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name: bigquery-migration-sql-translator metadata: version: "1.0.2" category: BigDataAndAnalytics publisher: google description: >- Translates SQL queries, DDL schemas, and stored procedures from legacy database dialects (Teradata, Oracle, Redshift, Snowflake, SparkSQL, HiveQL) to BigQuery GoogleSQL using the BigQuery Migration Service (BQMS) MCP server. Use when translating legacy SQL queries, migrating schemas or metadata zip archives, or configuring BigQuery Migration Service (BQMS) YAML translation settings. Don't use for executing BigQuery queries or managing datasets and tables (use bigquery-basics), diagnosing runtime job errors (use bigquery-troubleshooting), or tuning slot reservations (use bigquery-optimization).

BigQuery Migration Service (BQMS) SQL Translator

The BigQuery Migration Service (BQMS) translates legacy database SQL scripts, queries, and schemas into GoogleSQL. This skill coordinates translation workflows, Cloud Storage staging, compiler error triage, and YAML configuration.

Usage Attribution

MUST READ: Before executing any gcloud, REST API, or MCP command, you MUST read and follow the instructions in Usage Attribution.

Before you begin

Before translating queries or schemas, verify the following prerequisites:

1. Google Cloud SDK & Project Setup: Ensure the Google Cloud SDK is installed, verify that your Google Cloud project has an active Cloud Billing account attached, and select the target project:

   gcloud config set project {project_id}
   

2. Enable the API: Enable the BigQuery Migration API (bigquerymigration.googleapis.com) in your Google Cloud project:

   gcloud services enable bigquerymigration.googleapis.com
   

3. Required IAM Roles: Verify that your identity or service account has:

   • roles/mcp.toolUser (MCP Tool User)
   • roles/bigquerymigration.editor (Migration Workflow Editor)
   • For batch translation, roles/storage.objectViewer and roles/storage.objectCreator (or roles/storage.objectAdmin) on the Cloud Storage buckets that hold the input and output files.

4. MCP Server Connection: If BigQuery Migration Service (BQMS) tools (e.g. translate_query, translate_batch_queries) are not present in your active tool list, configure the remote BigQuery Migration Service (BQMS) MCP server. For setup instructions across clients (Claude, Cursor, Gemini CLI, VS Code), see mcp_config.md.

5. Cost & Cloud Storage Cleanup: Review Google Cloud Pricing for Cloud Storage and BigQuery usage details. After completing a batch migration, clean up temporary staged input and output folders in Cloud Storage:

   gcloud storage rm -r gs://{bucket}/{folder}
   

6. Translation Tracking: For batch translations, and for queries that need more than one translation iteration, keep a translation tracker (e.g. translation_tracker.md) to document goals, planned steps, and progress. After each translation, record one row with this template:

   Iteration | Status | Total Errors | Correctness Errors | Completeness Errors | Compatibility Errors | Key Actions / Transformations --------- | ------ | ------------ | ------------------ | ------------------- | -------------------- | -----------------------------

   A single query that translates cleanly on the first attempt needs no tracker: report its translation ID and logs in your response.

Workflow Selection

Choose the workflow based on your migration scope:

• Interactive Translation (Single Query): Best for ad-hoc queries, exploratory testing, dialect syntax comparison, or quick error debugging.
• Batch Translation (Multiple Files & Cloud Storage): Use for more than one file or a directory, including SQL embedded in application code (extract it into .sql files first). For multi-file Python codebases containing embedded SQL, use python_embedded_sql_migrator_template.py to extract queries into staged files and re-inject translated SQL. Batch translation is faster and cheaper than translating files one by one, and it resolves cross-file dependencies, so it usually gives better results. Run translate_batch_queries on the staged files first; use translate_query only afterwards if needed to debug a specific statement from translation_report.csv.

1. Interactive Translation Workflow

Use the interactive workflow to translate a single query string, inspect compiler logs, and iteratively resolve issues.

Steps

1. Submit Query (translate_query): Call translate_query with projectNumber, location, inputQuery, sourceDialect, and targetDialect (BigQuery). Include optional parameters:

   • metadataFilePath: Cloud Storage URI to a metadata zip file (gs://.../metadata.zip) for schema resolution.
   • translationConfigs: Inline YAML configurations (e.g. object renaming, schema redirection, optimizer rules). Show the YAML you used in your response so the user can reuse it (e.g. in batch translation).

2. Poll Status (get_translation): If the returned translation_state is RUNNING or PENDING, poll get_translation using the translation ID, projectNumber, and location until translation.state is SUCCEEDED or FAILED, at the interval given in the BigQuery Migration Service (BQMS) MCP tool descriptions. While the job runs, do work that doesn't need the result (e.g. read the dialect gotchas reference).

