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Comparison15 min readFebruary 1, 2026

AWS vs GCP vs Azure for Data Platforms

Side-by-side comparison of cloud data services: storage, compute, orchestration, streaming, and ML across the Big Three.

Choosing your cloud data platform

AWS, GCP, and Azure each offer 50+ data services. The challenge isn't whether a service exists — it's picking the right combination for your workload, budget, and team skills. This guide compares the three clouds across five platform layers.

Object storage

AWS S3 — the gold standard. 11 nines durability, intelligent tiering, and S3 Select for in-place querying. Every AWS service integrates natively.

GCP Cloud Storage — comparable durability and pricing. Autoclass replaces lifecycle policies with ML-driven tiering. Tighter BigQuery integration than S3 has with Redshift.

Azure Blob Storage — ADLS Gen2 builds hierarchical namespaces on top of Blob, making it the natural choice for Databricks and Synapse workloads. NFS 3.0 support is unique.

Data warehousing

AWS Redshift — Serverless mode eliminates cluster management. RA3 instances separate compute/storage. AQUA caches accelerate dashboard queries. Redshift Spectrum queries S3 directly.

GCP BigQuery — truly serverless, slot-based pricing, built-in ML (BQML), and BI Engine for sub-second dashboards. Best for ad-hoc analytics and teams that dislike cluster tuning.

Azure Synapse — unified analytics workspace combining serverless SQL pools, dedicated pools, and Spark pools. Deep Power BI integration. Best for Microsoft-centric enterprises.

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Stream processing

AWS Kinesis — Data Streams for ingestion, Data Analytics (managed Flink) for processing. Kinesis Firehose delivers to S3/Redshift with zero code.

GCP Dataflow — managed Apache Beam with auto-scaling. Unified batch and stream processing model. Pairs with Pub/Sub for ingestion.

Azure Event Hubs — Kafka-compatible ingestion at millions of events/sec. Stream Analytics provides SQL-based real-time processing. Integrates with Cosmos DB change feed.

Orchestration

AWS: Step Functions (serverless workflows), MWAA (managed Airflow), Glue Workflows (ETL-specific).

GCP: Cloud Composer (managed Airflow), Workflows (lightweight serverless orchestration).

Azure: Data Factory (low-code ETL + orchestration), Synapse Pipelines (integrated), Logic Apps (event-driven).

ML & AI

AWS SageMaker — end-to-end ML platform with Studio notebooks, training jobs, endpoints, and now JumpStart foundation models. Bedrock for managed LLM access.

GCP Vertex AI — unified ML platform with AutoML, custom training, model registry, and Gemini API for generative AI. Tight integration with BigQuery ML.

Azure ML — designer UI, automated ML, responsible AI dashboard. Azure OpenAI Service provides GPT-4, DALL-E, and Whisper with enterprise security controls.

Which cloud should you choose?

  • AWS for maximum service breadth and ecosystem maturity. Default choice if you have no existing cloud commitment.
  • GCP for analytics-heavy workloads, BigQuery power, and ML/AI-first teams.
  • Azure for Microsoft-centric enterprises, Power BI integration, and hybrid (on-prem + cloud) scenarios.

Most organizations end up multi-cloud by acquisition or team preference. Visualize your cross-cloud architecture with Datadef to keep track of which services live where.