Migration Services
Move Informatica mappings, transformations, workflows, 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 processing infrastructure.
Use one managed platform for large-scale batch transformations and continuously operating streaming pipelines.
Automatically scale processing resources as data volumes and workload demand change.
Connect pipelines with BigQuery, Cloud Storage, Pub/Sub, Managed Airflow, and other Google Cloud services. Dataflow provides unified batch and streaming processing through the Apache Beam programming model.
How We Migrate
Review Informatica mappings, transformations, workflows, sessions, connections, schedules, data-quality rules, security, and workload dependencies.
Map Informatica functionality to Dataflow, Managed Airflow, BigQuery, Cloud Run, and other suitable Google Cloud services.
Rebuild mappings, transformations, workflows, reusable logic, connections, and error-handling processes for Google Cloud.
Compare source and target data, transformation results, business rules, workflow execution, failure handling, and processing performance.
Transition production pipelines and optimize worker settings, autoscaling, orchestration, 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 mappings, workflows, data sources, schedules, integrations, and security controls together.
Structured Validation. We verify data quality, transformation logic, workflow behavior, performance, and downstream results before production cutover.
Phased Migration. We reduce disruption by migrating and validating related workload groups in controlled waves.
Platform Comparison
Both platforms support enterprise data integration, but Dataflow provides a more scalable, managed approach to batch and streaming processing on Google Cloud. Dataflow can dynamically adjust worker capacity for both batch and streaming pipelines, while Managed Airflow provides managed scheduling and orchestration for workflows spanning Google Cloud, other clouds, and on-premises systems.
| Area | Informatica | Dataflow |
|---|---|---|
| Platform model | Enterprise data-integration and ETL platform | ✓ Fully managed batch and streaming data-processing service |
| Infrastructure | Deployment and infrastructure responsibilities depend on the Informatica product and hosting model | ✓ Worker infrastructure is provisioned and managed by Google Cloud |
| Development model | Visual mappings, transformations, sessions, and workflows | ✓ Apache Beam pipelines built with supported SDKs and templates |
| Processing types | Supports batch, integration, quality, and selected real-time workloads | ✓ Unified support for large-scale batch and low-latency streaming pipelines |
| Scaling | Depends on runtime configuration, capacity, and selected deployment | ✓ Horizontal autoscaling adds or removes workers based on pipeline demand |
| Orchestration | Informatica workflows, schedules, and platform services | ✓ Integrates with Managed Airflow, Workflows, and Google Cloud scheduling services |
| Transformations | Built-in transformations, reusable mappings, and custom components | ✓ Apache Beam transforms, connectors, templates, and custom pipeline logic |
| Real-time processing | Capabilities depend on the selected Informatica products and architecture | ✓ Designed for scalable streaming ingestion and transformation |
| Data integration | Supports broad enterprise databases, applications, and data platforms | ✓ Native integration with BigQuery, Pub/Sub, Cloud Storage, Spanner, and Google Cloud services |
| Autoscaling | Scaling depends on the selected Informatica environment and runtime | ✓ Managed horizontal autoscaling for batch and streaming pipelines |
| Monitoring | Informatica monitoring tools and product-specific operational consoles | ✓ Integrated job graphs, metrics, logs, diagnostics, and Cloud Monitoring |
| Portability | Mappings and workflows are tied to Informatica products and metadata | ✓ Apache Beam pipelines can run on Dataflow and supported alternative runners |
| Pricing | Subscription, licensing, capacity, infrastructure, and support costs | ✓ Usage-based pricing for processing resources consumed by each pipeline |
| Operations | Operational responsibility varies by deployment and product | ✓ Google manages service infrastructure while teams focus on pipeline logic |
FAQs
We can migrate mappings, transformations, workflows, sessions, parameters, connections, data-quality rules, schedules, error handling, and connected data workloads.
There is no complete one-click conversion from Informatica to Dataflow. Metadata can accelerate discovery and mapping, but pipelines must be redesigned and validated for the target Google Cloud services.
No. Dataflow is suitable for scalable batch and streaming processing, while some workloads may be better implemented with BigQuery SQL, Managed Airflow, Cloud Run, Dataproc, or other services.
Workflow dependencies, schedules, retries, and operational controls can be rebuilt using Managed Airflow, Workflows, native scheduling, or service-specific orchestration.
Custom logic is reviewed and rebuilt using Apache Beam, BigQuery SQL, Cloud Run, Cloud Functions, or another suitable Google Cloud service.
Yes. Their logic can be redesigned as reusable pipeline components, templates, configuration files, or shared transformation libraries.
Data-quality rules are documented, rebuilt in the appropriate target layer, and validated against known Informatica results.
We compare row counts, aggregates, rejected records, transformation outputs, business totals, workflow behavior, execution time, and downstream results.
Yes. Informatica workflows can remain active while replacement pipelines are developed, tested, and transitioned in phases.
Not necessarily. Cost depends on Informatica licensing, infrastructure, workload frequency, processing volume, operational overhead, and the target Google Cloud architecture.
Assess your Informatica mappings and workflows and build a practical roadmap for pipeline redesign, transformation migration, orchestration, validation, and production cutover.
Start Your Migration
