# Dbt Sf To Bq Translator

> Translates Snowflake dbt SQL models to Standardized BigQuery SQL. Handles SQL compilation, Jinja macro placeholder masking, BigQuery Translation Service migration workflows, AST-based config transformations, explicit type casting, JSON extraction standardization, and deduplication. Use when migrating Snowflake dbt pipelines or models to Google Cloud BigQuery. Don't use for generic BigQuery queries or non-Snowflake SQL migrations.

- Skill: `tuyv/dbt-sf-to-bq-translator` (Agent Skill, multi-file: 4 files)
- Install (CLI): `npx skillmds@latest add tuyv/dbt-sf-to-bq-translator`
- Raw SKILL.md: https://api.skillmd.com/api/skills/tuyv/dbt-sf-to-bq-translator/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: DevOps & Infra
- Author: tuyv (https://skillmd.com/u/tuyv)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/tuyv/dbt-sf-to-bq-translator

---


# dbt Snowflake to BigQuery Translator

You are responsible for:
1.  **Dialect Translation**: Translating Snowflake dbt SQL models to Standardized Google BigQuery SQL.
2.  **Standardization & Compliance**: Enforcing Google-specific standards including copyright headers at the very top of each file, explicit type casting, standardized JSON extraction, and deduplication via `QUALIFY` with `_extracted_at`.
3.  **Workflow Integration**: Preserving dbt Jinja constructs and storing the final BigQuery-compatible models.

Follow the instructions given you under `migration_plan/[mig_prefix]/tasks.md`. You will add your progress during operation and summary at the end to the tasks file so that human supervisor can track where you are. You recover from errors by checking the tasks file.

# Prerequisites & Environment Setup

Before starting the translation, ensure your Google Cloud environment is properly configured:
1.  **Google Cloud SDK**: Install the [Google Cloud SDK](https://cloud.google.com/sdk/docs/install.md.txt) if not already installed.
2.  **Authentication**: Authenticate your CLI session:
    ```bash
    gcloud auth login
    gcloud auth application-default login
    ```
3.  **Project Configuration**: Set your active GCP project:
    ```bash
    gcloud config set project {project_id}
    ```
4.  **Billing Account**: Verify that an active Google Cloud Billing account is attached to the target project.
5.  **Enable Required APIs**: Ensure BigQuery, Migration, and Storage services are enabled:
    ```bash
    gcloud services enable bigquerymigration.googleapis.com storage.googleapis.com bigquery.googleapis.com
    ```
6.  **Region Selection**: Configure your preferred compute/BigQuery region (default recommended: `us-central1` or `us`). See [Google Cloud Locations](https://cloud.google.com/about/locations.md.txt):
    ```bash
    gcloud config set compute/region us-central1
    ```

# Steps

- **Initialization & Setup**: If you do not have a defined `mig_prefix` or if the user wants to start a new translation project, you **MUST** first ask the user for:
  1. Migration Project Name (e.g. `my_migration_project`).
  2. Input directory containing Snowflake SQL files.
  3. Output directory where BigQuery SQL files should be saved.
  4. GCS Bucket name for staging translation assets.
  5. GCP Region (e.g. `us` or `eu`).
  6. (Optional) Local path to a directory or `.zip` file containing source database metadata (such as `columns.csv` or `tables.csv`).
  Once provided, create the tasks checklist file under `migration_plan/[mig_prefix]/tasks.md` with unchecked tasks representing the migration steps.

- **Automated Execution via Bundled Scripts**:
  Execute the deterministic end-to-end migration using the bundled translation script `scripts/bulk_translate_via_gcloud.py`:
  ```bash
  python3 scripts/bulk_translate_via_gcloud.py \
    --input <input_dir> \
    --output <output_dir> \
    --bucket <gcs_bucket> \
    --location <region> \
    [--metadata <metadata_path>]
  ```
  The migration tools bundled in `scripts/` perform the following coordinated actions:
  - `scripts/bulk_translate_via_gcloud.py`: Orchestrates end-to-end bulk migration, automating pre-processing, GCS upload, BigQuery Translation Service invocation, download, post-processing, and YAML configuration copying.
  - `scripts/dbt_translator.py`: Core translation library containing the deterministic AST parser for `config(...)`, Jinja placeholder masking and restoration, JSON extraction sanitization, macro auditing, casing/join standardization, and copyright header enforcement.

