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This article compares analytics services on Amazon Web Services (AWS) and Azure. It covers data ingestion and orchestration, data lakes, batch processing, data warehouses, stream processing, governance, sharing, and business intelligence.
For operational relational and non-relational data stores, see Database services on Azure and AWS. For machine learning and application AI services, see AI and machine learning services on Azure and AWS.
Platform approaches
AWS provides individual analytics services that you compose into a solution. Microsoft Fabric provides integrated Data Factory, Data Engineering, Data Warehouse, Real-Time Intelligence, Data Science, and Power BI workloads over OneLake. Azure also provides standalone services, including Azure Data Factory, Azure Databricks, Azure Event Hubs, Azure Stream Analytics, and Azure Data Explorer.
An integrated platform isn't automatically the right choice for every workload. Compare source and format support, required engines and APIs, network isolation, regional availability, governance boundaries, operational control, existing skills, and pricing. Fabric capacity is shared across Fabric workloads, while standalone Azure services have their own capacity and billing models.
Data integration and orchestration
Use data integration services to ingest, copy, transform, and orchestrate data across operational systems and analytical stores.
| AWS service | Azure service | Selection guidance |
|---|---|---|
| AWS Glue | Data Factory in Microsoft Fabric or Azure Data Factory | All three provide managed data integration. Compare connectors, transformation engines, private networking, runtime placement, orchestration features, and integration with the target analytics platform. |
| Amazon Managed Workflows for Apache Airflow | Apache Airflow jobs in Microsoft Fabric | Both run Apache Airflow workflows. Validate supported Airflow versions, package installation, networking, identity, scaling, and migration requirements. |
| AWS Database Migration Service | Azure Database Migration Service or Fabric Migration Assistant for Data Warehouse | These tools have different scopes. Use Azure Database Migration Service for supported database source-target pairs. Use Fabric Migration Assistant to move supported SQL schemas and data into Fabric Data Warehouse. |
| Amazon AppFlow | Data Factory connectors or Azure Logic Apps | Use Data Factory for analytical ingestion and transformation. Use Logic Apps for application and business-process integration. Verify connector operations, authentication, and data-volume requirements. |
Data lakes and lakehouses
Amazon S3 commonly provides the storage layer for AWS data lakes. On Azure, Azure Data Lake Storage provides object storage with a hierarchical namespace. OneLake is the logical data lake built into Microsoft Fabric.
| AWS service | Azure service | Selection guidance |
|---|---|---|
| Amazon S3 | Azure Data Lake Storage | Both provide durable object storage for analytical data. Compare namespace behavior, security, lifecycle management, replication, ecosystem integration, and data-transfer costs. |
| Amazon S3 | Microsoft OneLake | OneLake is part of Fabric and provides shared storage for Fabric workloads. OneLake isn't a general replacement for every S3 workload. Use it when Fabric is the analytics platform and its capacity, region, security, and workload model meet your requirements. |
| AWS Lake Formation | OneLake security and Microsoft Purview in Fabric | AWS Lake Formation manages access to S3-based data lakes. Fabric separates storage permissions, item permissions, and governance capabilities. Map authorization and governance requirements instead of assuming a single-service equivalent. |
| Amazon S3 external data | OneLake shortcuts | Shortcuts reference supported data in S3, Azure Data Lake Storage, Google Cloud Storage, and other locations without copying it. Use data movement instead when you need complex transformations, custom scheduling, or a destination outside OneLake. |
Batch data engineering
| AWS service | Azure service | Selection guidance |
|---|---|---|
| Amazon EMR | Azure Databricks | Both support managed Apache Spark and other data engineering workloads. Compare runtime compatibility, cluster control, autoscaling, governance, notebooks, jobs, and ecosystem integrations. |
| Amazon EMR and AWS Glue interactive sessions | Fabric Data Engineering | Fabric Data Engineering provides managed Spark, notebooks, environments, and jobs integrated with OneLake. It isn't a direct replacement for every EMR framework or cluster configuration. Validate runtime, library, networking, and performance requirements. |
| AWS Glue Studio | Fabric Data Factory or Azure Data Factory | These services provide visual and code-based data transformation experiences. Select based on transformation engine, orchestration, source and target support, and operating model. |
