# Create a New Starlake Project with Bootstrap
> Create a new Starlake project with starlake bootstrap. Generates YAML configuration for BigQuery, DuckDB, and other connections. Includes sample data and project structure.
The `starlake bootstrap` command creates a new Starlake project with a standard directory structure. It generates the `metadata/` folder containing `application.sl.yml` (project configuration), environment override files, type mappings, and sample data in `datasets/incoming/`. The sample data includes JSON, CSV, and XML files so you can test loading different formats.
## Run the Bootstrap Command
Create an empty directory and run `starlake bootstrap`. The command generates the full project structure in the current directory. By default, Starlake uses the current working directory. To use a different location, set the `SL_ROOT` environment variable.
**Linux/MacOS**
```bash
mkdir $HOME/userguide
cd $HOME/userguide
starlake bootstrap
```
To bootstrap in a custom directory:
```bash
SL_ROOT=/my/other/location starlake bootstrap
```
**Windows**
```powershell
mkdir c:\userguide
cd c:\userguide
starlake bootstrap
```
**Docker**
```bash
mkdir $HOME/userguide
cd $HOME/userguide
docker run -v `pwd`:/app/userguide -e SL_ROOT=/app/userguide -it starlakeai/starlake:VERSION bootstrap
```
## Default Starlake Project Directory Structure
The bootstrap command creates the following hierarchy:
```text {2,5,7,8,9,10,11,12,13}
.
├── metadata
│ ├── application.sl.yml # project configuration
│ ├── env.sl.yml # variables used in the project with their default values
│ ├── env.BQ.sl.yml # variables overridden for a BigQuery connection
│ ├── env.DUCKDB.sl.yml # variables overridden for a DuckDB connection
│ ├── expectations
│ │ └── default.sl.yml # expectations macros
│ ├── extract
│ ├── load
│ ├── transform
│ ├── types
│ │ ├── default.sl.yml # types mapping
└── datasets # sample incoming data for this user guide
└── incoming
└── starbake
├── order_202403011414.json
├── order_line_202403011415.csv
└── product.xml
```
Key directories:
- **metadata/**: Contains all configuration files for extract, load, and transform pipelines
- **metadata/expectations/**: Contains data validation rules applied during load and transform
- **datasets/incoming/**: Contains files to be loaded into your data warehouse. The sample data uses the Starbake project (a bakery management demo)
## Configure Your Data Warehouse Connection in application.sl.yml
The `metadata/application.sl.yml` file is the main project configuration. It defines:
- The list of database connections (DuckDB, BigQuery, Snowflake, etc.)
- The active connection reference (`connectionRef`)
- Audit sink configuration
- Environment-specific overrides
Here is the default configuration:
```yaml title="metadata/application.sl.yml"
application:
connectionRef: "{{activeConnection}}"
audit:
sink:
connectionRef: "{{activeConnection}}"
connections:
sparkLocal:
type: "fs" # Connection to local file system (delta files)
duckdb:
type: "jdbc" # Connection to DuckDB
options:
url: "jdbc:duckdb:{{SL_ROOT}}/datasets/duckdb.db" # Location of the DuckDB database
driver: "org.duckdb.DuckDBDriver"
bigquery:
type: "bigquery"
options:
location: europe-west1
authType: "APPLICATION_DEFAULT"
authScopes: "https://www.googleapis.com/auth/cloud-platform"
writeMethod: "direct"
```
The `connectionRef` uses a variable (`{{activeConnection}}`) that is resolved from the environment files. Each environment file sets this variable to point to the appropriate connection.
## Switch Between DuckDB and BigQuery Environments
The files `env.DUCKDB.sl.yml` and `env.BQ.sl.yml` override default variable values for their respective connections. Set the `SL_ENV` environment variable to select the active environment:
**Linux/MacOS**
```bash
SL_ENV=DUCKDB starlake
```
**Windows**
```powershell
SET SL_ENV=DUCKDB
starlake
```
**Docker**
```bash
docker run -v `pwd`:/app/userguide \
-e SL_ROOT=/app/userguide \
-e SL_ENV=DUCKDB \
-it starlakeai/starlake:VERSION
```
## Next Steps: Load, Transform, and Orchestrate Data
With the project created, follow these guides:
1. [Load data](../load/tutorial) into your warehouse
2. [Transform data](../transform/tutorial) for analysis
3. Run transformations from CLI and Airflow
4. Generate project documentation
The tutorials use the [Starbake](https://github.com/starlake-ai/StarBake/) sample project. Starbake is a bakery management demo that ships with the bootstrap command and demonstrates Starlake's load, transform, and orchestration features.
## Frequently Asked Questions
### What does `starlake bootstrap` do?
The `starlake bootstrap` command creates a new Starlake project with a default directory structure. It generates the `metadata/` folder with configuration files, type mappings, and sample data in `datasets/incoming/`.
### What is the default Starlake project structure?
A bootstrapped project contains: `metadata/application.sl.yml` (project configuration), `metadata/env.sl.yml` (default variables), environment-specific overrides (`env.BQ.sl.yml`, `env.DUCKDB.sl.yml`), and directories for extract, load, transform configurations. Sample data is placed in `datasets/incoming/`.
### How do I switch between DuckDB and BigQuery in Starlake?
Set the `SL_ENV` environment variable. Use `SL_ENV=DUCKDB starlake ` for DuckDB or `SL_ENV=BQ starlake ` for BigQuery. Each environment has its own override file (`env.DUCKDB.sl.yml`, `env.BQ.sl.yml`).
### What is the `application.sl.yml` file?
It is the main project configuration file in Starlake. It defines the list of database connections, the active connection reference, audit configuration, and environment-specific overrides.
### Can I bootstrap a Starlake project in a custom directory?
Yes. Set the `SL_ROOT` environment variable to the desired path: `SL_ROOT=/my/other/location starlake bootstrap`.
### How do I run Starlake bootstrap with Docker?
Create a directory, then run: `docker run -v $(pwd):/app/userguide -e SL_ROOT=/app/userguide -it starlakeai/starlake:VERSION bootstrap`.