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---
## Introduction

Moving from dbt Core to dbt Cloud streamlines analytics engineering workflows by allowing teams to develop, test, deploy, and explore data products using a single, fully managed service.
Moving from dbt Core to dbt Cloud streamlines analytics engineering workflows by allowing teams to develop, test, deploy, and explore data products using a single, fully managed software service.

dbt Cloud is the fastest and most reliable way to deploy dbt. Develop, test, deploy, and explore data products using a single, fully managed service. It also supports:
- The [dbt Cloud IDE](/docs/cloud/dbt-cloud-ide/develop-in-the-cloud) or command line with [dbt Cloud CLI](/docs/cloud/cloud-cli-installation) for development
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## Prerequisites

- You have an existing dbt Core project connected to a data platform and Git repository.
- You have an existing dbt Core project connected to a Git repository and data platform supported in [dbt Cloud](/docs/cloud/connect-data-platform/about-connections).
- You are dbt version 1.0 or later.
- You have a dbt Cloud account. **[Don't have one? Start your free trial today](https://www.getdbt.com/signup)**!

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3. Configure [Single Sign-On (SSO)](/docs/cloud/manage-access/sso-overview) or [Role-based access control (RBAC)](/docs/cloud/manage-access/about-user-access#role-based-access-control) for easy and secure access. <Lifecycle status='enterprise' />
- This removes the need to save passwords and secret environment variables locally.

4. In **Account settings**, switch on [partial parsing](/docs/deploy/deploy-environments#partial-parsing) to only reparse changed files, saving time.
4. In **Account settings**, enable [partial parsing](/docs/deploy/deploy-environments#partial-parsing) to only reparse changed files, saving time.

5. In **Account settings**, enable [Git repo caching](/docs/deploy/deploy-environments#git-repository-caching) to ensure job reliability, protection from third-party outages, and performance. <Lifecycle status='enterprise' />

## Data platform setup

This section explains the considerations and methods to connect your data platform to dbt Cloud.
This section outlines the considerations and methods to connect your data platform(s) to dbt Cloud.

1. Set up your [data platform connections](/docs/cloud/connect-data-platform/about-connections) and [environment variables](/guides/core-to-cloud-1?step=4) in dbt Cloud. dbt Cloud can connect with a variety of data platform providers including:
1. Set up your [data platform connections](/docs/cloud/connect-data-platform/about-connections) and [environment variables](/docs/build/environment-variables) in dbt Cloud. dbt Cloud can connect with a variety of data platform providers including:
- [AlloyDB](/docs/cloud/connect-data-platform/connect-redshift-postgresql-alloydb)
- [Amazon Redshift](/docs/cloud/connect-data-platform/connect-redshift-postgresql-alloydb)
- [Apache Spark](/docs/cloud/connect-data-platform/connect-apache-spark)
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## Git setup

Your existing dbt project source code should live in a Git repository. In this step, you should connect your existing dbt project source code from Git to dbt Cloud.
Your existing dbt project source code should live in a Git repository. In this section, you will connect your existing dbt project source code from Git to dbt Cloud.

1. Ensure your dbt project is in a Git repository
1. Ensure your dbt project is in a Git repository.

2. Once you’ve set up a dbt Cloud account, you can [connect and configure Git](/docs/cloud/git/git-configuration-in-dbt-cloud) in dbt Cloud to connect your Git repository:
- Connect with one of the [native integrations](/docs/cloud/git/git-configuration-in-dbt-cloud) in dbt Cloud (such as GitHub, GitLab, and Azure DevOps).
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