Data warehouses and SQL analytics
| AWS service | Azure service | Selection guidance |
|---|---|---|
| Amazon Redshift | Fabric Data Warehouse | Both provide distributed SQL data warehouses. Compare SQL compatibility, workload isolation, scaling, ingestion, data sharing, concurrency, governance, and pricing. Fabric Data Warehouse operates over OneLake and consumes Fabric capacity. |
| Amazon Redshift | Azure Databricks SQL | Consider Azure Databricks SQL when the workload uses a lakehouse architecture and Databricks is the primary data platform. It isn't a relational engine replacement for every Redshift workload. |
| Amazon Redshift Spectrum | Fabric lakehouse, OneLake shortcuts, and Direct Lake | Redshift Spectrum queries data in S3. Fabric provides separate storage, compute, and semantic-model capabilities. Direct Lake requires supported Delta tables and Fabric capacity. Review source formats and capacity guardrails. |
| Amazon Athena | Fabric lakehouse SQL analytics endpoint or Azure Databricks SQL | Athena is serverless SQL over data in S3. Azure options differ in storage, metadata, compute, and billing. Select based on data location, SQL compatibility, latency, concurrency, and governance. |
Real-time analytics
Separate event ingestion from stream processing and analytical storage when you compare services. A Kafka-compatible endpoint doesn't provide every Apache Kafka broker feature.
| AWS service | Azure service | Selection guidance |
|---|---|---|
| Amazon Kinesis Data Streams | Azure Event Hubs | Both ingest and retain high-throughput event streams for independent consumers. Compare partitioning, retention, replay, throughput, protocols, and scaling. |
| Amazon Managed Service for Apache Flink | Azure Stream Analytics or Fabric eventstreams | These services process streams but use different languages and execution models. Stream Analytics uses a SQL-based query language. Fabric eventstreams provides ingestion, no-code transformations, and routing within Fabric. |
| Amazon Managed Streaming for Apache Kafka | Event Hubs for Apache Kafka | Amazon MSK runs open-source Apache Kafka. Event Hubs provides a Kafka-compatible endpoint and isn't an Apache Kafka cluster. Validate protocol features, transactions, compression, Kafka Streams support, retention, quotas, and tier requirements. |
| Amazon OpenSearch Service | Azure Data Explorer or Fabric Eventhouse | Use Azure Data Explorer or Eventhouse for log, telemetry, and time-series analytics with Kusto Query Language. OpenSearch also provides full-text search capabilities, so these services aren't complete replacements for search workloads. |
| Amazon Timestream | Azure Data Explorer or Fabric Eventhouse | These services support time-series analytics. Compare ingestion protocols, query language, retention, tiering, update behavior, and integration requirements. |
Business intelligence and data sharing
| AWS service | Azure service | Selection guidance |
|---|---|---|
| Amazon QuickSight | Power BI | Both provide semantic modeling, reports, dashboards, sharing, and embedded analytics. Compare licensing, capacity, identity, governance, authoring, and embedding requirements. |
| Amazon Managed Grafana | Azure Managed Grafana | Both provide managed Grafana. Compare data-source integrations, authentication, private networking, high availability, plugins, and pricing tiers. |
| AWS Data Exchange | Microsoft Marketplace and Fabric external data sharing | AWS Data Exchange combines a data marketplace with delivery. Microsoft Marketplace and Fabric data sharing address different parts of that workflow. Fabric external data sharing provides governed cross-tenant access for supported Fabric items but isn't a data marketplace. |
Data governance
Microsoft Purview and a combination of AWS services provide data discovery, classification, lineage, access governance, and compliance capabilities. AWS commonly combines Glue Data Catalog, Lake Formation, Amazon Macie, AWS Identity and Access Management, and AWS Config. Microsoft Purview provides a unified catalog and governance capabilities across supported Azure, Fabric, on-premises, multicloud, and software as a service sources.
Governance coverage depends on the source and feature. Verify scanning support, lineage collection, policy enforcement, sensitivity labels, network access, and regional availability for every data source in the architecture.
Migration considerations
Don't migrate an analytics platform as a list of one-to-one service substitutions. Inventory data sources, formats, table types, pipelines, orchestration dependencies, transformation code, query dialects, semantic models, reports, security policies, data residency, service-level objectives, and operational processes. Use representative performance and cost tests before you select target services.