Informatica PowerCenter
to Open-Source Migration
Services

Informatica PowerCenter Workflow Assessment

Our experts audit every Informatica PowerCenter workflow, mapping, transformation, session configuration, and external system dependency in your environment. Each pipeline is classified by complexity, data volume, and risk level, giving you a precise migration scope, effort estimate, and risk register before any work begins.

• Inventory of all workflows, worklets, mappings, and mapplets, including unused or deprecated assets
• Dependency mapping across source systems, target systems, and shared sessions
• Complexity scoring per pipeline (simple pass-through vs. multi-transformation logic)
• Risk register flagging high-volume, high-dependency, or business-critical pipelines for priority handling

Open-Source Architecture Design

We design a custom target stack mapped to your specific workloads, determining what moves to Apache NiFi, what requires Apache Spark for heavy compute, and how Apache Airflow orchestrates it all. The architecture is fully documented and signed off before execution begins, with no ambiguity about what gets built.

• Workload-to-tool mapping (NiFi for ingestion and routing, Spark for transformation-heavy logic, Airflow for orchestration)
• Data flow diagrams covering source-to-target lineage for every pipeline category
• Capacity and scaling plan based on current and projected data volumes
• Sign-off document detailing exact tool versions, configurations, and integration points

Informatica Workflow Re-Engineering

Our engineers rebuild every Informatica PowerCenter mapping and transformation from the ground up using Apache NiFi processor chains, Apache Spark DataFrames, and Apache Airflow DAG logic. Complex expression transformations, joiner logic, and aggregator rules are recreated as Python or PySpark, optimised for distributed performance and built for long-term maintainability.

• Mapping-by-mapping conversion plan with traceability back to the original Informatica object
• Code review and unit testing for every re-engineered transformation before integration testing
• Reusable component libraries for common transformation patterns across pipelines
• Inline documentation tying each PySpark/NiFi component back to its original business logic

Cloud-Native Open-Source Deployment

We deploy Apache Spark and Apache NiFi on AWS EMR, GCP Dataproc, Azure HDInsight, or on-premises Kubernetes clusters, and orchestrate with Apache Airflow via AWS MWAA, GCP Cloud Composer, or self-managed Airflow on Kubernetes. The environment is containerised, fully managed, and integrated with Snowflake, BigQuery, Redshift, and Apache Kafka from day one, with no additional configuration required post-handover.

• Infrastructure-as-code setup (Terraform/CloudFormation) for repeatable, version-controlled deployments
• Pre-integrated connectors to your target warehouses and streaming systems
• Environment parity across dev, staging, and production
• Disaster recovery and backup configuration for the deployed stack

Phased Cutover and Parallel Run Management

Our team keeps your Informatica PowerCenter pipelines live until every new open-source flow is validated against production data. Automated reconciliation scripts confirm row counts, aggregates, and key business metrics across both systems before any cutover is executed. Rollback procedures are documented and tested before every phase transition.

• Pipeline-by-pipeline cutover sequencing based on risk and business priority
• Automated data reconciliation reports comparing legacy and new outputs
• Documented and rehearsed rollback procedures for every cutover phase
• Sign-off checkpoints with stakeholders before each pipeline goes live

Security and Compliance Configuration

We configure TLS encryption, Kerberos-based authentication for Spark and NiFi, OAuth2/SSO for NiFi and Airflow web access, role-based access control across all three layers, and full audit logging aligned to SOC 2, HIPAA, GDPR, and PCI-DSS technical control requirements. Security configuration is delivered as part of your project documentation, not addressed separately at the end of the engagement.

• End-to-end encryption in transit and at rest across all stack components
• Role-based access policies scoped per team and per environment
• Centralised audit logging mapped to relevant compliance frameworks
• Compliance-relevant configuration documentation handed over alongside architecture diagrams, not as an afterthought

Performance Optimisation and Tuning

Our engineers fine-tune Apache NiFi backpressure settings, Apache Spark execution plans, shuffle configurations, and fault-tolerant Airflow DAGs before handover. Cluster sizing, resource allocation, and monitoring dashboards are fully configured on day one, so your stack performs at production load from go-live.

• Load testing against production-equivalent data volumes before go-live
• Spark execution plan tuning to eliminate shuffle bottlenecks and skew
• Right-sized cluster and resource allocation based on actual workload profiling
• Pre-configured monitoring dashboards (e.g., Grafana and Prometheus) for ongoing visibility

Team Enablement and Training

Hands-on training, operational runbooks, architecture diagrams, and administration playbooks covering Apache NiFi, Apache Spark, and Apache Airflow are delivered directly to your internal engineering team. Your team understands and owns everything Ksolves builds before the engagement closes.

• Role-based training tracks for engineers, administrators, and analysts
• Runbooks covering common operational tasks and troubleshooting scenarios
• Knowledge-transfer sessions with recorded walkthroughs for future reference
• Internal team sign-off confirming readiness to operate the stack independently

Post-Migration Managed Support

Ksolves provides 24x7 SLA-driven support covering incident response, pipeline modifications, cluster upgrades, and compliance reviews after go-live. Critical incident response is available within 30 minutes across every global time zone. We also offer a managed operations model where our engineers extend your data platform team on an ongoing basis.

• Tiered SLA structure with a 30-minute critical incident response window
• Scheduled cluster upgrades and patching with zero unplanned downtime
• Periodic compliance and performance health checks post-go-live
• Optional managed operations model for ongoing pipeline changes and platform growth