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Description

Gather real-time data from your database, third-party applications, and various events. Create an analytics database for each workspace along with individual tables for each data source. Utilize AI to formulate complex queries and save these as accessible API endpoints. Monitor changes over time or directly apply queries within your product. Focus more on developing features and less on generating reports. Leverage Velvet to streamline your product development process more efficiently. Generate AI-enhanced visualizations to monitor progress over time. Aim features at specific user segments or present data within your product interface. Ingest any type of data source and execute queries across tables in mere milliseconds. Employ Velvet as your everyday assistant to reveal data insights, ensure that internal stakeholders are on the same page, and construct new features using collaborative building blocks. This approach not only enhances productivity but also fosters a data-driven culture within your team, ultimately leading to more informed decision-making.

Description

Nao is an innovative data IDE powered by artificial intelligence, specifically tailored for data teams, seamlessly merging a code editor with direct access to your data warehouse, enabling you to write, test, and manage data-related code while retaining complete contextual awareness. It is compatible with various data warehouses, including Postgres, Snowflake, BigQuery, Databricks, DuckDB, Motherduck, Athena, and Redshift. Upon connection, nao enhances the conventional data warehouse console by providing features like schema-aware SQL auto-completion, data previews, SQL worksheets, and effortless navigation between multiple warehouses. At the heart of nao lies its intelligent AI agent, which possesses comprehensive knowledge of your data schema, tables, columns, metadata, as well as your codebase or data-stack context. This agent is capable of generating SQL queries, constructing entire data transformation models such as those used in dbt workflows, refactoring existing code, updating documentation, conducting data quality assessments, and performing data-diff tests. Furthermore, it can uncover insights and facilitate exploratory analytics, all while maintaining strict adherence to data structure and quality standards. With its robust capabilities, nao empowers data teams to streamline their workflows and enhance productivity significantly.