For platform and data infra engineers

You already run Spark. Here is the warehouse.

Keep the engine and the cluster you have. Add the worksheet, the per-query profile and the cost view your team keeps asking for.

Join the waitlist or run it locally

01 / the problemThe engine is not the problem. The surface is.

You have clusters, jobs and notebooks. What you do not have is a worksheet, a profile for each query, or a way to see what a query cost. So engineers read job logs, and the bill is a single line item nobody can attribute.

TODAY Your Spark cluster, jobs, notebooks job logs WORKSHEET QUERY PROFILE COST PER QUERY

02 / what changesSame facts, better surface

Today

  • Notebooks stapled to a long-lived cluster
  • Job logs instead of a query profile
  • A cluster bill you cannot attribute per query

With Flashpoint

  • A worksheet your analysts can open
  • An operator-tree profile per query
  • Per-second metering and a cost center

03 / how it worksThree steps

Create a warehouse

One REST call, or the UI. XS is driver plus one executor.

Attach your client

Any Spark Connect client, including PySpark and notebooks. Existing DataFrame and Spark SQL code runs unchanged.

Read the profile

Every query gets an operator tree with duration and share of time, at a URL you can paste into a ticket.

04 / questionsStraight answers

Do I have to move my data?

No. The warehouse reads what your Spark deployment already reads. There is nothing to migrate to use the control plane.

Do I have to change my jobs?

No. The client protocol is stock Spark Connect. Your DataFrame and Spark SQL code is not rewritten.

Does it replace my cluster?

No. It gives you warehouses on demand next to what you run. You can adopt it one workload at a time.

What do I actually run?

A stateless gateway, an on-demand driver and spot executors, in your own account. Suspend a warehouse and compute stops.

Join the waitlist

Early access opens in batches. One email when a batch opens, nothing else. Or run the whole stack locally tonight.

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