A phased, risk-managed program to migrate from Oracle, Teradata, or on-prem warehouses to Snowflake or Databricks. We handle the architecture, pipeline redesign, and cutover — and leave your team able to run it. Backed by 20+ years of data architecture.
Enterprise data migrations are business-continuity challenges, not just technical projects. The Accelerator is built to avoid the five patterns that derail them.
Migrating everything at once turns cutover night into a crisis, and rollbacks are expensive and politically damaging.
Years of undocumented Oracle PL/SQL, triggers, and custom logic are tightly coupled — direct line-by-line translation rarely works.
ETL built for on-prem schedulers does not map cleanly to cloud-native orchestration; teams rewrite for months without a framework.
Your team knows Oracle, not yet Snowflake cost control or Databricks Delta Lake. Without enablement, the new platform becomes a black box.
Cloud warehouses bill differently. Without FinOps guardrails from day one, the first invoice is a shock.
We re-architect for the cloud rather than lift-and-shift. Every migration runs through five phases with clear gates, deliverables, and go/no-go decisions.
Full inventory of schemas, stored procedures, ETL, and downstream consumers; dependency mapping; risk scoring; and TCO modelling (current vs. projected cloud cost).
Deliverables: Migration roadmap · risk register · target architecture blueprint · cost projection & FinOps baseline
Provision the cloud environment with governance and cost guardrails from day one, build the landing/ingestion layer, and pilot 2–3 low-risk, high-visibility domains.
Deliverables: Production-ready landing zone · pilot domain validated · CI/CD for data · monitoring & observability · ops runbook
Migrate domains in priority order, refactor logic into dbt models / Spark transformations, rebuild ETL as cloud-native ELT, and run legacy + new in parallel for validation.
Deliverables: Core domains migrated · ELT suite operational · logic refactored & documented · performance benchmark · data-quality results
Execute the cutover with tested rollback ready, reconcile source vs. target, switch downstream consumers (BI, reports, APIs), and decommission legacy pipelines in a controlled way.
Deliverables: Zero-downtime cutover · stakeholder sign-off · legacy archived/decommissioned · post-migration support activated
Intensive knowledge-transfer workshops, architecture walkthroughs, troubleshooting deep-dives, cost-management training, and a 30–60 day post-migration support window.
Deliverables: Team ready to operate independently · full documentation repository · 30-day support · quarterly optimization check-in
We do not force-fit tools — the stack is chosen for your data volume, latency needs, and team skills.
| Layer | Technology | Purpose |
|---|---|---|
| Ingestion | Airflow · Azure Data Factory · Fivetran | Reliable, scheduled data movement |
| Storage & Compute | Snowflake · Databricks / Delta Lake | Elastic, decoupled storage and compute |
| Transformation | dbt · Spark SQL · Databricks SQL | ELT with version control and testing |
| Orchestration | Airflow · Databricks Workflows | Pipeline dependency management |
| Quality & Observability | Great Expectations · custom frameworks | Data validation and anomaly detection |
| Governance | Snowflake Governance · Unity Catalog · Purview | RBAC, masking, lineage, and tagging |
| FinOps | Resource Monitors · chargeback model | Cost visibility and control |
Drawn from 15+ cloud migrations and greenfield builds over the last five years. Actual results depend on your starting point.
Infrastructure cost reduction vs. legacy Oracle/on-prem
Faster batch processing
Data availability instead of next-day
To a self-sufficient team on the new platform
Downtime cutover, with tested rollback
Governance — RBAC, masking, and lineage
The Accelerator targets 90-day delivery for core workloads. Large estates (50+ TB, complex stored procedures) may extend to 16–20 weeks. We do not trade quality for speed.
No. We run parallel systems and execute a switchover, not a shutdown — with tested rollback procedures ready.
We refactor them into cloud-native patterns — dbt models for transformations, Spark for heavy compute, orchestration tools for scheduling — not brittle line-by-line translation. Every piece of logic is documented and tested.
We work with your team. Embedded enablement is part of the engagement — the goal is to make you self-sufficient, not dependent on us.
Discovery includes a platform recommendation based on your workload, cloud footprint, team skills, and budget model. We are platform-agnostic and recommend the right fit for you.
Fixed-price packages for well-scoped migrations, time-and-materials for complex or exploratory work. Every proposal includes a detailed scope and cost breakdown.
Every Accelerator engagement is led by Amit Koti — 20+ years of enterprise data architecture, SnowPro Core and 2× Databricks certified. The architect you talk to is the architect who designs the migration, guides the team, and owns the handover. No bait-and-switch, no junior hand-off.
In 30 minutes we will map your current state, surface the biggest risks, and outline a 90-day path to your modern data platform.
Cut Snowflake or Databricks costs by 30–50% without a migration.
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