Analysts suggest Snowflake’s integration of Cortex into dbt and Airflow seeks to reduce context switching and embed AI more deeply into production workflows.
Snowflake has broadened its support for Cortex Code CLI, their AI coding agent accessed via terminal, to now include dbt and Apache Airflow. This expansion is designed to assist data professionals in optimizing their engineering processes.
In an official statement, the company declared, “This expanded support enables developers to access secure, AI-powered assistance directly within their favored data engineering platforms. This empowers teams to manage data across various locations and to more effectively create, govern, and refine production-level workflows.”
Both Apache Airflow and dbt are foundational elements within modern enterprise data infrastructures. dbt offers a SQL-based method for converting raw data into analytical models, complete with version control and testing. Airflow, conversely, is used for coordinating and scheduling intricate data pipelines across diverse systems.
Streamlining developer workflows by reducing context switching
This advancement promises significant benefits for both data practitioners and software developers.
“dbt and Airflow act as the command centers for contemporary data ecosystems. Integrating AI capabilities directly into these data transformation and orchestration layers will alleviate complexities, accelerate development cycles, and enhance governance,” remarked Phil Fersht, CEO of HFS Research.
Stephanie Walter, who leads the AI stack at HyperFRAME Research, elaborated on how Cortex Code CLI’s integration lessens obstacles and speeds up development by allowing developers to bypass frequent context switching.
Walter explained that prior to this integration, while developers could use Cortex Code CLI via platforms such as VS Code and Cursor, its AI support was largely “Snowflake-focused,” lacking detailed understanding of dbt models or Airflow DAG structures.
Consequently, developers and data specialists would typically create code in one setting, transfer it to dbt or Airflow, and then manually modify it to align with their ongoing transformation or orchestration requirements, Walter highlighted. She further emphasized that this represented an inefficient, disjointed process that isolated AI assistance from live production pipelines.
Snowflake’s strategic move to dominate the developer experience
This integration is likely a strategic maneuver by Snowflake to assert control over the developer experience for data workflows, extending beyond merely managing the data warehouse.
Fersht stated, “Snowflake aims to establish itself as the intelligent core across the entire modern data stack, even for elements operating independently of its primary platform. The ultimate goal is to create platform stickiness. Should developers depend on Cortex for constructing and managing pipelines, Snowflake will enhance its strategic command over data logic, governance, and AI capabilities, irrespective of the physical location of the raw data.”
Fersht also suggested that this development would intensify rivalry within the AI-native data stack: “Databricks has historically emphasized its openness and the adaptability of its lakehouse architecture. Snowflake’s integration of Cortex Code CLI with dbt and Airflow diminishes this distinction, indicating its capacity to function across diverse environments.”
Furthermore, Snowflake has unveiled a subscription option for Cortex Code CLI, enabling customers without an existing Snowflake workload to utilize the AI coding assistant throughout their data workflows.
Specifics regarding this subscription model have not yet been released by the cloud data warehouse service.
