Cloud has changed data architecture permanently. On-premise data infrastructure, the owned servers, the SAN storage, the proprietary appliances, is increasingly a legacy choice. But cloud data infrastructure isn't a single thing. It's a spectrum of services and trade-offs that require deliberate architectural decisions.
Every major cloud provider offers a complete data stack. Here's what each one includes:
AWS: S3 (storage), Glue (ETLETLETL (Extract, Transform, Load) is a data integration process that pulls data from sources, reshapes it into a consistent format, and writes it into a target system.View full definition →/catalog), Redshift (warehouse), Athena (serverless SQLSQLSales Qualified Lead: a prospect the sales team has validated as ready for direct outreach and a proposal, having passed clear qualification criteria.View full definition → on S3), EMR (Spark), SageMaker (ML), Kinesis (streaming), Lake Formation (governance).
Google Cloud: GCS (storage), BigQuery (warehouse + lake hybrid), Dataflow (streaming/batch ETLETLETL (Extract, Transform, Load) is a data integration process that pulls data from sources, reshapes it into a consistent format, and writes it into a target system.View full definition →), Vertex AI (ML), Pub/Sub (messaging), Dataplex (governance).
Azure: ADLS Gen2 (storage), Synapse Analytics (warehouse + lake), Data Factory (ETLETLETL (Extract, Transform, Load) is a data integration process that pulls data from sources, reshapes it into a consistent format, and writes it into a target system.View full definition →), Azure ML, Event Hubs (streaming), Microsoft Purview (governance).
Cloud Data Architecture Patterns
Knowledge check
1. According to the lesson, what is the recommended practical answer to the build vs. buy decision?
2. Why does the lesson suggest that open table formats (Iceberg, Delta Lake) and open compute frameworks (Spark) are valuable?
3. According to the lesson, what is the most common actual reason organizations end up running multi-cloud architectures?
4. Select ALL statements that reflect the case for BUYING vendor data products (as opposed to building).
Select all the correct answers.
5. Select ALL correct statements about cloud data infrastructure as presented in the lesson.
Select all the correct answers.
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. The 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 schemaschemaA schema is the formal blueprint that defines how data is structured, named, typed, and related within a database, file, or message.View full definition →, 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 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.
These actions are compiled in the role's Playbook.
Cloud data costs are non-trivial and easy to underestimate. The main cost drivers:
At Spotify, a dedicated "data platform cost optimization" team cut cloud spend by 30% without reducing functionality, through query optimization rules, data retention policies, and compute scheduling. A CDO who doesn't own cost architecture doesn't really own the data platform.
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.
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
Réponse: C