nao

What is nao?

nao is a specialized code editor built for data professionals, forked from VS Code with native integrations to major data warehouses including BigQuery, Snowflake, and Postgres. The platform features an intelligent copilot system that understands both data schemas and codebases, enabling data teams to write more accurate SQL, Python, and YAML code. nao provides real-time data diff previews, automated quality checks, and lineage impact analysis to help data teams ship faster while maintaining data integrity. The editor includes specialized tools for dbt workflows, allowing users to preview models, create documentation, and run tests directly within the IDE.

Key Features

• Native Data Warehouse Connection

  Direct integration with BigQuery, Snowflake, and Postgres, providing real-time schema context for intelligent code suggestions and execution capabilities.

• Schema-Aware Code Generation

  Intelligent autocomplete and code generation that understands your actual data structure, generating SQL, Python, and YAML code that works with your specific tables and columns.

• Data Diff Visualization

  Side-by-side comparison of code changes and their impact on data output, allowing teams to visualize exactly how modifications affect their datasets.

• Automated Quality Assurance

  Built-in agent tools for running data quality checks, detecting duplicates and outliers, comparing dev and production environments, and assessing downstream lineage impact.

• dbt Workflow Integration

  Comprehensive support for dbt projects including model previews, column-level lineage tracking, automated documentation generation, and test creation within the IDE.

Use Cases

• SQL Pipeline Development : Data engineers and analysts can build and maintain SQL data pipelines with confidence, using schema-aware suggestions and automated quality checks.
• dbt Model Management : Analytics engineers can create, document, and test dbt models while ensuring data lineage integrity and preventing downstream breaks.
• Data Quality Monitoring : Data teams can identify and resolve production data quality issues through automated checks and comparative analysis between environments.
• Database Exploration : Software engineers and data scientists can explore database schemas, write DDL statements, and perform ad-hoc analytics with intelligent assistance.
• Team Collaboration : Large data teams can maintain consistent coding standards and factorized metrics across projects while onboarding less technical team members.

FAQs