3. Inspect Output & Logs:

   • Examine translated_query for the converted GoogleSQL.
   • Review each entry in translation_logs: inspect severity (ERROR, WARNING), category (e.g. RelationNotFound), and effect (CORRECTNESS, COMPLETENESS, COMPATIBILITY).
   • Check the SQL itself, not only the logs: BigQuery Migration Service (BQMS) emits ERROR_TYPE(...) for types it cannot map, sometimes with no log entry. Treat any ERROR_TYPE or inline error comment as an error, and when a BigQuery project is available, dry-run the result (bq query --dry_run --use_legacy_sql=false). A Not found or must be qualified with a dataset error still means the SQL is syntactically valid; the referenced objects just don't exist in BigQuery yet. Say so and move on, even if the error suggests a fix: don't stub tables or change the SQL or bq flags to get past it.
   • A NoSuchFunction error means BigQuery Migration Service (BQMS) copied that call unchanged, sometimes with the function name in backticks. Treat it as untranslated even if BigQuery has a function with that name: a dry run that stops at missing tables doesn't check it.
   • If the source dialect has a gotchas reference (see Dialect-Specific Gotchas), read it and check the output for the patterns it lists. Some semantic differences produce no log entry and still pass a dry run.

4. Iterate Until Clean: If the logs contain errors or warnings, the translated SQL contains ERROR_TYPE or inline error comments, the dry run fails, or the output matches a dialect gotcha, resolve the issues as described in Resolving Translation Issues and re-translate.

5. Explain & Compare Differences (explain_translation): When requested to explain a translation, compare syntax differences, or understand how specific query logic was converted, call explain_translation with translation (the translation ID), projectNumber, and location. This returns a structured explanation of the query's purpose and a detailed breakdown of syntax, function, and behavioral differences between the source query and the translated GoogleSQL. The explanation doesn't validate the output, so check the translation as in step 3 rather than relying on it.

2. Batch Translation Workflow

Batch translation handles multiple files staged in Cloud Storage. It resolves cross-file dependencies and produces a consolidated translation summary report (translation_report.csv).

Steps

1. Stage Input Files to Cloud Storage: Upload the raw SQL files, configuration files (*.config.yaml), and any metadata file from the local workspace to a Cloud Storage input folder:

   gcloud storage cp input/*.sql gs://my-bucket/migration/input/
   gcloud storage cp *.config.yaml gs://my-bucket/migration/input/
   

   Note: Do not pass configuration file paths in tool parameters if those files are already located inside sourceBaseUri.

   Use new, empty Cloud Storage input and output folders for each run (e.g. with a timestamp suffix). Stage only files from the local workspace or generated in this run. Never reuse or copy inputs, DDL, or outputs that are already in a bucket from earlier runs: they may come from different inputs or settings.

2. Initiate Batch Translation:

   • Translating SQL Queries (translate_batch_queries): Call translate_batch_queries with projectNumber, location, sourceDialect, targetDialect, sourceBaseUri, and targetBaseUri.
   • Translating Metadata Zip Files (translate_metadata): To aid batch translation by generating target BigQuery DDL schema definitions from an extracted database metadata zip file (metadata.zip from Teradata, Oracle, etc.), call translate_metadata specifying sourceBaseUri (Cloud Storage URI to metadata.zip) and targetBaseUri (destination Cloud Storage folder).

3. Poll Completion (fetch_batch_translation): Poll using the returned workflow ID until translation.state is SUCCEEDED or FAILED, at the interval given in the BigQuery Migration Service (BQMS) MCP tool descriptions.

4. Retrieve & Inspect Outputs: Download the generated files from translation.target_base_uri:

   gcloud storage cp -r gs://my-bucket/migration/output/ ./local_output/
   

   Locate and inspect report/translation_report.csv to review all warnings, errors, and line-level diagnostics. Then scan the generated .sql files for ERROR_TYPE(...) and inline error comments, which the report can miss, and dry-run the generated SQL when a BigQuery project is available (handle Not found errors as in the interactive workflow). Check the output for the patterns in the source dialect's gotchas reference, if one exists. Resolve or clearly report every remaining issue; do not describe the output as clean until this check passes.

5. Iterate Until Clean: Resolve the issues found in step 4 as described in Resolving Translation Issues, and re-run from step 1 with the changed inputs.

6. Post-processing (Final Step Only): Only after resolving every compiler error that re-translation can fix, execute any application-level post-processing (e.g. AST re-injection) to restore host source files. effect_correctness.md lists the errors that may remain when re-injecting embedded SQL.

Resolving Translation Issues

Both workflows resolve issues the same way:

• Identify the Effect (CORRECTNESS, COMPLETENESS, COMPATIBILITY) of each issue, and resolve them in order of compiler phase dependencies: CORRECTNESS (parse errors) first, then COMPLETENESS (missing objects), then COMPATIBILITY (unsupported featu