BQ2BRICKS
Databricks-native accelerator for SQL workload migration
BigQuery to Databricks
The accelerator blends structured parsing, rule-based rewrites, and LLM reasoning to deliver fast, accurate, governed modernization at scale.Â
Why migrate from
BigQuery to Databricks?Â
Enterprises on BigQuery struggle with scattered SQL, isolated data marts, and inconsistent insights.
Manual pipelines create duplicated logic and metric drift, while rising volumes drive unpredictable costs and performance issues.
Without unified governance or a clear roadmap, teams can’t scale analytics or predict migration effort, cost, or risk.
BQ2BRICKS eliminates these blockers with a standardized, automated pathway to Databricks.
How BQ2Bricks Solves Migration Challenges
SQL Unification & Portability to reduce platform lock-in and metric drift.
Automated, low-risk migration path replaces manual rewrites.
Core Capabilities
SQL Conversion EngineÂ
Rule-driven translation converts BigQuery SQL, including complex functions, semantics, arrays, and nested structures into Databricks-ready code.
AI-Powered IntelligenceÂ
Pattern detection, anomaly spotting, and recommendations improve translation accuracy and reveal optimization opportunities.Â
Failure Mode ClassificationÂ
Automated grouping of incompatible patterns, dependencies, and rewrites needed before migration.Â
Data Ingestion & StandardizationÂ
Centralized ingestion of SQL scripts, UDFs, and metadata normalized into a structured format for analysis and translation.
Data Governance
& Security
Unity Catalog-integrated governance for SQL assets, lineage, logs, and translated code.
Performance Monitoring & AlertsÂ
Real-time indicators for translation quality, failures, and mismatches with automated rule-based checks.Â
Technical Capabilities
Hybrid Parsing &
Translation Engine
Combination of AST parsing, rule-based rewrites, and targeted LLM reasoning for precise BigQuery → Databricks conversion.Â
LLM + RAG
Self-Healing LayerÂ
Capture runtime errors, retrieval of relevant fixes, and intelligent auto-correction that improves accuracy over time.Â
Schema Generation &
Validation Framework
Auto-creation of schemas and tables, translated query execution on Databricks, and output validation.Â
Why Syren + Databricks?
A Unified SQL Foundation
Eliminate silos and metric drift by consolidating scattered BigQuery logic into a standardized Databricks format.
Faster Databricks Adoption
Quick validation of Databricks performance using real workloads, shrinking migration and POC timelines.
Higher Accuracy Through Intelligent Translation
AI-assisted rewrites reduce manual effort, close dialect gaps, and deliver consistent, reliable SQL.
End-to-End Validation Confidence
Side-by-side execution on BigQuery and Databricks ensures correctness before production migration.
Governed, AI-Ready Analytics
Unity Catalog integration brings secure access, lineage, auditing — and unlocks GenAI, vector search, and Databricks-native BI.
Faster Time-to-Value
Remove conversion bottlenecks so enterprises can move from exploration to production swiftly and confidently.
Value Delivered by BQ2Bricks
Times faster modernization timelines with rule-driven automation and reusable migration assets.
SQL logic standardized under Delta Lake + Unity Catalog for a governed analytics foundation.
Quicker Databricks evaluation using real workloads instead of synthetic tests.
Less engineering effort with modular components for ingestion, translation, validation, and reporting.