preset dashboards and datasets. You keep Unity Catalog as your governance layer. You keep Lakehouse Federation for multi-cloud data access. You keep all the Databricks infrastructure you've invested in.
What Preset adds is a portable analytics layer on top — one that also connects to Snowflake, BigQuery, Redshift, and dozens of other sources, and integrates with semantic layers like dbt and Cube. If your data stack ever grows beyond Databricks, or if different teams use different warehouses, Preset works across all of it without requiring you to re-curate your analytics logic from scratch.
The intelligence layer in Genie — the semantic definitions, evaluation workflows, and curated business logic — lives inside Databricks. That's fine if Databricks is your permanent home. But if your stack evolves, Preset's open foundation travels with you.
MCP: Open Standard vs. Managed Ecosystem
Both Preset and Databricks now support MCP (Model Context Protocol), so this isn't a binary choice. Databricks provides managed MCP servers for Genie spaces and SQL warehouses — a solid, integrated option within their ecosystem.
Preset's MCP implementation is built on Apache Superset's open foundation and is designed to work with any MCP-compatible AI agent, regardless of which data platform sits underneath. If your team uses multiple AI tools, or wants to avoid coupling your agentic workflows to a single vendor's framework, Preset's approach gives you more flexibility as the ecosystem evolves.
The Bottom Line for Databricks Teams
If you're all-in on Databricks and want a tightly integrated experience, Genie is a reasonable choice — and it's only getting better.
But if you want AI-powered analytics that builds on your validated business logic, keeps costs predictable as adoption grows, works across your full data stack, and connects to the broader AI agent ecosystem through an open standard — Preset is a powerful complement to your Databricks investment, not a competitor to it.
You don't have to choose between loving Databricks and getting the best analytics experience. Preset and Databricks work better together.
Ready to see how Preset complements your Databricks investment? Connect your Lakehouse to Preset in minutes — no migration required. Your Unity Catalog data, your validated business logic, and a purpose-built analytics layer that works across your entire data stack.
Get started with Preset | See Preset + Databricks in action | Explore AI-native analytics
Preset is built on Apache Superset and provides enterprise-grade data exploration, visualization, and AI-native analytics across any database or semantic layer — including Databricks.
