SSIS to Open-Source
Migration Services

SSIS Package Assessment

Our experts audit every SSIS package, data flow, control flow, and external 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 packages, data flows, control flows, and connection managers, including unused or deprecated assets
• Dependency mapping across source systems, target systems, and shared SSIS configurations
• Complexity scoring per package (simple data flow vs. heavy script-task or custom-component logic)
• Risk register flagging high-volume, high-dependency, or business-critical packages for priority handling

Open-Source Architecture Design

We design a custom target stack mapped to your workloads, determining what moves to Apache NiFi, what requires Apache Spark, and how Apache Airflow ties it all together. 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 package category
• Capacity and scaling plan based on current and projected data volumes
• Sign-off document detailing exact tool versions, configurations, and integration points

SSIS Job Re-Engineering

Our engineers rebuild every SSIS transformation from the ground up using NiFi processor chains, Spark DataFrames, and Airflow DAG logic. Compute-heavy custom C# and VB.NET script logic is recreated as Python or Scala in Spark, while lightweight control-flow scripting (file operations, variable handling, notifications) is rebuilt as Airflow Python operators or NiFi processors, whichever fits the original logic best.

• Package-by-package conversion plan with traceability back to the original SSIS object
• Code review and unit testing for every re-engineered transformation before integration testing
• Reusable component libraries for common SSIS transformation patterns (lookups, conditional splits, slowly changing dimensions)
• Inline documentation tying each Python/Scala component back to its original control flow logic

Cloud-Native Open-Source Deployment

We deploy Apache Spark on AWS EMR, GCP Dataproc, Azure HDInsight, Databricks, or on-premises Kubernetes clusters, and Apache NiFi on EMR, Dataproc, HDInsight, or Kubernetes. Apache Airflow is orchestrated via AWS MWAA, GCP Cloud Composer, or self-managed Airflow on Kubernetes. The environment is containerized, fully managed, and integrated with Snowflake, BigQuery, Redshift, and Kafka from day one.

• 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 SSIS pipelines live until every new open-source flow is validated against production data. We test rollback plans before every phase transition, ensuring no surprises and no rushed cutovers.

• Pipeline-by-pipeline cutover sequencing based on risk and business priority
• Automated data reconciliation reports comparing legacy SSIS output and new pipeline output
• 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 (via YARN/HDFS or platform IAM such as EMR roles or Databricks Unity Catalog), 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 bolted on at the end.

• 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 Optimization and Tuning

Our experts fine-tune NiFi backpressure, 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.

• 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, runbooks, architecture diagrams, and operational playbooks covering NiFi, Spark, and Airflow administration are delivered directly to your internal team so they own what we build.

• 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

Our experts provide 24x7 SLA-driven support covering incident response, pipeline modifications, cluster upgrades, and compliance reviews. Critical incident response within 30 minutes across every global time zone.

• 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