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Sampark Softwares
Sampark Softwares
A controlled IBM Cloud to GCP migration for live vehicle telematics, 100,000+ customer records, databases, messaging layers and data lake workloads.

Case Study: Cloud Migration

Case Study / Cloud Migration

Live vehicle telematics workloads moved from IBM Cloud to GCP with controlled cutover

We migrated a connected-vehicle platform from IBM Cloud to Google Cloud Platform, covering multiple databases, data lake assets, Redis, Kafka, RabbitMQ and PostgreSQL data while commercial vehicles across the country continued emitting live telematics events.

100,000+ customer data moved Live vehicle telematics stream Outcome lower latency, overhead and cost

The real challenge was continuous telematics cutover

This was not a static database migration. Vehicles were continuously sending telematics data such as location, device events, movement signals and operational readings. The migration had to preserve live ingestion, processing, storage and application availability.

The cloud platform changed underneath a running connected-mobility system. That required dependency mapping, data reconciliation, broker validation, cache readiness, route switching and close release control.

Source estate IBM Cloud workloads, databases, data lake, Redis, Kafka, RabbitMQ and PostgreSQL data.
Target estate GCP foundation with compute, storage, database, network, IAM, logging and monitoring layers.
Cutover need Keep live vehicle telematics flowing while data, brokers and application dependencies moved.
Business Context

Connected-vehicle platforms cannot pause while cloud foundations change

The platform supported commercial vehicle operations at national scale. It depended on live telematics ingestion, data processing, application access, dashboards and downstream operational workflows.

The migration needed to improve operating stability, reduce latency, reduce infrastructure overhead and create a stronger GCP foundation without interrupting the flow of field data.

Live stream Vehicle telematics data was continuously emitted, so ingestion continuity had to be protected during cutover.
Data integrity Customer, vehicle, trip and telematics records needed validation across source and target environments.
Broker layers Kafka and RabbitMQ flow had to be reviewed for lag, queue depth, throughput and consumer behaviour.
Runtime dependency Databases, cache, data lake jobs, APIs and application services had to be sequenced carefully.
Cloud migration from IBM Cloud to Google Cloud Platform

“The cutover challenge was not moving servers. It was protecting live telematics flow while databases, cache, message brokers and data lake layers moved together.”

Cloud Migration Lead
Migration Flow

The migration had to protect live telematics while the data platform moved

The workload included multiple databases, PostgreSQL data, Redis, Kafka, RabbitMQ and data lake assets. Each layer had its own dependency, consistency requirement and cutover behaviour.

We treated the migration as a controlled data movement program, with parallel validation, service dependency mapping and cutover readiness across Sampark, Google and customer-side teams.

Foundation GCP landing zone, VPC/network layout, IAM, routing, security controls, monitoring and environment readiness.
Data movement Database, PostgreSQL and data lake migration planned with replication, validation and reconciliation checks.
Broker layers Kafka and RabbitMQ migration reviewed for consumer lag, queue depth, throughput and failover behaviour.
Cutover Release window, route switch, live telematics verification, application checks and post-cutover monitoring.

Planning a complex cloud migration with live telematics data?

We help enterprises migrate critical workloads with controlled cutover, dependency mapping, performance validation and cloud cost governance.

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Delivery Approach

Planned around dependency, validation and release discipline

The migration required more than lift-and-shift execution. We had to understand application flow, database relationships, message movement, cache behaviour, live telematics ingestion and post-migration performance baseline.

Every stage was governed through environment readiness, data validation, broker-health checks, performance comparison, migration runbooks and post-cutover monitoring.
Discovery Mapped workloads, databases, telematics paths, dependencies, network flows and release constraints.
Build Prepared GCP compute, storage, database, network, security, logging and monitoring layers.
Migrate Moved databases, data lake, Redis, Kafka, RabbitMQ and PostgreSQL data with validation checkpoints.
Optimise Reviewed latency, throughput, cloud utilisation, operational overhead and cost controls after migration.
Operational Impact

Lower latency, lower overhead and stronger cloud cost control

The migration created a stronger cloud foundation for a telematics-heavy connected mobility platform. The benefits were not limited to infrastructure hosting. The program improved application responsiveness, reduced operational overhead and created a better base for ongoing cloud optimisation.

Latency reduction Workload placement, routing, database movement and runtime tuning helped reduce avoidable latency.
Cost optimisation Cloud resource sizing, utilisation review and managed-service adoption reduced unnecessary overhead.
Release control Cutover was handled through runbooks, dependency sequencing, validation gates and rollback readiness.
Operations visibility Monitoring, logging and alerting improved post-migration observability for infrastructure and applications.

What the customer gained

  • ✓
    Critical workloads moved from IBM Cloud to Google Cloud Platform.
  • ✓
    Multiple databases, data lake, Redis, Kafka, RabbitMQ and PostgreSQL data migrated.
  • ✓
    100,000+ customer records moved with validation and cutover control.
  • ✓
    Improved foundation for live vehicle telematics ingestion and processing.
  • ✓
    Reduced operational overhead through stronger architecture and monitoring.
  • ✓
    Cost optimisation through better resource sizing and cloud governance.
Technology and Delivery Considerations

The cloud foundation had to support scale, data movement and observability

The GCP environment had to support compute, network, database, cache, messaging, storage, security and monitoring requirements together. The program also required tight coordination across Sampark, Google and customer-side teams.

Cloud foundation VPC, IAM, routing, security controls, environment separation, access governance and network readiness.
Data platform Database migration, PostgreSQL movement, data lake storage, validation checks and reconciliation reports.
Runtime services Redis, Kafka, RabbitMQ, application services, live telematics ingestion and broker-flow dependency review.
Operations layer Cloud monitoring, logging, alerting, dashboards, release tracking and cost-optimisation review.
Why It Matters

Enterprise cloud migration succeeds when cutover, data and operations move together

In telematics-heavy environments, cloud migration is not simply a hosting decision. The real challenge is protecting live vehicle data flow, application continuity, database consistency, observability and cost control at the same time. This engagement helped move a complex connected mobility platform to GCP with stronger control over performance, operations and cloud economics.

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