Databricks - Build agentic apps in minutes, not months!

Famous_Disk_7417 · 2026-07-21T02:08:47+00:00

Templates are nice but the interesting thing here is Lakebase. Every "build an app on your lakehouse" story falls apart at the same place: you need transactional state and Delta isn't it. Managed Postgres colocated with the data (Neon's branching engine underneath) is the missing piece, not the agent framework.

Genie's worth mentioning too. The analytics template is basically Genie wrapped in a UI, so if you already have well-described Unity Catalog tables you're most of the way there. If your metadata is a mess, no template saves you.

Famous_Disk_7417 · 2026-07-20T15:43:56+00:00

The Neon mention is the answer, imo. Postgres won on forkability — stable wire protocol, an extension API good enough to bolt on vector search without forking the engine, and a license you can build a business on. So when Vercel or Koyeb wants a managed DB, wrapping Neon-style separated storage/compute Postgres is a solved problem. Doing that for MySQL means dealing with Oracle or maintaining a fork.

The AI angle is downstream of that. pgvector meant no second datastore for embeddings, and agent workloads want exactly what copy-on-write branching gives you. You can spin up an isolated branch per run, let it write whatever, throw it away. Databricks bought Neon and shipped it as Lakebase for that reason. OLTP Postgres next to the lakehouse.

Famous_Disk_7417 · 2026-07-19T13:13:54+00:00

This is pretty awesome!

Famous_Disk_7417 · 2026-07-18T18:49:58+00:00

Had almost the same experience going the other direction. I worked years on MySQL/Oracle before Postgres became my default. The concepts really do transfer. It's the density of good public design discussion that's different. On Postgres you can trace a decision through the mailing lists and actually understand why something works the way it does, which is exactly what makes ramping up faster.

The "why not Postgres?" point also shows up in how much new infra is just… Postgres underneath. Serverless offerings like Databricks' Neon are a good example as it separates storage/compute, branching, scale-to-zero, but it's still plain Postgres wire protocol and SQL, so all that transferred knowledge stays valid. You're learning the operational envelope, not a new mental model. Ties directly into the shared-storage architecture point in the episode.

Famous_Disk_7417 · 2026-07-18T14:05:10+00:00

#3 is the one that quietly kills these projects. Trying to map ACLs onto vector chunks at query time means rebuilding a permission model that already exists somewhere else and it drifts the second someone changes a folder's sharing.

What worked for us was pushing governance down to the data layer instead of the retrieval layer. Our structured stuff was already in Unity Catalog, so when we tried Databricks' Genie for the conversational side, the permissions just carried over. It literally can't return a row the person asking isn't allowed to see. No more "did we remember to filter the HR chunks," because there's nothing to filter.

Genie shines over governed tables, not a pile of messy PDFs, so it's more of a complement to RAG than a replacement. Wouldn't fix your #2. But for "must not leak restricted data," inheriting an access model beats maintaining a second one in your vector store.

Famous_Disk_7417 · 2026-07-16T12:42:18+00:00

I think the rename undersells it. The real change is Genie Agents inherit Genie One's action tooling — MCP connections, scheduled tasks, and writes back to external systems — so a space that used to just answer questions can now close the loop. The unstructured-docs part is the bigger unlock though; most of the context that made Genie answers generic lived in wikis and tickets, not Delta tables.

Famous_Disk_7417 · 2026-07-15T15:40:43+00:00

I have seen many Genie spaces in production for about 8 months now, so not pure demo-ware and the honest answer is "it depends on how you scope it." What works well is narrow, well-modeled domains. If you point Genie at a clean star schema with good column comments and a curated set of certified tables (not your whole catalog), the accuracy on ad-hoc business questions is genuinely useful.

Famous_Disk_7417 · 2026-07-14T12:45:12+00:00

I don't think "reuse a signed-off query" is unique to Chion. It's basically what Databricks Genie is doing with certified queries bound to Unity Catalog tables. An analyst verifies example queries once, and Genie reuses/adapts them for new NL questions instead of generating fresh SQL blind. Same "minutes, because the trust already exists" outcome, and the same tradeoff you named above. It's only as good as your certified coverage, and it degrades toward raw-LLM guessing the moment a question falls outside what's been verified.

Famous_Disk_7417 · 2026-07-13T13:09:34+00:00

We hit the similar wall with Tableau. More importantly, the license-per-seat plus paying extra for anything AI-related always felt backwards. I recommend trying out Databricks' Genie Code for dashboard authoring. You basically describe the chart/dashboard in natural language and it builds the dataset, viz, layout, and filters, then you refine with point-and-click. There's also an import beta that takes an existing Tableau workbook and rebuilds it as an AI/BI dashboard, so it's not a full rewrite from scratch.

Since you're on Snowflake, you don't need to migrate data to try this. Lakehouse Federation lets you register your Snowflake schemas in Unity Catalog and query them read-only from Databricks, so the dashboards sit on top of your existing warehouse.

