The store-operations live dashboard.
A table with hundreds of millions of rows behind an operations dashboard at a Fortune 500 retailer. Sub-second cross-filter for thousands of concurrent users, included in Athena services.
Data · Athena Lakehouse: Analytics on your data
Store and query billions of rows at millisecond speed in your storage
Analytics on open Iceberg format in your S3, GCS, or Blob—with catalog, semantic models, dashboards, and an engine built for the questions agents actually ask.
Billion-row tables. Three- to eight-way joins.
- Open Iceberg format
- Your object storage
- 79 ms on 2M rows
- 1.2B rows at millisecond engine time
- Per-user warehouse permissions
- Coexists with Snowflake and Databricks
The Problem
You pay for compute you cannot use
The warehouse and the BI layer monetize compute, so they throttle it. Dashboards refresh in hours. Capacity SKUs get bought and then rationed. Analysts wait, and agents, which ask a hundred questions where a human asks one, wait more.
And the data is in a proprietary format, so leaving is a migration.
Open format. Your storage. An engine you are allowed to floor.
How It Works
Land. Model. Query. Own.
Land the data. Migrate or replicate tables to Iceberg in your bucket using connectors or agents. Or point the Lakehouse at existing Snowflake, Databricks, or BigQuery and query through it with each user's own permissions.
Model it. Athena drafts a semantic model from the catalog and context you provide. You review it. Everything downstream queries through it.
Query it. From dashboards, from
=ATHENAQUERYin a sheet, from an agent, from a notebook. Millisecond engine time on billion-row tables.Own it. Iceberg is open. One retailer migrated between cloud warehouses; dashboards queried Iceberg throughout with no changes.
People and agents
What people and agents each do
Lakehouse. Governed storage for everything connectors bring in, from Snowflake, Databricks, files, and systems of record, stored where Palladium says it lives.
| Capability | People can | Agents can | Together |
|---|---|---|---|
| Lakehouse | Connect sources, set retention and residency. | Load, clean, and join data; keep pipelines running. | Both read from the same governed copy, with the same permissions. |
Anything a person can do here, an agent can do with the same permissions, and both land in the same audit trail.
Use cases
Put the Lakehouse to work
Consolidating the Wild West.
Several ungoverned BI capacities, multi-hour refreshes, and frequent throttling. One governed platform, fresher data.
Data-science tooling without a second vendor.
Notebooks with isolated compute and per-user volumes on billion-row datasets. Ephemeral Postgres up to 1 TB.
"Why this number?"
Click a chart. See the exact upstream table, the transformation, and freshness. Finance stops calling the data team.
Digests without dashboards.
Email or Slack digests from the Lakehouse on a schedule: what moved, where, why.
What customers say
“This combination is a winning combination for us, and this is where we see maximum traction, which is how do you apply all the intelligence that you have within [our data platform] and not worry about how you render that output.”
“I envision Athena and [our data platform] as the combination... that's the stack for me.”
“It's good to understand the capability of the platform. That how far, and how quickly you guys can get these things going [...] that has been impressive overall.”
“Lets us get a lot more out of our data than [...] We can sit on top of all of the data in [our data warehouse] as opposed to just, like, our little ingested, you know, three gigabytes semantic models. I think we have a lot of power here.”
“Gave Athena the spreadsheet, explained which column I was trying to figure out, and then ten secs it told me... exactly how the calculation was based on the other data.”
“He wrote the entire stuff. By itself. And I was there, and I was thinking, alright. If I had to write all this stuff, I mean, I would've spent days, days, days.”
“I send that to the programmer and he said, alright. I read your file. I copied the section of the code because he has the right interface, and it works. Wow. Just like magic.”
“One workspace. All different, let's say, avatars of their output data. Right? Suddenly, it's dashboard. Excel is here. PowerPoint is when it's all together. It's very impressive.”
“Use the Athena platform probably closer to the concept and how it was designed, like, with the spaces architecture where you've got a set of primitives and capabilities, and you bring together the right set of capabilities to solve a task.”
“Digital worker, it gets spun up, it gets given access to the toolkits that it needs. It gets given access to the data that it needs. Just exists for the period it needs to exist to perform the task. And then it gets torn back down again afterwards.”
Verbatim from customer calls. Customers anonymized.
The bet
Any customer with real spend on BI, warehouse, or lakehouse tools: we want that business. Not to rip it out on day one. To sit on top with your users' own permissions and let the numbers make the argument. Coexistence first. Consolidation as the outcome.
Governance, stated plainly
Tables live in your object storage, in open Iceberg format.
Every query runs with the user's own warehouse permissions; agents included.
Lineage and freshness on every chart.
Deploy in your VPC, on-prem, or air-gapped with the rest of the platform.
What to plan for
New Lakehouse figures are engine time, not end to end. We publish both when we have both, and we do not round up.
FAQ
Why does this matter to the business?
It makes large amounts of data fast to use for people and agents, without locking it into a vendor.
Where does our data live?
In your own storage, in open formats.
Do we have to migrate?
No. Start on top of your existing warehouse.
One Platform Underneath
AGS · Athena Governance System
Records every change by a person or an agent, rolls back one contributor's edits without losing anyone else's, and keeps the model, instructions, and sources behind each agent action.
Palladium · deployment
How the platform is deployed: Athena's managed cloud, your cloud on AWS, GCP, or Azure, on-prem, air-gapped, or GovCloud. Same platform in every option.
How it fits together
Build, Work, and Data on top; 150+ connectors and every surface in and out; one map of the whole platform.
The rest of the platform
Related products and stories
Bring one ugly table and one slow dashboard.
We will land the table, rebuild the dashboard, and show you the cross-filter.