Migration Services
SSIS to Dataflow Migration Services
Move SSIS packages, transformations, and data pipelines to Dataflow through a structured migration designed to improve scalability, maintainability, and operational visibility.
Migration Services
Move SSIS packages, transformations, and data pipelines to Dataflow through a structured migration designed to improve scalability, maintainability, and operational visibility.
Why Dataflow?
Run data pipelines without provisioning or maintaining dedicated ETL servers and worker infrastructure.
Use one managed platform for scheduled batch processing and continuously operating streaming pipelines.
Scale processing resources as data volume and workload demand change.
Connect pipelines with BigQuery, Cloud Storage, Pub/Sub, Cloud Composer, and other Google Cloud services.
How We Migrate
Review SSIS packages, data flows, control flows, Script Tasks, custom components, connections, schedules, security, and workload dependencies.
Map SSIS functionality to Dataflow, Cloud Composer, BigQuery, Cloud Run, and other suitable Google Cloud services.
Rebuild transformations, data movement, orchestration, custom logic, connections, and error-handling processes for Google Cloud.
Compare source and target data, transformation results, business rules, pipeline execution, failure handling, and processing performance.
Transition production pipelines and optimize worker settings, autoscaling, scheduling, monitoring, retry logic, and cost controls.
Why Codimite?
Codimite combines Google Cloud expertise, data engineering, ETL modernization, and structured validation to support the complete migration journey.
Start Your MigrationGoogle Cloud Expertise. We build the target environment using Dataflow and the wider Google Cloud data and analytics ecosystem.
Workload-Led Planning. We migrate related packages, transformations, data sources, schedules, integrations, and security controls together.
Structured Validation. We verify data quality, transformation logic, pipeline behavior, performance, and downstream results before production cutover.
Phased Migration. We reduce disruption by migrating and validating related package groups in controlled waves.
Platform Comparison
Both technologies support data integration, but Dataflow provides a more scalable, managed approach to batch and streaming data processing on Google Cloud.
| Area | SSIS | Dataflow |
|---|---|---|
| Platform model | Microsoft ETL and data-integration platform | ✓ Fully managed batch and streaming data-processing service |
| Infrastructure | Requires SQL Server Integration Services runtime and supporting infrastructure | ✓ Worker infrastructure is provisioned and managed by Google Cloud |
| Development model | Visual packages containing data flows, control flows, and tasks | ✓ Apache Beam pipelines developed using supported programming SDKs |
| Processing types | Primarily scheduled batch ETL workloads | ✓ Unified support for batch and streaming pipelines |
| Scaling | Depends on server capacity, package design, and deployment architecture | ✓ Horizontal autoscaling adjusts workers according to workload demand |
| Orchestration | Control flows, SQL Server Agent, and external scheduling tools | ✓ Integrates with Cloud Composer, Workflows, and Google Cloud scheduling services |
| Transformations | Built-in components, Script Tasks, and third-party connectors | ✓ Apache Beam transforms, connectors, templates, and custom processing logic |
| Real-time processing | Limited compared with dedicated streaming platforms | ✓ Designed for scalable, low-latency streaming data processing |
| Data integration | Strong alignment with SQL Server and Microsoft data tools | ✓ Native integration with BigQuery, Pub/Sub, Cloud Storage, Spanner, and other services |
| Autoscaling | Scaling commonly requires infrastructure or package-level planning | ✓ Automatically adds or removes workers based on pipeline demand |
| Monitoring | SSIS logging, SQL Server tools, and external monitoring solutions | ✓ Integrated job graphs, metrics, logs, autoscaling visibility, and Cloud Monitoring |
| Portability | Packages are tied to the SSIS runtime and package model | ✓ Apache Beam pipelines can run on Dataflow and supported alternative runners |
| Pricing | SQL Server licensing, infrastructure, and operational costs | ✓ Usage-based pricing for processing resources consumed by each job |
| Operations | Teams manage runtime infrastructure, deployment, patching, and capacity | ✓ Google manages service infrastructure while teams manage pipeline logic |
FAQs
Codimite can migrate SSIS data flows, transformations, control flows, Script Tasks, schedules, connections, business rules, error-handling logic, and connected data workflows.
There is no complete one-click conversion from SSIS to Dataflow. Existing package metadata can support discovery, but pipelines usually need to be redesigned using Apache Beam and suitable Google Cloud services.
No. Dataflow is best suited to scalable batch and streaming processing. Some SSIS workloads may be better rebuilt using BigQuery SQL, Cloud Composer, Workflows, Cloud Run, or managed data-transfer services.
Control flows can be rebuilt using Cloud Composer, Workflows, or Google Cloud scheduling services. Script Tasks and custom components may be rewritten using Apache Beam, BigQuery SQL, Cloud Run, or Cloud Functions.
Yes. Existing SSIS packages can remain active while replacement Dataflow pipelines are developed, tested, validated, and transitioned in controlled phases.
We compare row counts, aggregates, rejected records, transformation outputs, business totals, execution times, failure handling, and downstream results before production cutover.
Assess your SSIS packages and build a practical roadmap for pipeline redesign, transformation migration, orchestration, validation, and production cutover.
Start Your Migration