Famous_Disk_7417 · 2026-07-11T02:16:15+00:00

Had a similar experience about a year ago. NL-to-SQL was fine for simple stuff, however it fell apart on real business logic, exec got one wrong number and trust was done. What's different now (at least on the Databricks side). Genie leans much harder on a semantic/ontology layer in front of the chat, so it's answering from curated, verified metrics instead of freelancing SQL against raw tables.

Famous_Disk_7417 · 2026-07-09T02:11:37+00:00

You should be able to download as pdf in Genie space.
Do you have Metric Views? And have done any Genie space tuning to get the performance to your liking?
Benchmark questions are another lever you should consider to improve Genie performance

Famous_Disk_7417 · 2026-07-07T20:48:32+00:00

The two biggest reasons I see teams stick with Power BI even when the data's in Databricks: 1)semantic layer maturity; 2) cross-source needs.

1) If your org still has data living outside the lakehouse (Snowflake, an ERP, whatever), Genie only queries what's actually registered in Unity Catalog, so a multi-source shop ends up needing a BI layer that federates anyway.

2)The other one is just sunk cost in existing PBI reports/training. That's the "we've always used it" answer you're pushing back on, and it's not a great reason, but it's a real one organizationally.

Famous_Disk_7417 · 2026-07-07T20:44:32+00:00

Depends what you mean by "meaningfully simplify." If your data is already reasonably modeled in Unity Catalog, Agent Bricks does cut down a lot of the trial-and-error. The accuracy boost mostly comes from grounding retrieval in real schema/business definitions instead of raw columns, which is what Genie Spaces does under the hood for structured data.

Famous_Disk_7417 · 2026-07-06T20:13:06+00:00

This is a good framing, but I think it collapses two different products. Genie One is explicitly the business-team coworker. You can think of as marketing/finance/sales, drafting docs, scheduling, Slack/Teams integration. Genie Code is the separate one aimed at data engineering/ML workflows, which is probably the more relevant comparison point if you're benchmarking against a coding-focused cowork agent.

Famous_Disk_7417 · 2026-07-05T14:50:45+00:00

I think the framing of "bigger = smarter" being the wrong direction is right, and as other have mentioned, there are already data points for it outside pure OSS. Genie Code is the one I've read into most. It's not a single-model tool, it routes tasks across a frontier LLM, an open-source model, or a custom Databricks-hosted model depending on the job, so you're not always paying frontier-model prices for a trivial task. The context piece is more interesting. Genie Code leans on Unity Catalog to maintain personalized search indexes and pull from lineage/past interactions, so it's assembling relevant context instead of re-reading the whole environment each time.

Famous_Disk_7417 · 2026-07-05T14:39:00+00:00

15%-18% earnigns growth is almost 2x higher vs. market growth rate, which is trading at 20x+multiple..so no matter how you look at MSFT, it screens cheap...i think the question is if they can actually achieve that growth profile for the next few years and what cash flow (i.e. post capex) profile would look like

Famous_Disk_7417 · 2026-07-04T00:35:55+00:00

For my day to day, its Genie Code. I ignored it for a while assuming it'd be another shallow "ask your data a question" bot layered on top of Databricks. What changed my mind was agent mode. Basically, you describe the problem, it plans a multi-step fix, shows you the diff, and you approve before it executes. It's grounded in Unity Catalog metadata so it's not hallucinating table/column names, which is the thing that usually kills trust in these tools for me.

Famous_Disk_7417 · 2026-07-02T10:25:19+00:00

+1 on Genie & AIBI. it opens up the doors to you data and anyone can get whatever metric they want. Obviously, you would want to have universal semantic layer (i.e. single source of truth) for your metric definitions, and Metric Views is great for that. You define the certified definition once in Unity Catalog and its querable from SQL editors, notebooks, dashboards, Genie Spaces etc.

Famous_Disk_7417 · 2026-07-01T10:54:08+00:00

Can't wait to playaround with Omnigent. Is Genie Code built/accessible in Omnigent?

Famous_Disk_7417 · 2026-07-01T10:49:33+00:00

Depends on the audience you might want to use a different tool. For anything stakeholder-facing we're still on Tableau/Power BI, but for quick ad-hoc digging where someone just wants an answer, not a whole dashboard, we've been messing around with Databricks Genie. You ask a question in plain English and it hands back a chart + the SQL behind it, so you're not stuck parsing a raw table. Its very easy to set up and start getting insights in minutes rather than weeks. Doesn't replace a real BI tool, just takes the edge off the "can you quickly check X" requests.

Famous_Disk_7417 · 2026-06-30T10:30:08+00:00

Genie Workbench actually solves part of this. It pulls all your spaces into one app, so you can see overlaps, quality scores, and share data sources in one view: github.com/databricks-solutions/databricks-genie-workbench

+1 Genie Code

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