# Google Cloud Dataproc: Customer Evaluation Installation and Usage

This is the customer-ready DataAI ETL evaluation package from Yanbor LLC,
provider of the DataAI product. DataAI runs inside the customer's pipeline; it
is not a hosted service and makes no required DataAI network call.

## Prerequisites

- Java 17
- Apache Spark 3.5.0 with Scala 2.12
- Customer-controlled Google Cloud project and Dataproc evaluation runtime

## Installation and first verification

1. Review LICENSE.md and run smoke-test\run-smoke-test.ps1.
2. Upload the DataAI JARs to a private Cloud Storage location and grant the Dataproc identity minimum access.
3. Attach the JARs to the selected Dataproc Serverless batch or cluster job.
4. Run only against isolated evaluation sources and customer-selected outputs.
5. Verify IAM, networking, catalog, runtime compatibility, output rows, and matrix convergence on the exact Google Cloud runtime.

## Evaluation boundary

- Use only fictional or approved non-production data.
- Review `LICENSE.md`; the standard evaluation is less than 32 consecutive
  calendar days.
- DataAI software is provided AS IS, with no obligations except those
  expressly accepted in a signed commercial agreement or order form.
- Spark, Hadoop, cloud platforms, BI tools, orchestration products, and vendor
  drivers remain customer-provided and governed by their respective terms.
- A successful local smoke test validates the delivered DataAI artifacts. It
  does not claim certification on the customer's external platform.

## Package layout

- `install/`: platform-specific installer or packages, when applicable.
- `lib/`: DataAI JARs required by Spark-based integrations.
- `smoke-test/` or `nuget-smoke-test/`: executable local verification.
- `docs/`: complete function catalog and product-specific guidance.
- `samples/`: fictional evaluation inputs.
- `platform/`: platform-specific examples, notebooks, configuration, POMs, or
  container-review assets.
- `PACKAGE_CONTENTS.sha256`: SHA-256 for every other file in this ZIP.
