offdata ai — agentic AI for data modelers and data engineers

Snowflake

AI Snowflake schema generator

Describe your domain in plain English. Ship a Snowflake warehouse — DDL with CLUSTER BY and TIMESTAMP_NTZ, plus a complete dbt project with sources, staging, marts, tests, and seeds.

ddl/snowflake.sqlsnowflake sql
CREATE OR REPLACE TABLE analytics.dim_customer (
  customer_sk   STRING        NOT NULL,
  customer_id   STRING        NOT NULL,
  name          STRING,
  plan_id       STRING,
  valid_from    DATE          NOT NULL,
  valid_to      DATE,
  CONSTRAINT pk_dim_customer PRIMARY KEY (customer_sk)
)
CLUSTER BY (customer_id, valid_from);

CREATE OR REPLACE TABLE analytics.fct_usage (
  usage_id      STRING        NOT NULL,
  customer_sk   STRING        NOT NULL,
  plan_id       STRING        NOT NULL,
  event_ts      TIMESTAMP_NTZ NOT NULL,
  api_calls     NUMBER(18,0)  NOT NULL,
  CONSTRAINT pk_fct_usage PRIMARY KEY (usage_id)
)
CLUSTER BY (event_ts, customer_sk);

OffDataAI is purpose-built for Snowflake. Our generator emits Snowflake-native DDL with correct types (TIMESTAMP_NTZ, NUMBER(p,s), STRING), CLUSTER BY clauses tuned to your fact grain and dimension keys, and primary-key constraints that document your model. From the same conversation, you also get a complete dbt project ready for dbt build — and realistic seed data so you can prototype without source connections.

What OffDataAI generates

  • Snowflake-native DDL

    Real Snowflake syntax: TIMESTAMP_NTZ, NUMBER(p,s), STRING, CLUSTER BY, transient and dynamic tables — never generic ANSI SQL.

  • Kimball, Data Vault 2.0, or 3NF

    Three paradigms supported on Snowflake. Pick the one your team uses; the generator shapes the IR and DDL accordingly.

  • Full dbt project

    dbt_project.yml, sources, staging, marts (with cluster_by), schema tests, and seeds. dbt build works on the first run.

  • SCD2 handled correctly

    Dimensions with SCD2 history get valid_from / valid_to windows, surrogate keys via dbt_utils.generate_surrogate_key, and clustering on the natural key.

Frequently asked questions

Can OffDataAI generate Snowflake DDL from plain English?
Yes. Describe your business domain in plain English. OffDataAI's interview agent asks targeted follow-up questions, synthesizes a typed Intermediate Representation, validates it, and emits Snowflake-native DDL with correct types (TIMESTAMP_NTZ, NUMBER(p,s)) and clustering (CLUSTER BY).
Does OffDataAI use Snowflake-specific features like CLUSTER BY?
Yes. OffDataAI's Snowflake generator emits CLUSTER BY clauses on facts (typically by event timestamp + a high-cardinality key) and on SCD2 dimensions (by natural key + valid_from). Types use TIMESTAMP_NTZ, NUMBER(p,s), and STRING — never generic ANSI types.
Does it support Kimball, Data Vault, and 3NF on Snowflake?
Yes. All three paradigms are first-class for Snowflake. Pick the paradigm; the IR and generators shape the output accordingly.
Does it generate a dbt project for Snowflake?
Yes. OffDataAI emits a complete dbt project for Snowflake — dbt_project.yml, sources, staging, marts (with cluster_by config), schema tests, and seed CSVs. dbt build works on the first run.
How is this different from asking ChatGPT for Snowflake DDL?
ChatGPT can sketch DDL but rarely uses Snowflake-native syntax consistently. OffDataAI runs a structured pipeline (interview → IR → validate → generate) with a dedicated Snowflake generator, so the output is reproducible and warehouse-ready.

Your data warehouse is one conversation away.

Describe your domain, or open one of 150+ production-grade templates. ERDs, DDL, and a complete dbt project — generated in under a minute.