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This guide walks you through how to use Recce, whether you're exploring for the first time or expanding to Recce Cloud.
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## Prerequisites
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Recce requires that your dbt project has two separate [environments](https://docs.getdbt.com/docs/environments-in-dbt) that refers to different [schemas](https://docs.getdbt.com/docs/core/connect-data-platform/connection-profiles#understanding-target-schemas). For example, one for production with prod schema and another for development with dev schema.
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Use Recce to validate your data changes with clarity and confidence. Recce helps you:
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Recce can get your schema from all data warehouses, so you don't need to update the profiles.yml. However, if you use duckdb, you need to add a separate schemas in your dbt profile so you can run locally.Your `profiles.yml` might look something like this:
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- Explore what changed
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- Validate downstream impacts
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- Collaborate through shareable checklists
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For a hands-on walkthrough, check out the [Jaffle Shop Tutorial](./get-started-jaffle-shop.md).
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## Start Recce with two commands
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Navigate to your dbt project and run:
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```shell
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cd your-dbt-project/ # if you're not already there
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pip install -U recce
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recce server
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```
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Recce use dbt [artifacts](https://docs.getdbt.com/reference/artifacts/dbt-artifacts) generated with every invocation. You can find these files in the `target/` folder.
The regeneration of the `catalog.json` file is not required after every `dbt run`. it is only required to regenerate this file when models or columns are added or updated.
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## Get instant visibility
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With just one environment, Recce gives you:
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-**Lineage clarity**: Trace changes down to the column level
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-**Query insights**: Explore logic and run custom queries
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-**Live diffing**: Reload and inspect changes as you iterate
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Perfect for early exploration, root cause analysis, and faster debugging before involving others.
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## Unlock diff & validation with two environments
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Not all data changes are obviously right or wrong. Comparing metrics before and after is key to confident validation.
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When you configure two dbt environments (e.g. prod and dev schemas), Recce lets you:
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-**Explore** modified models and downstream impact
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-**Validate** changes via diffs or custom queries
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- Add validation results to **Checklists** for review and alignment
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### How to setup two environments
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Setup two separate [environments](https://docs.getdbt.com/docs/environments-in-dbt) that refers to different [schemas](https://docs.getdbt.com/docs/core/connect-data-platform/connection-profiles#understanding-target-schemas), e.g., prod for production and dev for development.
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Recce supports schema discovery across all major warehouses without needing to modify your `profiles.yml`.
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However, if you're using DuckDB, you’ll need to explicitly define both schemas to make it run locally. For example:
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```yaml
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jaffle_shop:
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target: dev
@@ -24,32 +79,16 @@ jaffle_shop:
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schema: main
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```
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## Install Recce
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Install Recce using `pip`:
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```shell
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pip install -U recce
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```
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## Use Recce in your dbt project
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The following instructions give an overview of the process of using Recce in your dbt project. For a hands-on tutorial, please check the [Jaffle Shop Tutorial](./get-started-jaffle-shop.md).
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Navigate to your dbt project.
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```shell
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cd your-dbt-project/
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```
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### Prepare dbt artifacts
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Recce expects two sets of dbt [artifacts](https://docs.getdbt.com/reference/artifacts/dbt-artifacts) to be present:
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- `target-base/` - dbt artifacts for to be used as the base for the comparison e.g. production
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- `target/`- dbt artifacts for your development branch
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#### Prepare artifacts for the `base` environment
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For most data warehouses, you can download the artifacts generated from the codebase of your main branch. You don't need to re-run the whole production in your local.
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#### Generate artifacts for the `base` environment
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For most data warehouses, you can download the artifacts generated from the codebase of your main branch.
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You don't need to re-run the whole production in your local.
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However, if you use duckdb, you need to generate the artifacts for the base environment.
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Checkout the `main` branch of your project and generate the required artifacts into `target-base`. You can skip `dbt build` if this environment already exists.
If you’ve followed the steps above and are ready to share your checklist with others, Recce Cloud makes it easy.
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Just one link gives full context:
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- Lineage
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- Diff results
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- Checklist validation with comments
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## Start the Recce server
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Start the Recce server with the follow command:
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```shell
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recce server
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```
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Recce use dbt [artifacts](https://docs.getdbt.com/reference/artifacts/dbt-artifacts), which is generated when every invocation. You can find these files in the `target/` folder.
The regeneration of the `catalog.json` file is not required after every `dbt run`. it is only required to regenerate this file when models or columns are added or updated.
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## First Time Guide for Recce instance
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After you start the Recce server, you can see the Recce instance, the Web UI of the active Recce server.
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Here are the 3 steps to use Recce: (see the image below)
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Built for teams, Recce Cloud includes secure, cloud-hosted sharing and collaboration features designed for fast reviews and confident sign-off.
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1. Click the model you want to check
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1. Click “Explore Change”
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1. Click “Add to Checklist”
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{: .shadow}
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[Sign up for Recce Cloud](https://datarecce.io/pricing) to unlock collaboration at scale.
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