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IndieBI Direct Data Access

One Data Feed for Every Store

If your data is siloed, your decisions are too. The IndieBI DDA (Direct Data Access) SDK unifies the commerce data in your IndieBI account across every major storefront and hands it back through a single Python interface, so your team works from one source of truth instead of a folder of exports.

Fragmented per-store data on the left, consolidated by IndieBI into one unified dataset on the right

Where to Start

Limited Preview

The IndieBI DDA SDK v2 is in limited preview and moving fast. Both the schema and the capabilities are still expected to change rapidly, so be cautious about putting it behind a production pipeline before it settles.

  • Releases can break things. Table names, columns and function signatures may change between versions. Ask your IndieBI contact what changed before you update, and pin a version if you have a pipeline that must not move.
  • Not every table is final. Some are still being reshaped, and a few have been withdrawn to come back redesigned. Tables & Schema Reference is the current truth; treat it as a snapshot rather than a contract.
  • Coverage varies by portal and by plan. What you can query depends on what each storefront reports and what your account is provisioned for. See Portal Support.

What You Get

One picture of your revenue. Sales, wishlists, impressions and refunds stop living in separate worlds. IndieBI DDA consolidates multi-platform data into one model, so you can read title, platform, region and cohort in one place instead of stitching exports together. One set of credentials covers all of it, whether you're a studio with a single title or a publisher with a full catalogue.

No pipeline to build or keep alive. A pipeline looks simple until the real work starts: connectors, auth flows, rate limits, retries, backfills, schema changes, monitoring. Collection runs on our side, every day. You query the result.

Built to plug straight in. Typed schemas, documented tables and copy-paste examples for your tools and your agents. The hard parts are already solved, so your team's time goes into the questions rather than the wiring.

It fits the stack you already have. IndieBI DDA is built on the Delta Lake open table standard, the same format Databricks, DuckDB, Polars and many other tools already read. Your data isn't locked into a proprietary format. Point a notebook, a BI tool, a scheduled job or Claude at it, and every channel reads the same tables.

What's Inside

  • DuckDB client. SQL against Delta Lake tables on Azure, returned as validated Polars DataFrames.
  • Polars client. Lazy evaluation for large date ranges; nothing is fetched until you .collect().
  • Typed schemas. Pandera-validated DataFrames, so you know which columns you have before a query runs.

Compared With Building In-House

Building it in-house IndieBI Direct Data Access
Months, sometimes years, before the first useful query About ten minutes from an empty folder to a working dashboard
Fixing issues, updating pipelines and rewriting as you scale Collection and monitoring run on our side; updating is one command
Every new storefront is more engineering time and budget New tables and portals arrive through the same one-command update

Tell us what you need

IndieBI DDA v2 is in rapid development, and the preview is what sets the order of work. Talk to your IndieBI contact if:

  • a capability you're after isn't there yet, or a table doesn't carry the field you need
  • something behaves differently from how these pages describe it
  • you want engineering support wiring IndieBI DDA into your own stack, pipeline or BI tool

Requests raised now are the ones that shape what ships next.