Cloud Data Architecture & Migration

Modernize your data infrastructure with cloud-native architectures. From multi-cloud strategies to serverless data processing, we help organizations leverage cloud technologies for scalable, cost-effective data solutions.

Cloud Assessment

Cloud Data Services

Cloud Migration

Seamless migration from on-premises to cloud with minimal downtime.

Multi-Cloud Strategy

Hybrid and multi-cloud architectures for vendor independence.

Serverless Data Processing

Event-driven data processing with AWS Lambda, Azure Functions.

Cloud Data Warehousing

Modern data warehouses with Snowflake, BigQuery, Redshift.

Data Lake Implementation

Scalable data lakes on AWS S3, Azure Data Lake, Google Storage.

Cost Optimization

Resource optimization and cost management strategies.

Cloud Platforms

Amazon Web Services (AWS)

Comprehensive AWS data services including S3, Redshift, EMR, Glue, and Kinesis.

Microsoft Azure

Azure data platform with Synapse, Data Factory, Data Lake, and Cosmos DB.

Google Cloud Platform

Google Cloud data services including BigQuery, Dataflow, Pub/Sub, and Vertex AI.

Snowflake

Cloud data platform for data warehousing, data lakes, and data sharing.

Cloud Data Architecture

Cloud Data Architecture

Modern cloud-native architectures designed for scalability, performance, and cost optimization

  • Serverless computing integration
  • Auto-scaling capabilities
  • Multi-region deployment
  • Disaster recovery planning
  • Security best practices
Explore Architecture
Migration Strategy

Migration Strategy

Comprehensive migration planning and execution with risk mitigation and testing

  • Assessment and planning
  • Proof of concept development
  • Phased migration approach
  • Data validation and testing
  • Performance optimization
Plan Migration
Cloud Data Architecture Patterns

Cloud-Native Architectures

Data Lake Architecture (AWS)

Data Sources → Amazon Kinesis → Amazon S3 (Data Lake)
                             ↓
AWS Glue (ETL) → Amazon Athena/Redshift Spectrum (Analytics)
             ↓
Amazon QuickSight (Visualization)

Modern Data Stack (Snowflake)

  • Data Ingestion: Fivetran, Stitch, or custom ELT pipelines
  • Data Storage: Snowflake cloud data platform
  • Data Transformation: dbt (data build tool)
  • Analytics: Looker, Tableau, or PowerBI
  • Orchestration: Airflow or Prefect

Serverless Data Processing

  • Event-Driven Architecture: Lambda functions triggered by S3 events
  • Stream Processing: Kinesis Analytics for real-time processing
  • Batch Processing: AWS Batch or Azure Batch for scheduled jobs
  • API Integration: API Gateway for data service exposure

Multi-Cloud Strategies

Hybrid Cloud Patterns

  • Data Residency Compliance: Keep sensitive data on-premises
  • Burst to Cloud: Scale to cloud during peak loads
  • Disaster Recovery: Cloud as backup site
  • Development Environments: Cloud-based dev/test environments

Multi-Cloud Data Mesh

Architecture:
  AWS:
    - Primary data lake (S3)
    - Real-time processing (Kinesis)
    - ML/AI services (SageMaker)
  
  Azure:
    - Business applications integration
    - Identity management (Active Directory)
    - Hybrid connectivity (ExpressRoute)
  
  GCP:
    - Advanced analytics (BigQuery)
    - Machine learning (Vertex AI)
    - Real-time messaging (Pub/Sub)

Migration Strategies

Lift and Shift

  • Virtual Machine Migration: Direct VM replication to cloud
  • Database Migration: AWS DMS, Azure Database Migration Service
  • Application Containerization: Docker and Kubernetes deployment
  • Network Configuration: VPN and private connectivity setup

Re-platforming

  • Managed Services Adoption: RDS instead of self-managed databases
  • Serverless Conversion: Functions instead of always-on servers
  • Container Orchestration: Kubernetes for microservices
  • API Modernization: RESTful APIs and GraphQL implementation

Re-architecting

  • Microservices Design: Breaking monoliths into services
  • Event-Driven Architecture: Asynchronous communication patterns
  • Data Mesh Implementation: Domain-oriented decentralized data
  • Cloud-Native Services: Leveraging cloud-specific capabilities

Cost Optimization Strategies

Resource Management

  • Right-Sizing: Matching resources to actual usage
  • Reserved Instances: Long-term commitments for stable workloads
  • Spot Instances: Cost-effective compute for flexible workloads
  • Lifecycle Policies: Automatic data archiving and deletion

Storage Optimization

# Example AWS S3 lifecycle policy
{
    "Rules": [
        {
            "ID": "DataLifecycleRule",
            "Status": "Enabled",
            "Transitions": [
                {
                    "Days": 30,
                    "StorageClass": "STANDARD_IA"
                },
                {
                    "Days": 365,
                    "StorageClass": "GLACIER"
                },
                {
                    "Days": 2555,
                    "StorageClass": "DEEP_ARCHIVE"
                }
            ]
        }
    ]
}

Monitoring and Optimization

  • Cost Monitoring: CloudWatch, Azure Cost Management
  • Resource Tagging: Comprehensive cost allocation
  • Automated Scaling: Auto-scaling groups and policies
  • Performance Monitoring: Application and infrastructure metrics

Security and Compliance

Data Protection

  • Encryption: At rest and in transit encryption
  • Key Management: AWS KMS, Azure Key Vault, Google KMS
  • Access Control: IAM policies and role-based access
  • Network Security: VPCs, security groups, and NACLs

Compliance Frameworks

  • SOC 2: Security and availability controls
  • PCI DSS: Payment card industry standards
  • HIPAA: Healthcare data protection
  • GDPR: European data privacy regulation

Accelerate Your Cloud Journey

Ready to modernize your data infrastructure? Let’s assess your current state and design a cloud migration strategy.
Start Cloud Assessment