Skip to main content

Cockpit Overview

The Starlake Cockpit is a web-based interface for managing your data projects end-to-end. It connects to the Starlake REST API backend and provides a visual environment for every stage of the data pipeline: extraction, loading, transformation, orchestration, testing, and monitoring.

Setup​

Launch the Cockpit with:

starlake serve

Then open http://localhost:9900 in your browser.

Command Options​

starlake serve [options]
OptionDescriptionDefault
--host <value>Address on which the server listens127.0.0.1
--port <value>Port on which the server listens9900
--reportFormat <value>Report format: console, json, or htmlconsole

The host and port can also be set through environment variables SL_HTTP_HOST and SL_HTTP_PORT.

Examples​

Listen on all interfaces on port 11000:

starlake serve --host 0.0.0.0 --port 11000

See the Configuration page for deployment options, authentication providers, and all environment variables.

Projects​

The home screen lists all projects in your workspace. From here you can create new projects, switch between existing ones, and manage workspace-level users. Each project has its own settings, connections, and pipeline definitions.

Within a project, members are assigned one of three roles: Admin (full access including settings and user management), Owner (project-level control), or User (read and execute access).

Data Extraction​

The Extraction section lets you configure jobs that pull schemas and data from external databases. You can create, edit, and delete extract configurations, each targeting a specific connection. The UI also supports extracting schemas from external sources to bootstrap load configurations.

Data Loading​

The Loading section organizes ingestion configurations by domain and table. You can browse load configurations by folder, edit domain-level and table-level settings, and configure file formats (CSV, JSON, XML, position-based), metadata, write strategies, and schema definitions.

Data Transformation​

The Transformation section provides a notebook-style editor for SQL and Python transform jobs. You can create and edit jobs, validate SQL syntax against the target database, and preview transformation logic. Each job maps to a named transform that can be orchestrated independently.

Semantic Modeling​

The Semantic Modeling section offers a visual diagram editor for defining domains, tables, and their relationships. You can drag and arrange entities, define columns and types, and visualize the data model as an interactive flow diagram powered by React Flow.

SQL Worksheet​

The SQL Worksheet is an interactive editor for running ad-hoc queries against your project data. It supports syntax highlighting, autocompletion, and result display in a data grid. Use it to explore data, validate queries, or test transformation logic before committing it to a job.

Orchestration​

The Orchestration section manages workflow scheduling and execution. You can build DAGs from templates, generate orchestration code for Airflow, Dagster, or Snowflake tasks, and trigger runs directly from the UI. The schedule configuration page lets you define cron-based schedules for automated pipeline execution.

Data Quality and Testing​

The Testing section lets you define and run data quality checks using expectations. You can configure tests per job, run them against live data, and view results. The monitoring dashboard tracks data freshness and surfaces quality metrics over time.

Git Integration​

The Git section provides full version control for your project metadata. You can view status, stage and commit changes, create and switch branches, push to and pull from remotes, and resolve merge conflicts. The version history page shows the commit log for your project.

AI Assistant​

The AI Assistant is available across the Loading and Transformation sections as a chat interface. It can generate column descriptions from sample data, explain SQL queries, suggest fixes for errors, and help scaffold new configurations. Responses stream in real time.

Settings​

The Settings section groups all project configuration:

  • General — project name, description, and metadata
  • Application — application YAML configuration with validation
  • Connections — database connection templates and credentials
  • Environment Variables — project-level key-value pairs for pipeline configuration
  • Custom Types — user-defined data types alongside built-in defaults
  • Schedule — job scheduling configuration
  • Members — project team members, roles, and invitations
  • Git References — branch and tag management