Neotask이 Snowflake 데이터 웨어하우스에 대화식 인터페이스를 제공합니다 - 데이터를 일반 언어로 쿼리하고, 오브젝트를 관리하며, Cortex AI 기능을 활용합니다.
Snowflake 웨어하우스를 일반 영어로 쿼리하고 즉시 결과를 얻습니다 - SQL 불필요
대화로 Snowflake 오브젝트, 시맨틱 뷰, 데이터 모델을 생성하고 관리합니다
Neotask을 통해 Cortex AI 검색 및 분석을 사용하여 인사이트를 수집합니다
할 수 있는 것
With 16 actions across Snowflake's Cortex, Object Manager, Query Manager, and Semantic Manager, Neotask gives you full control over your data platform.
Cortex AI Services (3 actions)
cortex_agent - Run Cortex Agent for intelligent data analysis and multi-step reasoning
cortex_search - Search across unstructured content stored in Snowflake
cortex_analyst - Natural language analytics against your semantic models
Object Manager (5 actions)
Create, drop, describe, and list Snowflake objects (tables, views, schemas, warehouses)
Create or alter objects with full DDL control through conversation
Query Manager (1 action)
run_snowflake_query - Execute any SQL against your warehouse with results returned directly
Semantic Manager (7 actions)
List and describe semantic views
Show dimensions and metrics defined in your semantic layer
Write and execute queries against semantic views
Export semantic view DDL for version control
모든 액션은 자율적으로 실행되거나 승인을 요청합니다 - 여러분이 결정합니다.
이렇게 물어보세요
"What were our top 10 revenue-generating customers last quarter? Query the Snowflake data warehouse."
"Run this SQL against Snowflake: SELECT product_category, SUM(revenue) FROM orders GROUP BY 1 ORDER BY 2 DESC"
"Create a new table in Snowflake called 'weekly_signups' with columns for date, region, and count"
"What semantic views exist in our data model and what metrics do they expose?"
"Search our Snowflake unstructured data for any documents mentioning supply chain delays"
"List all tables in the ANALYTICS schema and describe their column structure"
"Query our revenue semantic view to get monthly recurring revenue for the last 6 months"
프로 팁
Schedule automated business reports that run Snowflake queries on a morning schedule and post results to Slack - your leadership team gets daily data briefings without analyst involvement.
Enable approval gates on create and drop object actions - schema changes in production Snowflake are high-impact and worth human review.
Pair Snowflake with MotherDuck in an app group to route ad-hoc analytical queries to the right warehouse based on data location.
Use the Cortex Analyst action for semantic-layer queries - it understands your business metrics and returns correctly aggregated answers without raw SQL.
Multi-agent teams can build automated data pipelines: one agent creates Snowflake objects, another loads data, and a third validates results before surfacing them.