Cloud Database Optimization, Migration & Modernization
We help technology leaders determine whether their current cloud database architecture is the most economical and operationally appropriate way to run their workloads, helping them optimize in place, migrate engines, or modernize hosting across PostgreSQL, MySQL, Oracle, Microsoft SQL Server, and Neo4j.
Initial Engagement: Estate Audit & Economics
Cloud Database Cost Assessment
A thorough, evidence-based audit of your cloud database estate across Google Cloud Platform (GCP), Amazon Web Services (AWS), and Microsoft Azure to identify compute overprovisioning, storage bloat, overprovisioned IOPS, idle replicas, and expensive licensing overhead.
What We Analyze Across Engines:
- • Compute and memory utilization curves across primary and standby nodes
- • Storage allocations and provisioned Input/Output Operations Per Second (IOPS)
- • Non-production storage duplication and branching feasibility
- • Commercial per-core licensing overhead (Oracle Database & Microsoft SQL Server)
- • Active read replica sizing and idle standby capacity
- • Reserved instances, savings plans, and committed-use discount coverage
Key Deliverables Produced:
- • Current-state Total Cost of Ownership (TCO) financial model
- • In-place rightsizing opportunities without platform disruption
- • Engine migration & hosting modernization feasibility analysis
- • Architecture alternatives with explicit trade-offs and operational prerequisites
- • Prioritized step-by-step engineering roadmap with risk mitigation plans
Implementation & Execution Services
Targeted engineering engagements designed to implement the recommendations identified during your assessment.
In-Place Database Optimization
Maximize the efficiency of your existing Google Cloud SQL, Amazon RDS, Aurora, or Azure database instances without the operational disruption or risk of migrating engines.
Optimization Scope:
- • Rightsizing compute instances to align with empirical baseline utilization
- • Trimming overprovisioned IOPS and auto-expand storage volumes
- • Scheduling non-production and development database instances
- • Structuring committed-use discounts and reserved instance portfolios
Key Benefits:
- • Rapid financial impact without application architectural changes
- • Disciplined, low-risk process with no mandatory schema alterations
- • Preserves familiar managed operational workflows
Database Engine & Platform Migration
Execute risk-mitigated database migrations across on-premises, cloud, and Kubernetes platforms, or modernize proprietary commercial engines (Oracle, SQL Server) to modern PostgreSQL, with minimal cutover downtime.
Migration Scope:
- • On-premises / data center → cloud database platform migrations
- • Cloud-to-cloud platform and multi-cloud database transitions
- • Managed cloud database → Kubernetes database platform transitions
- • Oracle Database & Microsoft SQL Server → PostgreSQL modernization where justified
- • Data warehouse modernization: migrating Snowflake or BigQuery workloads to Trino operators on Kubernetes, easily saving 50%+ depending on usage
- • Database estate consolidation and platform modernization
- • Evaluation of Aurora Standard, Aurora I/O-Optimized, Amazon RDS PostgreSQL, and Kubernetes alternatives based on Total Cost of Ownership
Verification & Safety:
- • Pre-migration compatibility assessment and workload profiling
- • Cutover rehearsals, data reconciliation, and automated validation tests
- • Explicit rollback procedures and phased cutovers to manage and minimize operational risk
Platform Modernization on Kubernetes
Achieve direct cost savings by moving suitable workloads off managed database services onto Kubernetes-based database platforms. Where supported by the selected storage architecture, Copy-on-Write cloning can reduce lower-environment storage duplication and provisioning time.
Engineering Capabilities:
- • Production deployments of best-in-class, open-source database and query operators (PostgreSQL, Trino) on GKE, EKS, and AKS
- • Declarative Infrastructure-as-Code (Terraform) and GitOps cluster management
- • Analytical query decoupling: running Trino on Kubernetes over cloud object storage to cut data warehouse spend by 50%+ depending on usage
- • Copy-on-Write cloning where supported by the selected storage architecture to reduce lower-environment storage duplication
- • Direct cloud object storage backup integration (S3, GCS, Azure Blob)
Target Environment & Fit:
- • Best fit for teams with some in-house Kubernetes familiarity
- • Substantially reduces infrastructure-level markups over managed database instances
- • Enhances environment density, developer branching, and deployment control
Reliability, Performance & Advisory Services
Ongoing technical advisory and deep engineering support for critical database infrastructure.
Reliability & Disaster Recovery
Validate Recovery Point Objectives (RPO) and Recovery Time Objectives (RTO), verify Point-in-Time Recovery (PITR), automate Write-Ahead Log (WAL) archiving, and test failover runbooks.
- • Backup verification & automated restore drill pipelines
- • Cross-region replication and split-brain prevention
- • High-availability architecture reviews
Database Observability, Performance & Operational Intelligence
CloudDBOptimize designs monitoring around the behavior that matters to each database and workload, rather than simply infrastructure availability. Our approach is informed by nearly 30 years of production database operations and management experience.
- • Query latency distributions, slow query profiling, and execution plan analysis
- • Workload and activity baselines across diurnal and seasonal traffic peaks
- • Transaction throughput (TPS), commit rates, and connection pool pressure (PgBouncer, ProxySQL)
- • Locking, blocking contention, and deadlock analysis
- • Replication behavior, replica lag, and WAL streaming synchronization
- • Buffer-cache efficiency and engine-specific memory tuning, including PostgreSQL shared_buffers where applicable
- • Storage latency, Input/Output Operations Per Second (IOPS), and disk queue depth
- • Capacity growth forecasting, autovacuum bloat remediation, and tablespace headroom
- • Backup health, automated restore drill pipelines, and recovery validation
- • Unusual workload shift detection and application-specific alerting thresholds
Cloud Data Platform Advisory
Strategic advisory across Cloud Data Operations (DataOps), infrastructure automation with Terraform, cloud observability (Prometheus / Grafana), and long-term data platform governance.
- • Declarative database infrastructure via Terraform
- • Telemetry and alert design with Prometheus & Grafana
- • FinOps database cost tracking and estate governance
How We Engage With Your Team
Engagements are designed to be low-friction, technically rigorous, and non-disruptive to active development and production workloads.
Discovery & Scoping
Initial technical alignment to understand your cloud environment, database engines, operational constraints, and commercial concerns.
Telemetry & Estate Audit
Read-only inspection of performance metrics, query profiles, storage configurations, replication architectures, and billing line items.
Economics & Recommendations
Delivery of the Total Cost of Ownership model, architecture recommendations, risk mitigation assessments, and implementation roadmap.
Partnered Execution
Collaborative implementation alongside your internal engineering team to execute optimization, rightsizing, or migration milestones.