Oracle to BigQuery Converter - Free Online Tool 2025

Enterprises are rapidly migrating mission-critical workloads from Oracle to Google BigQuery for cost-effective analytics, elastic scalability, and integrated cloud services. However, transforming complex Oracle schemas, procedural logic, and SQL syntax into BigQuery's architecture poses real migration challenges, especially for large-scale or legacy databases. Data type mismatches, PL/SQL incompatibilities, proprietary Oracle constructs, and performance tuning gaps can easily halt migrations or introduce subtle errors.

AI2sql streamlines Oracle to BigQuery migration by instantly converting your business logic, queries, views, and transformations into production-grade BigQuery SQL—using simple natural language or by pasting your Oracle code. No syntax rewrites, no weeks of trial and error, just accurate, vendor-specific SQL you can trust. Whether you're handling a greenfield data lake project or refactoring an enterprise data warehouse, AI2sql accelerates Oracle migrations without deep BigQuery expertise.

Oracle to BigQuery Migration Overview

Moving from Oracle to BigQuery involves more than just code translation. You must re-architect for cloud-native analytics, accommodate BigQuery's serverless, columnar processing, and adapt legacy Oracle features to Google Cloud best practices. Typical use cases include:

• Modernizing on-premise Oracle data warehouses to the cloud

• Scaling legacy reporting to support huge, analytic workloads

• Retiring expensive Oracle licenses and infrastructure

• Integrating real-time analytics with GCP services

The AI2sql platform is designed for enterprise SQL conversion, making multi-terabyte schema and ETL migrations effortless—no matter how complex the source Oracle environment.

Key Syntax Differences: Oracle vs BigQuery

Oracle PL/SQL and BigQuery Standard SQL share a foundation, but differ sharply:

Data Type Mapping Guide

Correct data type translation is critical for query compatibility and data integrity:

Common Conversion Challenges

• PL/SQL logic: BigQuery does not support Oracle's procedural code. Refactor with JavaScript UDFs or BigQuery scripting.

• Sequences & Triggers: Replace with BigQuery-generated columns or use UUIDs for surrogate keys.

• Row-level functions: Adjust syntax around ROWID, ROWNUM, and analytic functions.

• Packages & procedures: Migrate core logic to microservices or external orchestration (e.g., Cloud Functions).

• Partitioning/indexing: BigQuery manages partitions and clustering differently; no direct index creation.

• Security: Adapt Oracle roles/privileges to IAM-based controls in BigQuery.

Step-by-Step Migration Process

1. Assess compatibility: Catalog all Oracle schemas, stored logic, and third-party dependencies.

2. Extract schema/data: Use tools like Data Pump or custom scripts for schema and data extraction.

3. Convert schema: Use AI2sql to generate BigQuery DDL from your Oracle CREATE TABLE scripts.

4. Migrate data: Stage in Google Cloud Storage, then bulk-load into BigQuery.

5. Convert queries/procedures: Bulk-convert SELECTs, views, and reporting logic with AI2sql. Refactor unsupported PL/SQL.

6. Validation/testing: Compare record counts, sums, analytics between source and target.

7. Optimize and tune: Apply BigQuery-specific cost and performance optimizations.

AI2sql: Generate BigQuery Queries from Natural Language

Skip manual translation. With AI2sql, simply describe your Oracle query requirements or paste your Oracle SQL, then select BigQuery as the target. Instantly get optimized, accurate BigQuery SQL ready for execution. Trusted by 50,000+ developers and supporting 15+ databases, AI2sql ensures:

• Error-free SQL conversion between Oracle and BigQuery

• No manual syntax rewrites or guesswork

• Automatic mapping of data types, functions, and query structures

• Full support for SELECTs, JOINs, aggregations, and more

• Enterprise-grade reliability with cloud-specific optimizations

Try AI2sql BigQuery Generator to accelerate your Oracle to BigQuery migration.

Performance Considerations

• Partitioning: Use BigQuery's table partitioning for date-based analytics instead of traditional Oracle range partitions.

• Clustering: Optimize frequent filter columns with clustering keys.

• Denormalization: BigQuery performs best with flattened, denormalized data models rather than Oracle-style 3NF schemas.

• Query pricing: Optimize SQL logic to scan less data (project only needed columns, avoid SELECT *, and filter early).

Schema Migration Best Practices

• Map constraints and PKs via table decorators or ETL assertions, since BigQuery doesn't enforce them.

• Document all manual refactoring (e.g., for triggers, procedures) for traceability.

• Use scripts generated by AI2sql for audit-ready, repeatable migrations.

Testing and Validation

• Compare row counts, checksums, and analytics on source and target

• Run automated regression queries on BigQuery, using AI2sql to convert test scripts

• Create test harnesses for any rewritten logic (UDFs, scripts)

Rollback Strategies

• Snapshot Oracle and BigQuery datasets prior to cutover

• Use controlled dual-write or replication for staged switchover

• Validate all dependent apps and BI tools before production switch

Conversion Examples: Oracle SQL to BigQuery SQL

Need to convert complex scripts? Skip manual conversion - Generate BigQuery queries instantly with AI2sql using natural language.

Cloud-Specific Features and Syntax

• Transition from Oracle's schema constraints to BigQuery's flexible schema model

• Leverage BigQuery's support for ARRAY and STRUCT fields for semi-structured data

• Utilize BigQuery's integrated machine learning, GIS, and analytics functions

Cost Optimization Tips

• Partition and cluster tables to minimize query data scanned

• Limit use of SELECT *; project only necessary columns

• Routine cost analysis with INFORMATION_SCHEMA and billing exports

• Use flat-rate or per-query billing based on workload size

Security and Compliance

• Map Oracle roles/privileges to Google Cloud IAM roles

• Enable audit logging and data access monitoring within BigQuery

• Encrypt datasets at rest and in transit using GCP-native features

• Use authorized views and column-level security for sensitive data

Troubleshooting Common Conversion Errors

• Syntax errors: Carefully review reserved words and function renames (e.g., NVL → IFNULL)

• Type mismatches: Ensure all NUMBER mappings specify correct precision for NUMERIC/BIGNUMERIC

• Unsupported PL/SQL: Extract logic into BigQuery scripting or external orchestration

• Data import failures: Validate UTF-8 encoding, column order, and null value handling

• Performance issues: Partition/cluster tables, avoid repeated subqueries, limit data scans

AI2sql: Your Oracle to BigQuery Migration Assistant

Database migration from Oracle to BigQuery does not have to be risky or drawn out. With AI2sql, you can:

• Convert complex Oracle queries, views, and DDL to BigQuery syntax with one click

• Reduce migration errors and accelerate project timelines

• Rely on enterprise-grade accuracy and security

• Stay focused on business insights—let AI2sql automate the low-level SQL work

Ready to modernize your analytics with Google BigQuery?
Try AI2sql Free – Generate BigQuery Queries from Plain English

Explore more migration resources:
• Try AI2sql BigQuery Generator
• BigQuery SQL Tutorial

• Oracle Migration Tools