Amazon Q Developer
Accelerate software development with AWS-optimized generative AI coding and security.
AI-powered automated SQL translation and schema migration for petabyte-scale modernization.
The Google Data Warehouse Conversion Tool (GDWCT) is an enterprise-grade migration suite designed to automate the transition of legacy on-premise and cloud data warehouses to BigQuery. By 2026, the tool has fully integrated Gemini-based LLMs to handle complex SQL dialect translation with a 95% automated success rate. It operates as part of the Google Cloud Migration Center, providing a unified interface for planning, assessing, and executing migrations from sources like Teradata, Netezza, Oracle, and Spark. The technical architecture focuses on recursive AST (Abstract Syntax Tree) parsing and LLM-driven logic refactoring, ensuring that proprietary stored procedures, macros, and DDL are optimized for BigQuery's serverless execution engine. This eliminates the manual 'lift-and-shift' risks and significantly reduces the total cost of ownership (TCO) for data engineering teams. GDWCT serves as the foundational layer for 2026 data modernization projects, offering a seamless path for enterprises to leverage AI-native analytics and real-time streaming without the technical debt of legacy SQL architectures.
Uses LLM models specifically trained on billions of lines of cross-dialect SQL to translate complex logic.
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Heuristic mapping of legacy data types to BigQuery's modern columnar types (e.g., Decimal to Numeric).
Executes translated code in a sandbox and compares results with source system outputs.
Builds a complete syntax tree of the source system to understand hierarchical dependencies.
Multi-threaded processing of thousands of SQL files simultaneously.
Visualizes the relationship between tables, views, and downstream dependencies.
Provides a RAG (Red-Amber-Green) status for every code block based on translation confidence.
Complex BTEQ scripts and macros that do not natively run in a cloud-native SQL environment.
Registry Updated:2/7/2026
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High maintenance costs of Spark clusters for simple ETL transformations.
Data silos across providers leading to increased egress costs and fragmentation.