offdata ai — agentic AI for data modelers and data engineers

Databricks

AI Databricks schema generator

Describe your domain in plain English. Ship a Databricks lakehouse — Delta tables with liquid clustering, generated columns, and Unity Catalog three-part names, plus a complete dbt project.

ddl/databricks.sqldatabricks sql
CREATE TABLE main.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
)
USING DELTA
CLUSTER BY (customer_id, valid_from)
COMMENT 'SCD2 customer dimension';

CREATE TABLE main.analytics.fct_usage (
  usage_id      STRING       NOT NULL,
  customer_sk   STRING       NOT NULL,
  plan_id       STRING       NOT NULL,
  event_ts      TIMESTAMP    NOT NULL,
  event_date    DATE         GENERATED ALWAYS AS (CAST(event_ts AS DATE)),
  api_calls     BIGINT       NOT NULL
)
USING DELTA
CLUSTER BY (event_date, customer_sk);

OffDataAI is purpose-built for Databricks. Our generator emits Delta-native DDL with the right features: liquid clustering (CLUSTER BY) on facts, generated columns for time-bucket partitioning, ZORDER BY for predicate-heavy reads, and Unity Catalog three-part names everywhere. From the same conversation, you also get a complete dbt project configured for the dbt-databricks adapter.

What OffDataAI generates

  • Delta Lake-native DDL

    USING DELTA, CLUSTER BY for liquid clustering, generated columns, ZORDER BY, and Unity Catalog catalog.schema.table identifiers.

  • Kimball, Data Vault 2.0, or 3NF

    Three paradigms supported on Databricks. The generator emits the right Delta features for each paradigm.

  • Full dbt project

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

  • Unity Catalog ready

    Three-part names, catalog-aware grants, and table comments are emitted out of the box. Drop the DDL into any Unity Catalog workspace.

Frequently asked questions

Can OffDataAI generate Databricks DDL from plain English?
Yes. Describe your business domain in plain English. OffDataAI's interview agent asks targeted questions, synthesizes a typed Intermediate Representation, and emits Databricks-native DDL using Delta Lake syntax, liquid clustering, generated columns, and Unity Catalog three-part names.
Does OffDataAI support liquid clustering and ZORDER BY?
Yes. The Databricks generator emits CLUSTER BY (liquid clustering) on fact tables and ZORDER BY for predicate-heavy columns where appropriate. Generated columns are used for time-bucket partitions when applicable.
Does it use Unity Catalog three-part names?
Yes. All identifiers use the catalog.schema.table format. You can configure the catalog and schema names per project — the generator never hard-codes them.
Does it support Kimball, Data Vault, and 3NF on Databricks?
Yes. All three paradigms are first-class. The synthesis agent shapes the IR; the Databricks generator emits the right Delta features for each paradigm.
Does it generate a dbt project for Databricks?
Yes. OffDataAI emits a complete dbt project for Databricks — dbt_project.yml configured for the dbt-databricks adapter, sources, staging, marts (with cluster_by and liquid clustering), schema tests, and seed CSVs.

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.