Dispatches
FIG. 19 AI enterprise search → FIG. 18 Athena Python SDK → FIG. 17 Athena Skills → FIG. 16 Web Monitor → FIG. 15 Athena for Operations → FIG. 14 Athena for Analytics → FIG. 13 AI search for your DMS → FIG. 12 Multiplayer AI agents → FIG. 11 Athena Anywhere → FIG. 10 Hey Athena → FIG. 09 Athena for Finance → FIG. 08 What code review becomes → FIG. 07 GTM without a CRM → FIG. 06 The company brain → FIG. 05 Two identical stacks → FIG. 04 Finance builds its own APIs → FIG. 03 Model routing in production → FIG. 02 Knowledge workers don't have GitHub → FIG. 01 Pricing, read from transcripts →
Customer story

Sub-second BI dashboards (customer story)

A store-operations dashboard that took four hours to refresh now cross-filters hundreds of millions of rows in under a second, included in Athena services.

100M+rows in the store-operations tableFortune 500 retailerStore Operations DashboardRegionWeekCategoryRefresh time4 hoursMonday users1000sThrottle days30%MonTueWedThuFriFour-hour refresh MondayFour-hour refresh. Capacity frequently throttled.01Import reportExisting BIworkbook02Rebuild semanticmodelOn Athena Lakehouse03Iceberg tablesRetailer's own objectstorage04Point at100M+ row tableSame source dataSemantic model on Lakehouse, same 100M+ rowsAthena rebuilt the semantic model on Lakehouse.Migration checksTrigger · Report import completeSemantic model on Iceberg tablesCross-filter query performanceTwo custom visualsRebuild custom visuals onceDashboard ready, twovisuals rebuiltWhat to plan forTwo custom visuals rebuilt once.Store Operations DashboardRegionWeekCategoryCross-filter time< 1 secConcurrent usersEveryoneViewer accessIncludedStore AStore BStore CStore DStore ERolling to Cross-filter under a second. Everyone at once Monday.Same 100M+ row tableThousands of managersTodayThousands of usersNext phaseIncluded in Athena services×100M+ rows,sub-secondIncluded in Athena services.Sub-second on 100M+ rows.athenaintel.com
Fig. 02The story in 22 seconds, from the old way to “Sub-second on 100M+ rows.”
Before · what we did · after
  1. Before

    A store-operations dashboard at a Fortune 500 retailer refreshed in about four hours. Thousands of managers opened it Monday morning. Capacity was frequently throttled, so queries queued or failed.

  2. What we did

    Imported the existing report. Rebuilt the semantic model on Athena Lakehouse, Iceberg tables in the retailer's own object storage. Pointed it at the same 100M+ row table.

  3. After

    Cross-filter in under a second. Everyone at once on Monday. Included in Athena services.

Four-hour refreshSub-second queryThousands of managers openMonday morning waitCapacity throttle 30%Per-seat BI licensesFortune 500 retailer store-ops dashboardCross-filter < 1 secEveryone at onceIncluded in Athena servicesRolling to 100M+ rows
Fig. 01From a four-hour refresh to sub-second cross-filters on the same data
How long it takes · what to plan for

Two custom visuals did not round-trip. They were rebuilt once.

Athena Lakehouse gives you BI-class speed on hundreds of millions of rows, included in Athena services.

Example workflow

Importing a existing BI semantic model to Athena Lakehouse

SURFACES INWHAT COMES INTHE PLATFORMWHAT GOES OUTDELIVERED BACKWebMobile · voiceText (SMS)EmailSlack · TeamsChromeOffice add-inMeeting botMeetingsWordPowerPointSheetsDataAgentsWorkflows (AOPs)AppsLibrarySessionsSpacesStudiosMeetingsDatabaseDashboardsWordPowerPointSheetsData · APIsApps · portalsWebMobile · voiceText (SMS)EmailSlack · TeamsChromeOffice add-inMeeting botFiles · PDFs1Lakehouse2Semantic models3Dashboards41 INImport the retailer'sexisting BI Report filewith the store-operationsdashboard.2 BUILDRebuild the semantic modelon Athena Lakehouse withIceberg tables in theretailer's object storage.3 RUNPoint the rebuilt model atthe same 100M+ rowtable already in storage.4 ANSWERCross-filter queries completein under a second; thousands ofmanagers use it Monday,.
Fig. 03The route on the system map: Files · PDFs → Lakehouse → Semantic models → Dashboards. Connectors: Legacy BI Report file · object storage · Iceberg tables
What it runs on
Athena Lakehouse →Semantic models & ontology →Dashboards →
What customers say
“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.”
Analytics lead · A Fortune 500 retailer
“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.”
Global Sustainability lead · A global manufacturer
“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.”
Analytics lead · A global CPG provider
“I think a good replacement for the BI. Correct? I don't really need a BI help here. I can develop my own dashboards.”
IT lead · A global manufacturer
“I envision Athena and [our data platform] as the combination... that's the stack for me.”
Analytics lead · A global CPG provider
“This is where the puck's going and this is where the moat is for an enterprise.”
AI Council lead · A Fortune 50 retailer
“So you gave her three sentences. And she spit out that.”
Analytics lead · A Fortune 500 retailer
“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.”
VP-level AI leader · A Fortune 500 retailer
“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.”
Analyst · A global manufacturer
“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.”
Manufacturing analyst · A global manufacturer

Verbatim from customer calls. Customers anonymized.

Related

Related products and stories

Athena Lakehouse →Iceberg tables on your object storage. Milliseconds on a billion rows.Dashboards →Import the existing report. Cross-filter in under a second. Give it to everyone.Spreadsheet consolidation →Department spreadsheets, each in a different shape, collapsed into a single intake table every two weeks—now in fifteen minutes.Semantic model generation →A data lead typed three sentences; Athena inspected the Snowflake catalog and drafted dimensions, measures, and calculations.

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