Databricks Brickbuilder BQ2BRICKS Migration Accelerator

BigQuery to Databricks

The accelerator blends structured parsing, rule-based rewrites, and LLM reasoning to deliver fast, accurate, governed modernization at scale.

Why migrate from
BigQuery to Databricks?

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

Cost transparency achieved by executing BigQuery SQL on Databricks

Cost transparency by executing real BigQuery workloads on Databricks.

SQL Unification & Portability to reduce platform lock-in and metric drift.

Automated, low-risk migration path replaces manual rewrites.

Built-in governance & lineage with Unity Catalog for secure scaling.

AI/BI-ready lakehouse architecture for advanced analytics & GenAI.

Core Capabilities

SQL Conversion Engine

Rule-driven translation converts BigQuery SQL, including complex functions, semantics, arrays, and nested structures into Databricks-ready code.

AI-Powered Intelligence

Pattern detection, anomaly spotting, and recommendations improve translation accuracy and reveal optimization opportunities.

Failure Mode Classification

Automated grouping of incompatible patterns, dependencies, and rewrites needed before migration.

Data Ingestion & Standardization

Centralized ingestion of SQL scripts, UDFs, and metadata normalized into a structured format for analysis and translation.

Performance Monitoring & Alerts

Real-time indicators for translation quality, failures, and mismatches with automated rule-based checks.

Technical Capabilities

Hybrid Parsing &
Translation Engine

Combination of AST parsing, rule-based rewrites, and targeted LLM reasoning for precise BigQuery → Databricks conversion.

LLM + RAG
Self-Healing Layer

Capture runtime errors, retrieval of relevant fixes, and intelligent auto-correction that improves accuracy over time.

Schema Generation &
Validation Framework

Auto-creation of schemas and tables, translated query execution on Databricks, and output validation.

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

0 x

Times faster modernization timelines with rule-driven automation and reusable migration assets.

90 %

SQL logic standardized under Delta Lake + Unity Catalog for a governed analytics foundation.

0 x

Quicker Databricks evaluation using real workloads instead of synthetic tests.

50 %

Less engineering effort with modular components for ingestion, translation, validation, and reporting.

Insights

Databricks

BQ2BRICKS Databricks-native accelerator for SQL workload migration