Cloud Data Infrastructure : services, coûts et migration, MBA Training, MBA Training

The build vs. buy decision

Every CDO faces this decision repeatedly: build custom solutions or buy vendor products?

The case for buying: Speed to value. A Snowflake implementation takes weeks, not years. Vendor manages infrastructure, performance tuning, and upgrades. Your team focuses on data problems, not infrastructure problems.

The case for building: Control. You own the code, the schema, the optimization. No vendor lock-in. No per-query pricing surprises. Open source (Spark, Iceberg, Airflow) gives you full flexibility.

The practical answer: Buy the commodity components (storage, compute, orchestration SaaS), build what differentiates your business (proprietary data models, custom ML pipelines, domain-specific features).

Multi-cloud and cloud-agnostic architecture

Multi-cloud sounds appealing (no vendor lock-in, best-of-breed services) but comes with real costs: operational complexity, data transfer costs, team skill fragmentation. Most organizations using multi-cloud do so for regulatory reasons (data residency requirements) or as the result of M&A activity, not by deliberate choice.

Open table formats (Apache Iceberg, Delta Lake) and open compute frameworks (Apache Spark) reduce lock-in without requiring multi-cloud complexity. Use them as your hedge.

Cost architecture in the cloud

Cloud data costs are non-trivial and easy to underestimate. Key cost drivers:

Storage: Cheap but scales with retention. Implement lifecycle policies, move data to cheaper tiers after 90 days, archive after 1 year.
Compute: The main variable cost. Warehouse query costs scale with data scanned. Clustering and partitioning reduce data scanned, directly reducing cost.
Egress: Moving data between regions or to on-premise is expensive. Design to minimize cross-region data movement.
Tools and licenses: Snowflake, Databricks, and similar tools layer pricing on top of cloud costs. Model total cost before committing.

At Spotify, a dedicated "data platform cost optimization" team reduced cloud spend by 30% without reducing functionality, by implementing query optimization rules, data retention policies, and compute scheduling. A CDO who doesn't own cost architecture doesn't truly own the data platform.

Migration strategy: on-prem to cloud

Most CDOs inherit some on-premise infrastructure. Cloud migration is almost always the right direction but requires sequencing:

1. Assess and inventory: What exists, what it costs, what it supports

2. Identify low-risk candidates: Historical data, archival data, reporting workloads

3. Lift-and-shift first: Move workloads before optimizing them, proving cloud works reduces organizational resistance

4. Refactor incrementally: Optimize for cloud-native patterns after stabilization

5. Decommission progressively: Kill on-premise only after cloud workloads are stable

Avoid the trap of re-architecting everything at once. Many cloud migrations fail not because of technical complexity but because the scope is too ambitious.

Quiz Questions

1. Quelle est la principale raison pour laquelle la plupart des organisations adoptent le multi-cloud ?

A) Pour réduire les coûts

B) Pour des raisons réglementaires ou suite à des M&A

C) Pour améliorer les performances

D) Pour simplifier l'architecture

Réponse: B

2. Quel est le principal avantage d'utiliser des formats de tables ouverts comme Apache Iceberg ?

A) Ils sont plus rapides que les solutions propriétaires

B) Ils réduisent les coûts de stockage

C) Ils réduisent le vendor lock-in sans nécessiter une architecture multi-cloud

D) Ils sont maintenus par les fournisseurs cloud

Réponse: C

3. Dans la stratégie de migration cloud, quelle est la bonne séquence ?

A) Refactoriser d'abord, puis migrer

B) Tout migrer et optimiser simultanément

C) Inventorier → migrer les cas simples → lift-and-shift → refactoriser → décommissionner

D) Décommissionner l'on-premise avant de migrer