Sub-second dashboards for thousands, included in Athena services, semantic models from your catalog, and agents that query with each user's governed permissions.
79 milliseconds on 2 million rows. Every user, their own permissions.
The warehouse and the BI layer monetize compute, so they ration it. Dashboards refresh in hours, capacity SKUs get bought and then throttled, and the analytics team spends its week defending a queue. Then agents arrive and ask a hundred questions where a human asked one.
Most groups have never had analytics and business documents in the same ecosystem, so every "why" becomes a ticket.
One governed layer. Fresh data. An engine you are allowed to floor.
Read what you have. Athena reads the existing BI semantic model, runs its measures, and browses reports. It queries Snowflake, Databricks, and BigQuery with each user's own permissions. Nothing moves on day one.
Model once. From your warehouse catalog and a paragraph of context, Athena drafts dimensions, measures, and joins. Governance reviews it like a document. Everything downstream queries through it.
Rebuild the slowest dashboard. Import the existing report, land the table in Iceberg on your storage, serve it sub-second as an Application to thousands, included in Athena services.
Consolidate when it pays. Open Iceberg means the tables are yours. Move more when the numbers say so; keep what you must.
A store-operations table with hundreds of millions of rows refreshed in about four hours for thousands of concurrent users at a Fortune 500 retailer. Rebuilt on the Lakehouse: sub-second cross-filter, included in Athena services. If you keep legacy BI, Athena reads its semantic layer from the same surface.
A data lead typed three sentences about their business. Athena inspected the raw warehouse catalog and drafted dimensions, measures, and calculations with lineage. "That's the power of AI and LLMs today."
Several ungoverned capacities, each with its own refresh schedule and throttling, collapsed onto one governed platform with fresher data.
Publish a dashboard to users who have no Snowflake account. Row-level security per viewer; the agent still queries with the publisher's governed model.
Notebooks with isolated compute and per-user volumes on billion-row datasets. Ephemeral Postgres to 1 TB. The tooling you were paying another vendor for.
Stale, failed, or over-complex pipelines and sync gaps surfaced with root cause and a plain-language summary posted to Slack. "We get no notification whatsoever that there's an outage with [our data sync]" was the brief.
Open Iceberg format made a warehouse migration a non-event for the dashboards at one retailer.
“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.”
“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.”
“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.”
“This is super cool. So in the future, our data will be sitting in [a cloud data warehouse]... How is this running so quickly?”
“You guys are functioning better because the harness is tested for a longer period of time against enterprise customers.”
“The developer platform that Athena comes up with [...] basically unlocks the next level engineering productivity.”
“It's like I'm running analysis that would have taken us, like, maybe a week, two. And thirty, forty minutes.”
“I envision Athena and [our data platform] as the combination... that's the stack for me.”
“Everything in Athena, it basically is you choose which weapon you wanna use.”
Verbatim from customer calls. Customers anonymized.
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.
Snowflake, Databricks, BigQuery, Redshift, SQL Server, Oracle, Prometheus, dbt-style catalogs. Agents query with each user's warehouse permissions. Nothing is copied unless you migrate it, and then it is Iceberg, so it is still yours.
Exotic Custom BI visuals do not always round-trip. Standard visuals, measures, and relationships do; the import lists what did not and you rebuild those once.
Take on report requests, build and maintain data models, monitor pipelines, and answer business questions from governed definitions, so analysts spend time on analysis.
Agents answer from the same governed metric definitions as your dashboards.
No. Start on top of what you have and modernize one piece at a time.
With the report or question the business asks for most often.
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.
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.
Build, Work, and Data on top; 150+ connectors and every surface in and out; one map of the whole platform.
Ready-made analytics briefs you can run on a schedule.
We will import the dashboard, land the table, and let you click the filter.