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Chained SQL

4 posts · Aug 2021 – Jul 2022

What this trail covers

Hex has evolved its data analysis capabilities by introducing Chained SQL, a feature that allows users to break down complex SQL queries into separate, referenceable cells. This enables easier debugging and inspection of data flow by automatically generating CTEs behind the scenes, improving upon the limitations of traditional CTEs which are confined to a single query. This feature enhances the user experience for writing and managing SQL within the Hex notebook environment. Query mode allows us

All Hex trails
  1. Stop using so many CTEs - Blog | HexThis post introduces and details the functionality of 'Chained SQL' in Hex. It explains how users can reference the output of one SQL cell (a named dataframe) in another SQL cell, with Hex automatically translating this into a CTE. The post highlights the debugging advantages over traditional CTEs, the technical challenge of parsing queries to identify these references, and the seamless integration into the Hex notebook, contrasting it with Dataframe SQL.
  2. Bringing data notebooks into the cloud eraIntroduced Query mode for SQL cells, allowing users to work with data directly in the warehouse without loading it into memory. Launched Chained SQL, enabling referenceable SQL cells that are automatically compiled into CTEs. Added Python pushdown to Snowpark, allowing Python code to run within Snowflake. Enhanced charts with server-side acceleration and aggregation for handling large datasets.
  3. SQL Notebooks > SQL RunnersThis post introduces the concept of SQL notebooks as a superior alternative to traditional SQL runners for organization, versioning, and collaboration. It highlights Hex's features that enable SQL-only workflows, including a cell-based SQL IDE with schema browser, autocomplete, syntax highlighting, and query caching. It also emphasizes built-in versioning, real-time collaboration, and commenting capabilities. The post positions Hex as the best tool for data work even when using only SQL, and links to related content on building data apps with SQL and dataframe SQL.
  4. Introducing: Dataframe SQLIntroduced Dataframe SQL, enabling SQL cells to query other dataframes (results of SQL queries or Pandas operations). This allows for seamless combination of SQL and Python in a single workflow, with dataframes forming their own database. Complex SQL queries can be broken down into chains of statements operating on and passing dataframes between cells, allowing for bidirectional language transitions.