Looker vs Malloy

Looker helps organizations explore, share, and visualize company data for business decisions. Its LookML semantic models define dimensions, aggregates, calculations, and relationships, then use those definitions to generate SQL queries. Gemini-powered natural-language analytics lets users ask questions, while Dashboard Agents provide summaries and explorations grounded in governed metrics. Looker connects to BigQuery, Spanner, Cloud SQL for MySQL, Cloud SQL for PostgreSQL, and other Google services. The Looker Marketplace offers applications, prebuilt LookML models, visualizations, and actions for delivering data to third-party services. Listed capabilities include hourly refresh, row-level security, scheduled reports, and embedded analytics. Deployment is hybrid, with web, Android, iOS, API, and self-hosted options. Mobile apps let users view Looks and dashboards. Google documents TLS 1.2 or later for data in transit and AES-256 encryption for locally stored sensitive application data. A free 14-day trial is advertised, but there is no free plan. Standard, Enterprise, and Embed each list one production instance, 10 Standard Users, and two Developer Users; their API call allowances differ. Pricing is on request.

Malloy is an open source language for describing data relationships and transformations. Users can define reusable calculations, table relationships and business logic in semantic models, then compile queries into SQL optimized for the connected database. The language supports nested datasets and automatically guards against common errors in sums, averages and counts. Supported databases include DuckDB, MotherDuck, BigQuery, Databricks, Snowflake, PostgreSQL, MySQL, Trino and Presto. DuckDB can read Parquet, CSV and JSON files without additional setup. Malloy offers a Visual Studio Code extension for modeling, querying, transformation and visualization, plus a browser notebook through github.dev that requires a GitHub sign-in. Its Python package loads models, compiles queries and runs statements with DuckDB and BigQuery. The CLI can run Malloy files, build persistent tables and automate transformations and pipelines. The project is free and open source, with web, editor extension, API and self-hosted options, among others. DuckDB connections can disable network access; sandboxed mode also limits file access to allowed directories.