starlake.ai
Official@starlake-ai · France
Starlake provides declarative metadata-driven frameworks for managing data warehouse ingestion, schema governance, and complex transformation pipelines across enterprise environments.
Agent Skills by starlake.ai
Showing 46 vetted skills indexed across 2 GitHub repositories.
starlake-aistarlake-skills
Index Starlake CLI skills for command references and pipeline workflows.
starlake-aiconfig
Validate Starlake YAML configuration files against the Starlake JSON Schema.
starlake-aiexpectations
Enforce data quality expectations in pipelines via YAML definitions.
starlake-aisite
Generate Docusaurus MDX or JSON documentation sites from Starlake metadata.
starlake-aikafkaload
Transfer data between Kafka topics and file storage in Parquet, JSON, and CSV formats.
starlake-aidag-generate
Generate Airflow DAGs from Starlake project YAML definitions.
starlake-aiyml2ddl
Convert Starlake YML definitions into SQL DDL statements.
starlake-aimetrics
Compute table metrics such as row counts and null counts.
starlake-aisettings
Test named connection settings and health across environments.
starlake-aiextract-bq-schema
Extract BigQuery schemas from Google Cloud projects using CLI options.
starlake-aiextract-script
Generate data extraction scripts from Mustache templates.
starlake-aivalidate
Validate Starlake project configurations and data source connections.
starlake-aiinfer-schema
Derive structured schemas from CSV, JSON, and XML data files.
starlake-aidag-deploy
Deploy generated DAGs to a target directory for Airflow or Dagster.
starlake-aicol-lineage
Generates column-level lineage across tasks as JSON for metadata systems.
starlake-aitable-dependencies
Generate a dependency graph of data warehouse tables with SVG/PNG outputs.
starlake-aibootstrap
Initialize a new Starlake project with optional template support.
starlake-aitest
Automates integration tests for Starlake projects via the starlake test command.
starlake-aixls2yml
Convert Excel domain and schema definitions into Starlake YAML files.
starlake-aicnxload
Load Parquet files into JDBC tables with configurable write strategies.
starlake-aiesload
Load structured datasets into Elasticsearch with configurable indexing options.
starlake-aiingest
Ingest data from diverse sources into a data lake or warehouse.
starlake-aibq-info
Retrieve BigQuery table schemas, metadata, and properties across datasets and projects.
starlake-aiyml2xls
Convert YAML-based Starlake definitions into Excel XLSX files.
Frequently Asked Questions About starlake.ai
FAQPage SchemaWhat specific data engineering tasks does Starlake enable?▼
Starlake enables declarative data ingestion, schema inference from raw files, automated DDL generation, and complex transformation management. It supports metadata-driven pipeline orchestration, data quality enforcement through YAML-based expectations, and comprehensive lineage visualization for enterprise data warehouses.
Which personas benefit most from these capabilities?▼
Data engineers, warehouse architects, and platform administrators benefit most from these capabilities. The framework is designed for technical teams managing large-scale batch processing, schema evolution, and strict security compliance across cloud-native data environments.
What are the primary prerequisites for running these pipelines?▼
Users require a configured environment with access to target data warehouses like BigQuery or JDBC-compliant databases. Prerequisites include valid YAML project definitions, source data files in Parquet, CSV, or JSON formats, and appropriate cloud IAM permissions for resource interaction.