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

CSV to SQL database

A million-row CSV becomes a queryable database in seconds—typed columns, indexes, and sub-second analytical queries where spreadsheets choke.

Rows in the CSV exportRows in the CSV exportExcel crashes · Google Sheets still processingSpreadsheet toolsManual workaroundOpen in ExcelApplication crashesTry Google SheetsWait for processing30-second queriesNo indexes · no type inferenceSplit into chunksLoad to SQL manuallyWrite schema by handHours of setup workExcel chokes · Sheets is still thinkingSESSIONHere's the exportsales_export.csvrowsDropped on Computer assetDrop the CSV on a Computer asset01Infer column typesDate, numeric, text detection02Create indexesStatistics run on all columns03Database readySQL or plain language queriesQueryable database · typed columns · indexes tunedAthena infers types · creates indexes · runs statisticsSESSIONShow me top sales by regionQuery returned in 500 mssales_exportIndexed · typed columnsTop regions500 ms query timeSeven seconds vs. still processingQueries run in 500 ms · SQL or plain languageAthena wants toFlag inferred date columns for review3 columns detected as datesFormat: YYYY-MM-DD inferredCheck once before queryingWAITING FOR A PERSONConfirm types · database ready to queryIt infers typesCheck the date columns onceSpreadsheet in. Database out.athenaintel.com
Fig. 02The story in 22 seconds, from the old way to “Spreadsheet in. Database out.”
Before · what we did · after
  1. Before

    A million-row export. Excel chokes. Google Sheets is still thinking.

  2. What we did

    Dropped it on a Computer—a managed, sandboxed execution environment—where Athena created a Database asset: typed columns, indexes created, statistics run. Athena created a Database asset in the Lakehouse: typed columns, indexes created, statistics run.

  3. After

    Queryable in SQL or in plain language in seconds, with AGS (Athena Governance System) tracking every query and transformation. A comparison that Google Sheets was still processing finished in seven seconds. Analytical queries that took 30 seconds on a first version run in 500 ms after Athena tuned performance.

Excel & SheetsDatabase assetMillion-row CSV exportExcel crashesGoogle Sheets30-second queriesNo indexesSpreadsheets choke on large datasetsTyped columns & indexesSQL or plain language500 ms queriesSeven-second comparison vs. still processing
Fig. 01From CSV to queryable database: statistics run, indexes tuned automatically
How long it takes · what to plan for

It infers types. Check the date columns once.

Spreadsheet in. Database out.

Example workflow

CSV to database in seconds

SURFACES INWHAT COMES INTHE PLATFORMWHAT GOES OUTDELIVERED BACKWebMobile · voiceText (SMS)EmailSlack · TeamsChromeOffice add-inMeeting botMeetingsWordPowerPointSheetsDataAgentsWorkflows (AOPs)LibrarySessionsSpacesStudiosMeetingsLakehouseSemantic modelsDashboardsWordPowerPointSheetsDashboardsApps · portalsWebMobile · voiceText (SMS)EmailSlack · TeamsChromeOffice add-inMeeting botFiles · PDFs1Apps2Database3Data · APIs41 INDrop a million-row CSVexport that Excel chokeson and Google Sheets isstill thinking about2 RUNComputer asset receivesthe CSV and Athena createsa Database asset3 BUILDAthena types columns,creates indexes, and runsstatistics automatically4 ANSWERQueryable in SQL or plainlanguage in seconds;analytical queries run in500 ms after index tuning
Fig. 03The route on the system map: Files · PDFs → Apps → Database → Data · APIs. Connectors: Computer asset · Database asset · plain language queries
What it runs on
Computers →Database →Spaces →
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
“There's one thing that this platform did, which I've never seen any other platform do... it always keeps my continuity as I'm moving around... it automatically knows where all my documents are, knows where everything is, and knows where I was.”
R&D lead · A global manufacturer
“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.”
Audit lead · A global professional services firm
“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.”
Audit lead · A global professional services firm
“They said that the human cannot multitask, which is true, but Athena can. Without any issue. So I have on a regular basis four or five sessions open.”
Manufacturing analyst · A global manufacturer
“I finally got a chance to play around with the computer asset and build out the chatbot you mentioned. It was really impressive.”
Marketing lead · An AmLaw 100 firm
“This is super cool. So in the future, our data will be sitting in [a cloud data warehouse]... How is this running so quickly?”
Analytics lead · A Fortune 500 retailer
“So it's a full blown running Linux instance with all the bells and whistles that come with that.”
Technology lead · A global professional services firm
“Wow. Athena is a beast. I have fourteen threads that are going in parallel.”
R&D lead · A global manufacturer

Verbatim from customer calls. Customers anonymized.

Related

Related products and stories

Database →One SQL surface for every warehouse. A database from any spreadsheet in seconds.Estimating from blueprints →A contractor texts a blueprint; Athena returns a priced bill of materials with live vendor links in seven minutes.Agent document editing →Open a PowerPoint deck in Studio; the agent edits through Athena's Python library for PowerPoint while you watch every change appear live in the same file.

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