As analytics, AI, and BI needs intensify, every industry is rethinking its data foundation. Teams that have relied on BigQuery, a fully managed, serverless data warehouse, for years, are now encountering challenges around scalability, governance, cost predictability, and flexibility. Many organizations we work with choose to migrate from BigQuery and other cloud analytics platforms to the Databricks Lakehouse for its end-to-end capabilities, such as governed data ingestion, GenAI, and real-time BI. But the biggest barrier often sits right at the beginning: SQL server data migration from BigQuery to Databricks.
At Syren, we built BQ2Bricks, our Databricks-native BigQuery to Databricks Migration Accelerator, to solve exactly this problem. It is an end-to-end framework that automates SQL conversion, dependency analysis, validation, and optimization, dramatically reducing the effort and risk associated with SQL migration programs. It gives enterprises a repeatable path to modernize faster, standardize logic, and compare BigQuery and Databricks performance using their actual workloads.
Why Enterprises Migrate to Databricks Lakehouse
Across industries like health and life science, finance, retail, manufacturing, and distribution, we see a few consistent drivers behind the shift from BigQuery to Databricks:
- Scaling analytics into AI: Teams want to bring SQL analytics, ML, streaming, dashboards, and AI into one governed platform.
- Cost-performance pressure: As workloads expand, BigQuery costs become unpredictable, and performance on large analytical queries can suffer.
- Fragmentation and logic drift: Multiple teams modify SQL differently, causing metric inconsistencies, slower reporting, and duplicated effort.
- Governance and lineage: Enterprises need unified governance with end-to-end lineage, auditing, and permissions, which Unity Catalog enables natively.
As ambitions grow, organizations quickly realize that SQL migration is not just a technical rewrite; it is a modernization catalyst that affects reporting, AI strategies, FinOps, operational processes, and governance.
Why BigQuery Migrations Are Challenging
Based on our experience, the migration bottlenecks usually appear long before SQL is executed on Databricks. Most enterprises struggle with:
1. Lack of visibility into migration cost and complexity
Teams often cannot evaluate how much SQL must be rewritten, what patterns are incompatible, or where dialect differences may cause metric drift.
2. Heavy manual logic and inconsistent metrics
Many organizations rely on decentralized SQL logic created by different teams. Migrating this manually results in inconsistent metrics and delays in decision-making.
3. Rising compute costs and performance unpredictability
BigQuery workloads often grow in ways that create cost overruns and performance bottlenecks, pushing teams toward Databricks for better control.
4.. Limited governance for cross-domain scaling
Without unified governance, onboarding new analytics or AI use cases becomes increasingly difficult.
These challenges are what shaped the design of Syren’s BQ2Bricks accelerator.
BQ2Bricks | A Databricks-Native Accelerator for SQL Data Migration
BQ2Bricks streamlines and standardizes how enterprises migrate SQL workloads from BigQuery to Databricks by automating the most complex, error-prone parts of the process. It provides a structured, governed, and AI-assisted approach to modernization.
Core Capabilities
- Data Ingestion & Standardization: We ingest BigQuery SQL scripts, UDFs, metadata, and DataProc jobs into a centralized model, normalizing everything into a consistent format for automated analysis.
- SQL Conversion Engine: A rule-driven transformation layer converts BigQuery SQL into Databricks-ready code. This includes functions, syntax, arrays, structs, nested queries, and advanced patterns with extensible mappings.
- AI-Powered Translation Intelligence: BQ2Bricks blends rule-based SQLGlot parsing with LLM reasoning for enhanced accuracy. An LLM + RAG self-healing loop captures errors, retrieves patterns, and auto-corrects SQL to improve translation quality continuously.
- Schema Generation & Validation Framework: The accelerator automatically infers schema requirements, creates necessary tables, runs translated queries on Databricks SQL Warehouse, and validates results end-to-end. If validation fails, the self-healing loop retries until correctness is achieved.
- Governance with Unity Catalog: Every translated query, log, migration artifact, and validation output is governed under Unity Catalog, ensuring secure and compliant data handling across teams.
- Performance Monitoring & Rule-Based Alerts: Near real-time tracking surfaces translation quality, incompatible patterns, exceptions, and validation failures for complete migration transparency.
BigQuery to Databricks Migration | Use Cases Across Industries
Finance
Financial analytics teams often want to test Databricks for faster insights, governed BI, and AI co-pilot adoption. BQ2Bricks accelerates POCs by converting representative BigQuery workloads in days instead of weeks, allowing quick comparison of performance, query cost, and SLA reliability.
Manufacturing & Distribution
Many organizations move away from BigQuery due to rising compute costs and uneven performance during peak analytical periods. BQ2Bricks enables rapid conversion + validation so they can evaluate Databricks cost and performance using their actual workloads.
Retail
Retail enterprises often suffer from “shadow ETL”, isolated SQL logic embedded across teams with no central governance. BQ2Bricks unifies and standardizes SQL under Delta Lake + Unity Catalog, enabling governed, AI-ready reporting and faster rollout of data products.
Customer Story
A large U.S. client operating in retail had relied heavily on BigQuery for years but struggled with unpredictable compute costs, inconsistent performance on large analytical workloads, and difficulty onboarding AI and BI. Their SQL estate had grown organically, hundreds of tables, deeply nested queries, and domain-specific transformations that created platform lock-in.
By deploying BQ2Bricks, the customer automated SQL ingestion, standardized logic, and translated queries into Databricks-ready code without months of manual rewrites. They quickly validated Databricks' performance, cost efficiencies, and governance advantages. Key outcomes included:
- 90% automated SQL conversion, dramatically reducing manual effort
- 50% detection of hidden incompatibilities, enabling proactive optimization
- 60% reduction in engineering effort through modular automation
- 20% productivity gains from immediate access to AI/BI capabilities
- 70% faster modernization timelines powered by reusable accelerators and rule-based workflows
This helped the organization adopt the Databricks Lakehouse significantly faster, with full confidence in accuracy, governance, and long-term scalability.
Why Industry Leaders Choose BQ2Bricks for SQL Migration
- It consolidates scattered SQL logic into a unified, governed foundation.
- It accelerates platform adoption by validating Databricks' performance using real workloads.
- It improves query accuracy with AI-assisted translation.
- It provides end-to-end validation for absolute trust before migration.
- It unlocks governed, AI-ready analytics at scale with Unity Catalog
If your organization is planning a SQL modernization initiative, explore how our BigQuery to Databricks Migration Accelerator can shorten migration timelines and unlock the full value of the Databricks Lakehouse.
