Migration Services
Codimite migrates analytical data, reporting workloads, and data pipelines from SQL Server, Oracle, and MySQL to Google Cloud BigQuery. Modernize your data platform and run large-scale analytics without managing traditional warehouse infrastructure.
Yes, you can migrate from On-Premises SQL to BigQuery without moving everything at once. BigQuery is a fully managed, serverless data platform designed to analyze large structured and unstructured datasets without requiring businesses to manage database infrastructure. We migrate data, schemas, queries, and analytics workflows in manageable stages while validating accuracy, performance, security, and business continuity.
Modernize Your Data Platform
Run analytical queries across terabytes or petabytes of data using BigQuery's distributed processing engine.
Move away from maintaining physical servers, database clusters, storage capacity, and data warehouse hardware.
Separate complex reporting and analytics queries from operational databases to reduce pressure on production systems.
Bring data from SQL Server, Oracle, MySQL, applications, and cloud services into a unified analytics platform.
Use change data capture and streaming pipelines to keep supported BigQuery datasets updated from source databases.
Connect governed enterprise data with BigQuery machine learning, Vertex AI, Gemini, business intelligence, and advanced analytics.
Migration Process
Review source databases, schemas, tables, data volumes, reporting queries, ETL pipelines, dependencies, security, and current infrastructure costs.
Define the BigQuery data model, datasets, access controls, partitioning, clustering, ingestion, transformation, and governance approach.
Migrate a representative dataset and validate data mappings, query compatibility, performance, and expected costs.
Transfer historical data, configure ongoing ingestion, convert analytical SQL, and rebuild suitable ETL or ELT pipelines.
Compare source and destination data, test reports and dashboards, optimize queries, and complete a controlled production transition.
Why Codimite
Codimite combines Google Cloud, data engineering, database modernization, analytics, AI, security, and DevOps expertise to deliver controlled BigQuery migrations.
Talk to a Data Migration ExpertData Migration Assessment. We evaluate your databases, analytical workloads, data volume, reporting dependencies, pipelines, and modernization priorities.
BigQuery Architecture Design. We design datasets, data models, ingestion pipelines, transformations, access controls, partitioning, clustering, and cost-management practices.
SQL and Schema Modernization. We map source data types, redesign schemas where required, and convert suitable analytical queries to GoogleSQL.
Data Pipeline Engineering. We build batch, streaming, change-data-capture, ETL, and ELT workflows for reliable data movement.
Security and Governance. We incorporate identity, permissions, encryption, data classification, logging, retention, and governance requirements.
End-to-End Support. Codimite supports assessment, architecture, migration, validation, dashboard integration, optimization, documentation, and knowledge transfer.
Comparison
| Comparison Area | Legacy SQL Environment | BigQuery Advantage |
|---|---|---|
| Primary workload | Often supports transactions, reporting, or both | ✓ Designed for large-scale analytical workloads |
| Infrastructure | Requires database servers, storage, patching, and capacity planning | ✓ Fully managed, serverless architecture |
| Scaling | Often requires hardware upgrades, clustering, or manual configuration | ✓ Separates scalable storage and analytical processing |
| Reporting impact | Heavy queries can compete with operational workloads | ✓ Moves analytics away from production databases |
| Data sources | Data may remain distributed across separate systems | ✓ Centralizes data from databases, applications, and cloud services |
| Data ingestion | Often depends on custom ETL and scheduled exports | ✓ Supports batch transfers, streaming, and change data capture |
| SQL | Uses SQL Server, Oracle, or MySQL-specific dialects | ✓ Uses GoogleSQL for BigQuery analytics |
| Data modeling | Commonly optimized for transactional applications | ✓ Supports analytical models, partitioning, and clustering |
| AI and analytics | May require separate tools and infrastructure | ✓ Integrates with machine learning, BI, Vertex AI, and Gemini |
| Best suited for | Transaction processing and existing operational systems | ✓ Enterprise analytics, reporting, data science, and AI-ready data platforms |
FAQs
Yes. Data can be transferred or replicated using appropriate Google Cloud services, partner tools, or custom pipelines. The selected method depends on the database, data volume, latency, and transformation requirements.
Not necessarily. BigQuery is designed primarily for analytics. Transactional databases can continue supporting operational applications while BigQuery handles reporting, analytics, machine learning, and historical data.
Yes. Organizations can retain their operational databases and continuously or periodically move data into BigQuery for analytics.
Many analytical queries can be converted to GoogleSQL. Some database-specific functions, procedures, indexes, and transactional logic may need to be redesigned.
A low-disruption transition may be possible using parallel environments, data replication, validation, dashboard testing, and phased cutover. The approach depends on data freshness and consistency requirements.
The timeline depends on data volume, schema complexity, query count, ETL pipelines, dashboards, security requirements, data quality, and required ingestion frequency.
Identify which datasets, reports, queries, and pipelines should move to BigQuery through a focused data-platform assessment.
Talk to a Data Migration Expert
