Cloud Migration and Data Modernization for a Digital Conversation Platform

The client is a conversation media company specializing in multilingual keyboards, personalized stickers, GIFs, and contextual content platforms for smartphone users and brands. Operating at large scale, the company leverages artificial intelligence and real-time analytics to enhance user engagement and deliver personalized digital experiences across diverse markets. With rapidly growing data volumes and evolving analytics requirements, the organization relies heavily on its data ingestion and analytics platforms to power insights, personalization engines, and business intelligence reporting. As the platform scaled, it required a more resilient, secure, and cost-efficient cloud infrastructure to support expanding analytics workloads and ensure long-term operational agility.

As the digital conversation media platform’s data footprint and analytics requirements expanded, the existing GCP-based analytics environment faced significant scalability, governance, and operational challenges.

• All infrastructure was deployed on public endpoints, resulting in insecure access, limited scaling, and self-managed, resource-intensive operations. Data processing workloads relied on public Dataproc clusters, and cost governance was largely manual, making implementations and maintenance hectic. The organization required a structured migration strategy to transition its analytics workloads to AWS while minimizing disruption to ongoing business operations.

• Key challenges included securely migrating approximately 10 TB of analytics data while maintaining data integrity, schema consistency, and validation across staging and production environments.

• Additionally, ingestion and processing pipelines needed to support containerized microservices, Spark-based big data jobs, and real-time orchestration, demanding a highly scalable, resilient, and secure architecture.

From a governance and security perspective, digital conversation media platform required a well-architected multi-account AWS setup with centralized billing, role-based access control, encryption, logging, and compliance guardrails. Cost visibility and financial governance were also critical to ensure controlled cloud spend as workloads scaled.

Overall, the organization needed a modern, secure, and automated analytics platform that could support high data volumes, enable faster insights, and provide long-term operational efficiency within a governed AWS environment.

To address digital conversation media platform’s scalability, governance, and migration challenges, Comprinno designed and implemented a secure, scalable, and fully governed AWS-based analytics platform. The solution included establishing a multi-account AWS landing zone with centralized governance, deploying a containerized ingestion layer on Amazon EKS, building a distributed analytics engine using Amazon EMR and Spark, and implementing orchestration through AWS Managed Workflows for Apache Airflow (MWAA). A secure one-time migration of approximately 2 TB of data from GCP to Amazon S3 was executed using AWS DataSync with checksum and object validation to ensure data integrity. The analytics ecosystem was further strengthened with AWS Glue for metadata cataloging, Amazon Redshift Serverless and Athena for querying, and Amazon QuickSight for business intelligence dashboards. Infrastructure provisioning was standardized using Terraform to ensure repeatability across Development, Staging, and Production environments, while comprehensive monitoring, logging, encryption, and guardrails were implemented to align with AWS Well-Architected best practices.

Key Actions

Multi-Account AWS Governance Implementation :

• Implemented a multi-account AWS setup using Organizations and Control Tower, separating Dev, Staging, Prod, and Audit with centralized billing and governance.

• Applied Service Control Policies (SCPs) to restrict services, enforce region controls, and mandate tagging for cost and compliance visibility.

• Enabled organization-wide CloudTrail and AWS Config, centralizing logs in a dedicated audit account for continuous monitoring.

• Enforced role-based access via IAM Identity Center (SSO) with MFA, ensuring least-privilege security across accounts.

Secure and Highly Available Network Architecture :

• Designed environment-specific VPCs across multiple Availability Zones to ensure high availability and fault tolerance.

• Segregated public and private subnets, exposing only load balancers while keeping EKS, EMR, and databases private.

• Implemented an air-gapped architecture by configuring VPC endpoints and private links to ensure secure, intranet connectivity to AWS services such as S3, Secrets managers, AWS Redshift etc without exposing traffic to the public internet.

• Implemented tightly scoped security groups and IAM roles to control service communication and minimize attack surface.

• Created custom AMIs for compliances and leveraged AWS CodeArtifact with bootstrap scripts to ensure system changes originate from verified upstream sources and maintain secure, consistent deployments

Scalable Containerized Ingestion on Amazon EKS :

• Deployed containerized ingestion microservices on Amazon EKS using managed node groups for scalability and operational efficiency.

• Implemented NGINX Ingress Controller with Network Load Balancer (NLB) to manage and securely expose external traffic to Kubernetes services.

• Configured Horizontal Pod Autoscaler (HPA) alongside Cluster Autoscaler to dynamically scale pods and nodes based on workload demand and traffic spikes.

• Enabled enhanced monitoring using CloudWatch Container Insights and configured alerts, set up Amazon Managed Service for Prometheus with exporters to collect metrics, and integrated them with Grafana, creating dashboards for visibility and reporting.

• Centralized logs and metrics in CloudWatch, storing encrypted raw data in Amazon S3 with versioning and lifecycle policies, following a medallion architecture for staged data processing

Distributed Data Processing on Amazon EMR :

• Developed Spark based processing pipelines on Amazon EMR to efficiently handle high volume event and log data spanning user behavior, campaigns, ads, content, and AI features

• Enabled dynamic EMR cluster provisioning with autoscaling and optimized instance fleets to balance performance and cost.

• Executed PySpark jobs stored in Amazon S3 to transform raw ingestion data into structured, analytics ready datasets.

• Integrated outputs with AWS Glue Data Catalog and Amazon Redshift, ensuring schema consistency and automatic cluster termination post processing.

Workflow Orchestration Using AWS MWAA (Apache Airflow) :

• Developed and deployed 65 production-ready Airflow DAGs to automate daily end-to-end data workflows

• Orchestrated dynamic EMR cluster lifecycle (provision → execute Spark jobs → terminate) directly from Airflow workflows.

• Managed complex task dependencies across ingestion, transformation, and Redshift loading to ensure reliable execution sequencing.

• Implemented retry mechanisms, monitoring, and logging to enhance pipeline resilience and operational visibility.

• Integrated Slack notifications and email alerts (via SMTP) to provide real-time Spark job status updates, including success, failure, and retry events.

Cross-Cloud Data Migration and Validation :

• Executed a secure one-time migration of 10 TB data from GCP to Amazon S3 using AWS DataSync, ensuring encrypted data transfer and minimal disruption.

• Performed checksum validation and object-level verification to maintain data integrity during the migration process.

• Conducted schema validation and dataset reconciliation to ensure structural consistency between source (GCP) and target (AWS) environments.

• Completed joint validation and business data sign-off prior to production rollout to ensure accuracy and operational readiness.

The architecture is deployed on AWS and organized into ingestion and analytics layers. User traffic flows over HTTPS through a Network Load Balancer (NLB) to an Amazon EKS cluster, where NGINX Ingress directs requests to the ingestion service pods. The ingestion layer stores incoming event and log data in Amazon S3. AWS Managed Workflows for Apache Airflow (MWAA) orchestrates the end-to-end data processing workflows, triggering Amazon EMR to process data directly from S3. The processed output is stored in separate S3 buckets, where AWS Glue crawlers catalog the datasets and load them into Amazon Redshift for structured analytics. Finally, Amazon Superset connects to Redshift to generate business intelligence dashboards.