> ## Documentation Index
> Fetch the complete documentation index at: https://docs.e6data.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Quickstart

> From signup to your first query in under 30 minutes. Pick your cloud and follow the path.

The fastest path from zero to running your first query in e6data.

## What you'll do

| Step | Where |
| - | - |
| 1. Sign up | e6data Console |
| 2. Create a workspace | Control Plane |
| 3. Grant storage access | Your cloud's portal |
| 4. Connect a catalog | Compute Plane |
| 5. Create a cluster | Compute Plane |
| 6. Run a query | SQL Editor |

For a guided choice between paths, see [Choose setup path](/get-started/choose-setup-path).

## Step 1 - Sign up

[Sign up and log in](/platform/sign-up-and-log-in) to an e6data account. You'll enter your email, get a magic link, and complete your profile.

## Step 2 - Pick your cloud and choose a deployment type

In the Control Plane, click **Workspaces → Create**. Choose between:

* **Serverless** - fastest to start. e6data manages the compute environment.
* **In Your VPC** - compute runs inside your own Kubernetes cluster. Pick this if network isolation is a hard requirement.

Then follow your cloud's setup guide:

* **AWS**: [Serverless](/platform/deploy/aws-serverless) | [In Your VPC](/platform/deploy/aws-cloudprem)
* **Azure**: [Serverless](/platform/deploy/azure-serverless) | [In Your VPC](/platform/deploy/azure-cloudprem)

## Step 3 - Grant e6data access to your storage

Each cloud has a different mechanism, but the principle is the same: create a cloud-native identity, grant it read access to the data you want to query, and federate it with e6data. No long-lived credentials are shared.

* AWS - IAM role with EKS Pod Identity (or IRSA).
* Azure - User-Assigned Managed Identity with federated credentials.

## Step 4 - Connect a catalog

Once the workspace is provisioned, click **Open** to enter the Compute Plane. Then:

1. Go to **Catalogs → Create**.
2. Choose the metastore type (Hive Metastore, AWS Glue, Databricks Unity, Microsoft Fabric, Apache Polaris, Iceberg REST, or AWS S3 Tables).
3. Fill in the connection details.
4. Click **Test Connection** to verify, then **Create Catalog**.

See [Register and manage catalogs](/query/catalogs/register-and-manage-catalogs) for the full walkthrough.

## Step 5 - Create a cluster

1. Go to **Clusters → Create**.
2. Name the cluster (for example, `analytics-dev`).
3. Choose a Cluster Runtime version and a Cluster Size.
4. Select one or more catalogs to connect.
5. (Optional) Enable Auto-Suspension and Auto-Resume so the cluster pauses when idle.
6. Click **Create Cluster**.

See [Create and manage clusters](/query/clusters/create-and-manage) for sizing guidance and all options.

## Step 6 - Run a query

1. Open the **SQL Editor** in the Compute Plane.
2. Select your cluster and catalog.
3. Type a query:

   ```sql theme={"theme":{"light":"github-light","dark":"github-dark"}}
   SHOW DATABASES;
   SELECT * FROM your_schema.your_table LIMIT 10;
   ```
4. Click **Run**.

## What's next

* Connect a BI tool or driver: [JDBC](/query/connect/jdbc), [Python](/query/connect/python), the [REST API](/query/connect/rest-api-for-sql), or the [PostgreSQL protocol](/query/connect/postgresql-protocol).
* Invite teammates and grant roles: [Identity and RBAC](/platform/identity).
* Set up cost guardrails: [Suspend and resume](/query/clusters/suspend-and-resume).
* Read the FAQs for your deployment path: [AWS Serverless](/platform/deploy/aws-serverless/faqs), [AWS In-VPC / CloudPrem](/platform/deploy/aws-cloudprem/faqs), or [Azure Serverless](/platform/deploy/azure-serverless/faqs).


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