phased migration strategy with zero-downtime approach, validation checkpoints at every stage, and rollback plans if issues emerge
KMS Technology acquires Addepto. Read the Full Press Release
KMS Technology acquires Addepto. Read the Full Press Release
Migrate to Cloud Without the Budget Overruns and Failed Cutovers: Move from legacy data warehouses to modern cloud platforms with a structured roadmap that prevents budget overruns, eliminates downtime risk, and ensures you select the right architecture for your workloads.
Moving from legacy data warehouses to modern cloud platforms should unlock faster analytics, lower costs, and AI-ready architecture. But most organizations face an impossible choice:
Migrate without proper planning: Rush to cloud, skip discovery, underestimate complexity. Projects miss timelines, exceed budgets, and create data quality issues discovered months after go-live.
Plan endlessly, never execute: Analysis paralysis. Internal teams debate Snowflake vs. Databricks vs. BigQuery while legacy costs compound and competitors already migrated pull ahead.
Hire consultants who don’t execute: Receive generic migration frameworks and beautiful PowerPoints. When it’s time to build, consultants leave, your team inherits the risk, and the recommended architecture doesn’t match your actual workloads.
This gap hits enterprises running legacy warehouses—Oracle, Teradata, SQL Server, Netezza—where costs spiral, performance degrades, and platforms can’t support modern analytics or AI workloads.
The Impact of Getting Migration Wrong:
In a focused three-week engagement, KMS Technology and Addepto work alongside domain experts to evaluate the full data and AI platform landscape.
The assessment spans cloud infrastructure, data warehouses and lakehouses such as Snowflake, Databricks, and BigQuery, as well as integration layers, governance frameworks, and ML infrastructure.
The objective is to identify cost inefficiencies, architectural constraints, and capability gaps that limit AI adoption—then translate those findings into a clear, actionable modernization strategy.
Within three weeks, you will get:
The outcome is a prioritized modernization roadmap grounded in the existing technology stack, organizational capabilities, and strategic business objectives.
KMS and Addepto works with your team through our data engineering services to transform migration uncertainty into execution confidence, assessing your legacy warehouse, evaluating cloud platforms against your actual workloads, and building a risk-mitigated roadmap from planning through cutover.
Through this engagement, you’ll:
phased migration strategy with zero-downtime approach, validation checkpoints at every stage, and rollback plans if issues emerge
complete discovery of data volumes, workload patterns, dependencies, and transformation logic reveals exactly what you’re migrating
time-sensitive roadmap prioritizes workloads by business value and technical risk, ready to hand off to implementation teams
TCO modeling compares legacy costs to cloud economics across a realistic 3-year horizon, turning migration into an ROI conversation
Snowflake, Databricks, or BigQuery evaluated against your requirements (not vendor marketing) with technical rationale backing the recommendation
The work focuses on concrete questions: where money is being wasted, where architecture limits scale or reliability, and whether the platform can support production-grade AI use cases. The outcome is not a theoretical framework, but a set of clear, defensible recommendations tied to cost, risk, and execution capacity.
The structure is consistent, but the conclusions are shaped by the platform’s real constraints, workloads, and business goals.
The result is a clear view of what the current platform enables, what it blocks, and what must change to support sustainable growth and AI execution.
At the end of engagement, you receive architecture recommendations, detailed migration roadmap, and risk mitigation strategy—everything needed to execute your migration with confidence, hand off to implementation teams, or bring to executive stakeholders for funding approval.
Complete inventory of data sources, warehouse schemas, ETL logic, workload patterns, dependencies, and transformation rules—the truth about what you’re actually migrating.
Snowflake, Databricks, and BigQuery compared against your workloads, team skills, cloud ecosystem, and growth trajectory—with clear recommendation and rationale.
Modern cloud warehouse design tailored to your analytics needs, data volumes, concurrency requirements, and AI/ML roadmap—showing how the new platform supports what legacy couldn’t.
Phased migration plan prioritized by business value and technical risk: which workloads migrate first, dependencies sequenced, validation gates defined, timeline estimated.
Risk mitigation approach using parallel systems, incremental cutover, continuous validation, and rollback procedures—minimizing disruption to operations.
Financial modeling comparing legacy costs (licensing, infrastructure, maintenance, personnel) to cloud economics with elasticity and efficiency gains.
Beyond deliverables, this engagement transforms how your organization approaches data infrastructure—replacing fear of migration with execution confidence and strategic clarity.
You understand exactly what can go wrong, how to prevent it, and what success criteria define each phase—replacing uncertainty with engineering discipline.
Architecture choice matches your workloads, team capabilities, and growth trajectory—not sales pitches—preventing a multi-million dollar wrong bet.
Reducing legacy system upkeep through cloud efficiency frees resources for AI, real-time data analytics, and new competitive capabilities
Roadmap is execution-ready with sequenced phases, validation checkpoints, and rollback strategies—your engineers can implement or hand off confidently.
TCO analysis and ROI modeling translate technical migration into financial outcomes stakeholders understand—accelerating approval and investment.
KMS Technology and Addepto combine deep enterprise software delivery experience with hands-on data and AI expertise. The result is a consulting approach focused on turning platform assessments into real, executable modernization outcomes.
covering infrastructure, data, AI/ML tooling, integration, and governance.
designed to align with industry requirements, scale expectations, and long-term AI ambitions
Platform guidance accounts for AI requirements from the start—data availability, operational stability, and readiness for production inference—rather than treating AI as a future add-on.
focused on support for generative AI, real-time inference, vector databases, and feature stores
Platforms are designed to be usable and sustainable—supporting self-service, clear ownership, and efficient workflows for data, engineering, and analytics teams.
including right-sizing opportunities, license rationalization, and structural inefficiencies
KMS contributes proven software engineering and cloud delivery at enterprise scale. Addepto brings deep specialization in data platforms, analytics, and AI. Together, platforms are evaluated not just for architectural soundness, but for their ability to reliably support production workloads and AI initiatives.
The same teams that assess the platform are capable of modernizing it. This removes handoff risk, shortens time to value, and ensures recommendations are grounded in what can actually be built, operated, and scaled.
Experience across regulated and high-growth industries ensures that compliance, data governance, and performance are addressed without slowing execution or innovation.
Every recommendation is tied to measurable outcomes: lower operating costs, improved reliability, faster delivery of analytics and AI use cases, and reduced operational risk.
The work is led by senior engineers and architects who have designed, optimized, and run data and AI platforms in production. Recommendations reflect real-world constraints—cost, reliability, security, and team capacity—not theoretical best practices.
grounded in the existing technology stack, organizational capabilities, and strategic business objectives.
The Cloud Data Migration Strategy creates your roadmap and de-risks platform selection. From here, organizations typically expand through:
Plan your cloud migration with clarity—assess your warehouse, evaluate platforms without bias, and build a roadmap for success.
