Pacific Data Integrators' Technology Insights Blog

Cloud Data Warehousing — Best Practices for Migration

Written by Blog Post by PDI Marketing Team | Oct 13, 2025 2:48:03 PM

 

TL;DR
 
For successful cloud data warehouse migration, combine a clear scope with cloud data warehousing best practices: move from ETL to ELT migration, add Change Data Capture (CDC) where freshness matters, enforce data governance and lineage, bake in data quality as code and validation and parity testing, and apply cost optimization for data pipelines (FinOps). Use a wave plan, standardized data orchestration (Apache Airflow), and a repeatable migration runbook & checklist to reduce risk and cost.
Migration phases at a glance
  1. Assess— Inventory sources/targets, jobs, SLAs, costs, and compliance scope.
  2. Prioritize— Wave plan by dependency and business value; decide retain / re‑platform / re‑architect / retire.
  3. Plan— Environments, security (RBAC/ABAC), masking & PII compliance, CI/CD, and cutover strategy.
  4. Build— Implement ingestion (batch + Change Data Capture (CDC)), adopt ETL to ELT with modular SQL/transformations, and set up orchestration.
  5. Validate & Cutover— Automated parity testing and performance checks; managed switchover with rollback readiness.
  6. Operate & Optimize— FinOps guardrails, SLO dashboards, lineage‑based impact checks, and continuous tuning.
25 Best Practices for Cloud DW Migration

Strategy & Scoping

  1. Define business outcomes first. Set latency, quality, and cost KPIs before tools.
    Why: Analysts stress outcome-led programs over tech-first moves.
    Source: McKinsey — Manage data like a product. 

  2. Adopt a domain view for waves. Migrate by business domain to reduce coupling and clarify owne3 rship.
    Why: Product-style operating models map cleanly to domains.
    Source: McKinsey — Scaling data products. 

  3. Decide per workload: retain / re-platform / re-architect / retire. Avoid blanket lift-and-shift.
    Why: Gartner urges composable, fit-for-purpose modernization—don’t replicate monoliths.
    Source: Gartner — Data Fabric overview. 
Architecture & Patterns

  1. Prefer ELT pushdown. Run transforms in the MPP/lakehouse engine; reserve external code for non-SQL logic.
    Why: Cloud trends favor ELT for performance/maintainability.
    Source: Gartner — Top Trends in Data & Analytics. 

  2. Use CDC for freshness and efficient backfills. Treat change tables as first-class data products.
    Why: Real-time/near-real-time patterns are key to activation and time-sensitive analytics.
    Source: Forrester — Real-Time Analytics hub. 

  3. Model curated layers with SCD2 where needed. Landing → staging → dims (SCD Type 2) → facts.
    Why: Stable dimensional history simplifies analytics and auditing.
    Source: Snowflake — Data Integration (ETL/ELT) guide.

  4. Separate compute tiers. Split ELT, BI/semantic, and ad-hoc workloads.
    Why: Prevent resource contention and enable cost control.
    Source: FinOps Foundation — Framework. 

  5. Standardize orchestration. Use Apache Airflow (or equivalent) with retries/backfills/SLA monitors.
    Why: Reliable, observable pipelines reduce failure MTTR.
    Source: Apache Airflow — Docs. 
Security, Governance & Lineage

  1. Policy-as-code for RBAC/ABAC & masking. Automate entitlements and PII controls.
    Why: Governance embedded in workflows > centralized gates.
    Source: Forrester — Data Governance hub.

  2. Capture end-to-end lineage. Stitch ingest → transform → publish for impact analysis and audits.
    Why: Active metadata is a control plane for modernization.
    Source: Gartner — From Passive to Active Metadata (session). 

  3. Data quality as code in CI/CD. Enforce schema, referential integrity, distribution checks, KPI parity.
    Why: Shift-left testing reduces cutover risk.
    Source: Great Expectations (GX) — GX Core. 
Build & DevEx

  1. Reusable ELT templates (MERGE, SCD2, partition). Avoid bespoke code for common patterns.
    Why: Speeds delivery and standardizes quality.
    Source: Databricks — Warehouse→Lakehouse migration approaches. 

