Migration Services
SAS to BigQuery Migration Services
Move SAS data, programs, reporting logic, and analytical workloads to BigQuery through a structured migration designed to improve scalability and governance.
Migration Services
Move SAS data, programs, reporting logic, and analytical workloads to BigQuery through a structured migration designed to improve scalability and governance.
Why BigQuery?
Run large-scale analytical workloads without provisioning or maintaining dedicated SAS servers and processing infrastructure.
Consolidate suitable SAS datasets and analytical data in a governed platform accessible across reporting, analytics, and AI workloads.
Enable analysts and data teams to work with GoogleSQL, Python notebooks, APIs, and familiar data-analysis workflows.
Connect BigQuery with Looker, Dataform, Dataflow, Vertex AI, Gemini, and the wider Google Cloud ecosystem.
How We Migrate
Review SAS datasets, libraries, programs, macros, procedures, data steps, reports, schedules, security, and system dependencies.
Define the BigQuery datasets, target data model, migration waves, transformation approach, access controls, and validation plan.
Move SAS datasets and supporting data into BigQuery or Cloud Storage using formats and transfer methods selected for the workload.
Rebuild SAS data steps, PROC SQL, macros, transformations, and reporting logic using BigQuery SQL, Python, Dataform, or supporting services.
Compare data and analytical outputs before cutover, then optimize queries, partitioning, clustering, capacity, monitoring, and cost controls.
Why Codimite?
Codimite combines Google Cloud architecture, data engineering, SQL modernization, and structured validation to support the complete migration.
Start Your MigrationSAS Workload Discovery. We identify active programs, datasets, macros, procedures, reports, dependencies, duplicated logic, and unused assets.
Purpose-Built Target Mapping. We determine which workloads belong in BigQuery and which require Dataflow, Dataproc, Dataform, Vertex AI, or another service.
Program and Logic Modernization. We translate SAS processing into maintainable SQL, Python, transformation pipelines, and governed analytical models.
Phased Business Transition. We reduce disruption through prioritized workload groups, parallel validation, controlled releases, and rollback planning.
Platform Comparison
Both platforms support enterprise analytics, but BigQuery provides a serverless, cloud-native foundation for scalable data processing and governed insights.
| Area | SAS | BigQuery |
|---|---|---|
| Platform approach | Broad analytics platform for data management, statistics, reporting, and modeling | Fully managed, serverless data platform for analytics and AI-ready workloads |
| Infrastructure | Deployment may require dedicated servers, grid environments, or managed SAS infrastructure | Google manages infrastructure, scaling, maintenance, and availability |
| Data storage | Uses SAS datasets, libraries, databases, and connected storage systems | Uses managed columnar storage with support for external and open-format data |
| Development languages | SAS language, DATA step, PROC SQL, macros, and procedures | GoogleSQL, Python, APIs, notebooks, and integrated data-development tools |
| Scaling | Depends on SAS deployment, compute capacity, grid configuration, and licensing | Storage and analytical compute scale independently according to demand |
| SQL analytics | PROC SQL and SAS analytical procedures | GoogleSQL designed for distributed, large-scale analytical processing |
| Data transformation | DATA steps, procedures, macros, and SAS data-integration tools | BigQuery SQL, Dataform, Dataflow, Dataproc, and scheduled pipelines |
| Batch processing | Commonly uses scheduled SAS jobs and batch programs | Supports scheduled queries, managed pipelines, notebooks, and workflow orchestration |
| Statistical analysis | Extensive SAS statistical and analytical procedures | SQL and Python analytics with BigQuery ML and integration into Vertex AI |
| Machine learning | SAS procedures and SAS machine-learning products | BigQuery ML for in-warehouse modeling and direct Vertex AI integration |
| Reporting | SAS reports, stored processes, and connected BI products | Native alignment with Looker and support for third-party BI platforms |
| Governance | Depends on SAS metadata, permissions, libraries, and deployment controls | IAM, dataset controls, policy tags, row-level security, lineage, and audit logging |
| Workload management | Based on SAS servers, queues, grid resources, and administrative configuration | Reservations, assignments, autoscaling, quotas, and workload isolation |
| Pricing model | Licensing, support, infrastructure, and user or capacity agreements | On-demand or capacity-based pricing with managed infrastructure |
| Operations | Teams manage SAS environments, services, libraries, jobs, and upgrades | Google manages core platform infrastructure while teams focus on data and analytics |
| Best suited for | Organizations with established SAS statistical and analytical workloads | Organizations seeking serverless analytics and deep Google Cloud integration |
FAQs
Codimite can migrate SAS datasets, libraries, programs, PROC SQL queries, macros, DATA steps, reports, schedules, permissions, and connected analytical workflows.
No. BigQuery supports scalable data storage, SQL analytics, Python workflows, and built-in machine learning. Advanced SAS statistical, modeling, or application workloads may require Vertex AI or another Google Cloud service.
SAS datasets are assessed, converted into suitable formats, and loaded into BigQuery or Cloud Storage based on performance, access, retention, and governance requirements.
Some SQL and common transformation logic can be accelerated using translation tools. Complex macros, procedures, DATA steps, and SAS-specific behavior usually require manual redesign and testing.
Yes. SAS can remain active while datasets, programs, reports, and analytical workloads are migrated, validated, and transitioned to BigQuery in controlled phases.
We compare schemas, row counts, aggregates, transformation results, analytical outputs, permissions, refresh behavior, performance, and downstream reports before production cutover.
Assess your SAS data, programs, reports, and analytical dependencies to build a practical BigQuery migration roadmap.
Start Your Migration