- **Detailed Translation Lifecycle (Executed by Scripts)**:
  1.  **Compile to Standard SQL using Placeholders (Pre-Translation)**:
      - Read the original source dbt `.sql` files. Extract and strip the `{{ config(...) }}` header block from the top of each file.
      - Replace dbt macro calls with standard-SQL-compliant placeholder identifiers to prevent BigQuery Translation Service from throwing syntax errors:
        * Replace `{{ source('src_name', 'table_name') }}` with `_DBT_SOURCE_src_name_DBTSEP_table_name_`
        * Replace `{{ ref('model_name') }}` with `_DBT_REF_model_name_`
      - Eliminate Jinja curly braces (`{{ ... }}`) from the SQL prior to translation, ensuring the transpiler processes 100% valid Snowflake dialect SQL.
  2.  **Isolate the SQL Files**:
      - Save these pre-processed, Jinja-free files to a staging input directory ready for GCS upload.
  3.  **Pre-Process Metadata & Translate SQL via BigQuery Translation Service**:
      - If a metadata path is provided, map table entries matching discovered dbt models/sources to their placeholder names in `columns.csv` and `tables.csv`, clear catalog names to prevent namespace resolution errors, package into `metadata.zip`, and upload to GCS.
      - Upload staging SQL files to GCS:
        `gcloud storage cp <staging_input_dir>/*.sql gs://[YOUR_BUCKET]/migration_input/`
      - Create `migration_config.yaml` specifying `snowflakeDialect` as source and `bigqueryDialect` as target (with `schemaPath` pointing to `metadata.zip` if provided).
      - Trigger translation workflow:
        `gcloud bq migration-workflows create --location=<region> --config-file=migration_config.yaml --no-async`
      - Download translated GoogleSQL files from GCS:
        `gcloud storage cp gs://[YOUR_BUCKET]/migration_output/*.sql <translated_output_dir>/`
  4.  **Restore Placeholders & Re-Embed dbt Logic**:
      - Take the translated BigQuery SQL files and perform advanced post-processing:
        * **AST-Based Config Transformation**: Parse the original `{{ config(...) }}` block, stripping Snowflake-specific parameters like `copy_grants`, `transient`, and `secure`. Sanitize hooks (`pre_hook` and `post_hook`) to remove invalid Snowflake commands like `ALTER ICEBERG TABLE ... REFRESH` or `UNSET SECURE`, while preserving valid ones.
        * **Reference Resolver (Namespace Resolution)**: Scan the SQL for hardcoded Snowflake database/schema table paths in FROM and JOIN clauses, and map them back to native dbt `{{ ref(...) }}` or `{{ source(...) }}` macros by resolving against discovered project models and sources.
        * **Macro & Syntax Audit**: Scan all `{{ ... }}` Jinja expressions and log warnings for any custom/non-allowlisted database-specific macros. Also audit these blocks for Snowflake-specific syntax (e.g. `::date`, `dateadd`, `to_date`) that may have been skipped or masked, listing warning comments directly in the file.
        * **Balanced SQL Edge-Cases Sanitization**: Convert date cast suffixes (`::date` -> `CAST(... AS DATE)`), datetime cast suffixes (`::timestamp` -> `CAST(... AS TIMESTAMP)`), nested `dateadd(...)` calls, and intervals (`- interval '5 month'`) inside and outside control blocks using balanced-parentheses parsers.
        * **Copyright Header Placement**: Prepend the mandatory Google copyright header at the very top of the file above the config block.
  5.  **Write to the New BigQuery dbt File & Copy YAML Configurations**:
      - Save the newly assembled, BigQuery-compatible files preserving directory structure. Also, copy all `.yml`/`.yaml` files from the input directory to the output directory.

- Put a summary to the tasks file at the end.
- Before handing over, ask for user approval for the outcome. Apply necessary changes from the user.
- Mark your task is done in the tasks file.

# Mandates & Behavioral Rules

### 1. Mandatory File Header
Every translated file **MUST** start with the following exact header at the very top:
```sql
# Copyright 2026 Google. This software is provided as-is, without warranty or
# representation for any use or purpose. Your use of it is subject to your
# agreement with Google.
```

### 2. Standardized JSON Extraction
Never use Snowflake colon notation or BigQuery `JSON_VALUE`. Always use the following pattern:
- **Rule**: `CAST(JSON_EXTRACT_SCALAR(json_column, '$.path') AS TYPE)`
- **Mandatory Casting**:
    - IDs (primary/foreign): `AS INT64` for all system IDs (do not use `NUMERIC` for IDs).
    - Boolean Flags: `AS BOOL`
    - Strings: `AS STRING` (do not wrap in `NULLIF` unless explicitly required to handle empty/null strings in source).
    - Timestamps: `AS TIMESTAMP`

### 3. Explicit Type Safety
- **Comparisons**: Always use `CAST` on both sides of a join or filter if types are not identical. Use `AS STRING` for universal comparison safety if necessary.
- **ID Fields**: Prefer `INT64` for all system IDs (e.g., `ticket_id`, `user_id`).
- **Null Handling**:
    - For placeholder columns, always use explicit type casting: `CAST(NULL AS TYPE)`.

### 4. Prescriptive String & Date Functions
- **Truncation**: Use `LEFT(col, length)` or `SUBSTR(col, 1, length)`.
- **Search**: Use `LOWER(col) LIKE '%pattern%'` instead of `REGEXP_CONTAINS`.
- **Date Add**: Use `DATE_ADD(CAST(col AS DATETIME), INTERVAL num HOUR)`.

### 5. Mandatory Deduplication & Joins
- **Deduplication**: If the source model requires deduplication on a primary key:
    - **Rule**: Use a `row_number() over (partition by [PRIMARY_KEY] order by [TIMESTAMP] desc) as rn` column in the base CTE, and apply `qualify rn = 1` directly on that CTE.
- **Joins**: Use `LEFT JOIN` when joining to custom field or attribute tables (e.g., `exploded_array` patterns) to prevent dropping records.

### 6. Preserve Jinja Constructs
Do not alter `{{ config(...) }}`, `{{ ref(...) }}`, or `{{ source(...) }}`. Keep `{% if is_incremental() %}` blocks functional. Do not inject historical data unions or other custom macros/tables unless they are present in the source files.

# Outputs
You will output BigQuery-compatible dbt SQL models under `migration_plan/[mig_prefix]/translated_models/`. The translated files must strictly adhere to the structural pattern and dialectic formatting.
For translation examples, see: [dbt_migration_patterns.md](references/dbt_migration_patterns.md)

# Constraints
Before handing over to the root agent, first get approval from the user about the translated models. After applying user's requests, then hand over the root agent.