  2. Parameterize environments; manage secrets centrally. Rotate credentials automatically.
    Why: Reduces configuration drift and security gaps.
    Source: Azure — Synapse migration guide (security considerations). 

  3. Treat pipelines as products. Assign owners, SLAs/SLOs, roadmaps.
    Why: Ensures accountability and business alignment.
    Source: McKinsey — Manage data like a product. 
Validation & Cutover

  1. Parallel runs with automated comparisons. Row counts, checksums, quantiles.
    Why: Objective evidence to build trust.
    Source: BigQuery — Load/Transform/Export intro (testing & validation context). 

  2. Acceptance thresholds for KPI parity. Define tolerances before go-live.
    Why: Locks scope and speeds approvals.
    Source: Forrester — Data Governance hub (policy & quality). 

  3. Rehearse rollback. Keep legacy endpoints warm until stability is proven.
    Why: Reduces blast radius of surprises.
    Source: IDC — Cloud market trends (risk & operational discipline). 
Performance & Cost (FinOps)

  1. Right-size compute & isolate workloads. Use auto-suspend/scale and resource monitors.
    Why: Prevents runaway spend and contention.
    Source: Snowflake — Resource Monitors.
     
  2. Partition/cluster & materialize heavy transforms. Cache hot aggregates.
    Why: Reduce scan and compute costs.
    Source: BigQuery — Partitioned & clustered tables. 

  3. Track cost per TB and per successful run. Set budgets and anomaly alerts.
    Why: Make spend measurable and actionable.
    Source: FinOps Foundation — Framework. 

  4. Favor columnar formats & pruning. Tune file sizes/statistics for efficient I/O.
    Why: Lowers latency and cost at scale.
    Source: Databricks — Delta Lake OPTIMIZE/Z-ORDER. 
Operations & Reliability

  1. Publish SLOs for freshness/throughput/success. Manage error budgets explicitly.
    Why: SRE-style discipline improves reliability.
    Source: McKinsey — Scaling data products (operating model). 

  2. Alert on data & job health. Watch schema drift, volume spikes/drops, lateness.
    Why: Detect anomalies early.
    Source: OpenLineage — Docs. 

  3. Lineage-aware change management. Assess downstream impact before merges/deploys.
    Why: Prevents surprise breakage.
    Source: Gartner — Active Metadata (control plane). 

  4. Monthly posture reviews. Optimize queries, storage, and governance; revisit wave plan.
    Why: IDC notes hybrid/multicloud estates evolve—continuous tuning is required.
    Source: IDC — Cloud market trends 2024 recap. 
Platform‑specific guidance

Snowflake migration

  • Use Task/Stream for incremental patterns; leverage Automatic Clustering and Search Optimization prudently.
  • Isolate warehouses by workload; enable resource monitors for spend guardrails.
  • External tables + Snowpipe for efficient ingestion; MERGE for CDC upserts.

BigQuery migration

  • Adopt partitioned & clustered tables; minimize cross‑region shuffles.
  • Prefer scheduled queries/Cloud Composer for orchestration; materialized views for hot aggregates.
  • Use Storage Write API for streaming and cost‑effective ingestion.

Databricks (Lakehouse)

  • Use Delta Lake with OPTIMIZE and Z‑ORDER for pruning; Auto Loader for ingestion.
  • Keep Unity Catalog central for security/lineage; manage reliable MERGE patterns for CDC.
  • Size Jobs/Clusters by task profile; favor serverless where appropriate.

