dbt-migration-hive

Hive/Spark/Databricks to dbt Model Conversion

Purpose

Transform Hive/Spark/Databricks DDL (views, tables, UDFs) into production-quality dbt models compatible with Snowflake, maintaining the same business logic and data transformation steps while following dbt best practices.

When to Use This Skill

Activate this skill when users ask about:

• Converting Hive/Spark/Databricks views or tables to dbt models
• Migrating HiveQL UDFs to dbt
• Translating HiveQL syntax to Snowflake
• Generating schema.yml files with tests and documentation
• Handling Hive-specific syntax conversions (external tables, PARTITIONED BY, LATERAL VIEW, file formats)

---

Task Description

You are a database engineer working for a hospital system. You need to convert Hive/Spark/Databricks DDL to equivalent dbt code compatible with Snowflake, maintaining the same business logic and data transformation steps while following dbt best practices.

Input Requirements

I will provide you the HiveQL DDL to convert.

Audience

The code will be executed by data engineers who are learning Snowflake and dbt.

Output Requirements

Generate the following:

1. One or more dbt models with complete SQL for every column
2. A corresponding schema.yml file with appropriate tests and documentation
3. A config block with materialization strategy
4. Explanation of key changes and architectural decisions
5. Inline comments highlighting any syntax that was converted

Conversion Guidelines

General Principles

• Replace procedural logic with declarative SQL where possible
• Break down complex procedures into multiple modular dbt models
• Implement appropriate incremental processing strategies
• Maintain data quality checks through dbt tests
• Use Snowflake SQL functions rather than macros whenever possible

Sample Response Format

-- dbt model: models/[domain]/[target_schema_name]/model_name.sql
{{ config(materialized='view') }}

/* Original Object: [database].[object_name]
   Source Platform: Hive/Spark/Databricks
   Purpose: [brief description]
   Conversion Notes: [key changes]
   Description: [SQL logic description] */

WITH source_data AS (
    SELECT
        -- Hive BIGINT/INT converted to INTEGER
        customer_id::INTEGER AS customer_id,
        -- STRING converted to VARCHAR
        customer_name::VARCHAR(100) AS customer_name,
        -- DECIMAL converted to NUMBER
        account_balance::NUMBER(18,2) AS account_balance,
        -- TIMESTAMP converted to TIMESTAMP_NTZ
        created_date::TIMESTAMP_NTZ AS created_date
    FROM {{ ref('upstream_model') }}
),

transformed_data AS (
    SELECT
        customer_id,
        UPPER(customer_name)::VARCHAR(100) AS customer_name_upper,
        account_balance,
        created_date,
        CURRENT_TIMESTAMP()::TIMESTAMP_NTZ AS loaded_at
    FROM source_data
)

SELECT
    customer_id,
    customer_name_upper,
    account_balance,
    created_date,
    loaded_at
FROM transformed_data