Assess & cost
Inventory legacy workloads, build TCO model for current vs. target architecture, and prioritize migrations by cost, risk, and analytics value.
The problem
Legacy warehouses can't keep up with what the business asks today
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Compute cost is ballooning
Hardware refresh cycles, licensing tiers, and overnight batches drive total cost up year on year, while utilization sits below 40% off-peak.
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Batch windows are shrinking
Reports need to land before 7am, but ingestion volumes outgrew the window. Engineers tune jobs every quarter just to keep yesterday's SLAs.
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Dashboards lag hours behind
Real-time decisions live in operational systems while leadership waits for the next morning's batch—and the gap keeps widening.
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No clean cutover plan
Past migration attempts ran in parallel for years, reports diverged, and the team can't agree on a date when legacy is finally retired.
运作方式
A migration that ships value before legacy retires
Modernize & validate
Move ETL to ELT, re-architect models on cloud-native engines, and run reconciliation in parallel so business reports stay correct end-to-end.
Cut over & retire
Orchestrated cutover with rollback gates, full performance tuning, and a dated decommission plan—so legacy actually leaves the estate.
Approach adapts to your target platform: Snowflake, BigQuery, Databricks, or hybrid.
现代化仓库，现代化速度
包含内容
Everything you needso the warehouse modernizes without breaking reports
Assessment, migration, governance, and decommissioning—delivered as one program your data and finance teams can sign off on.
Assessment & TCO model
Workload inventory, dependency map, and side-by-side cost comparison for legacy vs. target architecture.
ETL/ELT modernization
Refactor pipelines to cloud-native patterns with incremental loads, schema evolution, and contract enforcement.
Cutover orchestration
Parallel runs, reconciliation reports, and gated cutover with rollback paths so business reports never break.
Governance migration
Move access policies, lineage, and catalog metadata to the new platform—no governance regression at go-live.
Performance tuning
Workload-aware tuning, clustering, and warehouse sizing so cost lands where the TCO model promised.
Decommission plan
Dated retirement schedule with stakeholder sign-off—legacy actually leaves the estate, not just runs in parallel forever.
技术提供 Synapse
成果
What changes when the warehouse modernizes properly
Results vary by context, data maturity, and scope. We scope honestly before we promise precisely.
30–50%
lower run cost vs. legacy warehouse at steady state
Orientative—depends on starting workload and licensing.
Minutes
freshness for dashboards that previously waited for overnight batch
Orientative—based on ELT-modernized workloads.
Dated
decommission plan for legacy, with stakeholder sign-off
合作方式
From assessment to retirement—without report regressions
Assess
Assess
Week 1–4
Workload inventory, dependency map, TCO model, and target architecture decision.
Design
Design
Week 5–8
Pipeline patterns, governance migration plan, cutover sequence, and reconciliation framework.
Migrate
Migrate
Week 9–20
Move workloads in waves, run parallel reconciliation, tune performance, and validate business reports.
Retire
Retire
Week 21+
Dated cutover per workload, decommission legacy, and operate the new platform with cost monitoring.
Timelines vary by warehouse size, dependency complexity, and report-validation surface.
延伸阅读
Warehouse, AI, and what to explore next
开始
Ready to retire the legacy warehouse, not just run it in parallel forever?
No commitment. We start with a scoped session to map workloads, TCO, and cutover risk.