Azure Synapse / Fabric

  • Design for ELT: PolyBase/Copy into staging → transform with SQL/SPARK pools.
  • Orchestrate with ADF/Synapse pipelines; manage cost with short‑lived compute.
  • Use Dynamic Data Masking and Row‑Level Security for PII.
KPIs to track

  • Freshness SLO: % tables within target latency
  • Reliability: On‑time run rate; failure rate with auto‑retry
  • Cost: Cost per TB transformed; cost per successful run
  • Quality: Variance vs. legacy KPIs during parallel runs
  • DevX: Lead time for new pipeline; MTTR for failed runs
Common pitfalls to avoid

  • One mega‑warehouse for all workloads
  • Re‑creating on‑prem ETL monoliths in the cloud
  • Tests as a phase instead of data quality as code
  • No clear rollback or KPI parity thresholds
  • Weak lineage → surprise downstream breakage
 
Printable checklist — Cloud Data Warehousing Best Practices

Foundations
  • Inventory & lineage captured
  • Workloads scored and waved
  • Security policies (RBAC/ABAC, masking, PII) as code
  • Environments and CI/CD ready

Build
  • ELT patterns and CDC selected per workload
  • Orchestration DAGs with retries/backfills
  • Data quality as code tests authored and versioned
  • Catalog & lineage stitched end‑to‑end

Cutover
  • Parallel run variance within thresholds
  • Sign‑offs from data owners and downstream owners
  • Rollback rehearsed
  • Monitoring dashboards live

Operate
  • Budgets & anomaly alerts configured
  • SLOs & error budgets defined
  • Monthly optimization cadence in place
 
Analyst Insights: Cloud Data Warehousing — Best Practices for Migration

Industry analysts converge on a few themes for successful cloud data warehouse migration: adopt composable architectures (not monoliths), push transformations with ELT and active metadata/lineage, run programs with product-style ownership and FinOps discipline, and design for hybrid/multicloud portability.
 
Gartner — Composable, governed, and automated data stacks

  • Composable architecture & Data Fabric: Build modular capabilities for ingestion, transformation, quality, and governance instead of one monolith. Source

  • Active metadata & lineage: Use metadata as a control plane for observability, policy enforcement, and impact analysis. Source

  • Trends: ELT pushdown, streaming/CDC, and cost-aware operations appear in recurring Top Trends in Data & Analytics. Source

 
McKinsey — Treat data pipelines as products and tie to business value

  • Manage data like a product: Cross-functional ownership, SLAs/SLOs, and roadmaps for pipelines and semantic layers. Source

  • Scale data products: Prioritize domains that unlock measurable outcomes and reduce time-to-insight. Source

  • FinOps as code: Balance speed with cost governance using programmatic guardrails. Source

 
IDC — Hybrid/multicloud reality, automation first, and economic control

  • Hybrid & multicloud: Design for portability and standards across platforms (Snowflake, BigQuery, Databricks, Synapse). Source

  • Automation-first migrations: Code conversion, CI/CD for data, and orchestrated testing to reduce risk and cost. Source

  • Economic discipline: Right-sizing, usage visibility, and budget guardrails post-migration. Source

 
Forrester (contextual) — Real-time analytics and pragmatic governance

  • Real-time analytics: Support streaming/CDC patterns for time-sensitive use cases and activation. Source

  • Pragmatic governance: Federated, policy-driven controls embedded in developer workflows. Source

 
What this means for your program: Map these insights to the guide here—use ELT and CDC where it matters, implement active lineage and data quality as code, assign product owners with SLAs/SLOs, and instrument FinOps from day one.
 
FAQs

Is ELT always better than ETL in the cloud?

Not always—but in most cases, pushing transforms into the warehouse/lakehouse improves maintainability, performance, and cost transparency. Keep external code for ML/advanced transforms.
 
How do we keep costs predictable?

Right‑size compute, separate workloads, set budgets/alerts, and track cost per successful run; materialize heavy transforms and prune scans.
 
How do we ensure trust after migration?

Automate validation and parity testing (counts, checksums, quantiles, KPI parity), run parallel for a fixed window, and rehearse rollback.
 
Do we need real‑time (CDC) from day one?

Start with batch where acceptable; add CDC for domains that need freshness. Treat CDC tables as products with clear contracts and owners.
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