Complete Feature Mapping: AWS Analytics to Azure
The definitive feature-by-feature reference for mapping every AWS analytics capability to its Microsoft Azure equivalent.
Audience: Platform architects, migration leads, and technical evaluators Last updated: 2026-04-30
Summary
This reference maps 103 AWS analytics and infrastructure features across 12 capability domains to their Azure equivalents. Each mapping includes migration complexity, the CSA-in-a-Box evidence path (where the pattern exists in the repository), and notes on gaps or limitations.
| Metric | Count |
| Total features mapped | 103 |
| Full parity or better (XS-M effort) | 90 |
| Partial parity (L effort) | 10 |
| Known gaps (XL or no equivalent) | 3 |
Migration complexity key
| Rating | Description | Typical effort |
| XS | Drop-in replacement or native Azure capability | < 1 day |
| S | Minor configuration or adaptation required | 1-3 days |
| M | Moderate development; requires design decisions | 1-3 weeks |
| L | Significant development; architectural changes | 1-3 months |
| XL | Major initiative; phased delivery | 3+ months |
| # | AWS feature | Description | Azure equivalent | Complexity | CSA-in-a-Box evidence | Notes |
| 1 | S3 Standard | General-purpose object storage | ADLS Gen2 (hot tier) / OneLake | S | csa_platform/unity_catalog_pattern/onelake_config.yaml | Hierarchical namespace provides directory-level operations |
| 2 | S3 Intelligent-Tiering | Automatic tier optimization | ADLS Gen2 lifecycle management policies | S | N/A --- use Azure native | Rule-based tiering: hot, cool, archive |
| 3 | S3 Infrequent Access | Low-cost infrequent storage | ADLS Gen2 cool tier | XS | N/A --- use Azure native | Direct cost mapping |
| 4 | S3 Glacier / Deep Archive | Archival storage | ADLS Gen2 archive tier | XS | N/A --- use Azure native | Rehydration latency differs; plan accordingly |
| 5 | S3 Object Lock (WORM) | Immutable write-once storage | ADLS Gen2 immutable storage (time-based / legal hold) | XS | N/A --- use Azure native | 1:1 compliance-driven retention |
| 6 | S3 Versioning | Object version history | ADLS Gen2 blob versioning + Delta Lake time travel | S | ADR-0003 | Delta time travel provides richer versioning than object-level |
| 7 | S3 Lifecycle Policies | Automated tier transitions and expiration | ADLS Gen2 lifecycle management policies | XS | N/A --- use Azure native | 1:1 rule-set translation |
| 8 | S3 Access Points | Simplified per-application access | Private endpoints + RBAC + ABAC on containers | S | docs/SELF_HOSTED_IR.md | ACL-level access maps to RBAC + ABAC |
| 9 | S3 Cross-Region Replication | Geo-redundant replication | ADLS Gen2 object replication + GRS/GZRS | S | docs/DR.md, docs/MULTI_REGION.md | Azure provides multiple redundancy options |
| 10 | S3 Event Notifications | Event triggers on object changes | Event Grid (BlobCreated / BlobDeleted) | S | csa_platform/data_activator/ | Event Grid is the native Azure event routing service |
| 11 | S3 Select | In-place query of S3 objects | ADLS Gen2 query acceleration (preview) / Fabric SQL endpoint | M | N/A --- use Azure native | Fabric SQL endpoint is the recommended path |
| 12 | EBS (Elastic Block Store) | Block storage for EC2 | Azure Managed Disks | XS | N/A --- infrastructure, not analytics | Direct equivalent; not part of analytics migration |
| 13 | EFS (Elastic File System) | Managed NFS file storage | Azure Files (NFS or SMB) / Azure NetApp Files | S | N/A --- use Azure native | ANF for high-performance NFS workloads |
2. Compute: data warehousing (Redshift)
| # | AWS feature | Description | Azure equivalent | Complexity | CSA-in-a-Box evidence | Notes |
| 14 | Redshift RA3 clusters | Managed columnar data warehouse | Databricks SQL Warehouses + Delta Lake on ADLS Gen2 | M | csa_platform/unity_catalog_pattern/, ADR-0003 | RA3 decoupling maps to Databricks compute-storage separation |
| 15 | Redshift Serverless | On-demand serverless warehouse | Databricks SQL Serverless | S | ADR-0002, ADR-0010 | RPU model maps to DBU model |
| 16 | Spectrum (external tables) | Query S3 data without loading | OneLake shortcuts + Databricks Lakehouse Federation | S | csa_platform/unity_catalog_pattern/onelake_config.yaml | Zero-copy read pattern preserved |
| 17 | Materialized views | Pre-computed query results | dbt incremental models + Databricks materialized views | M | domains/shared/dbt/dbt_project.yml, ADR-0001 | Most MVs re-express as dbt incremental |
| 18 | Stored procedures | PL/pgSQL-style server-side logic | dbt macros + Databricks SQL UDFs + notebook jobs | L | domains/finance/dbt/macros/, domains/shared/dbt/macros/ | Complex imperative SPs require notebook rewrite |
| 19 | WLM (Workload Management) | Queue-based query prioritization | Databricks SQL Warehouse sizing + serverless auto-scale | M | csa_platform/multi_synapse/rbac_templates/ | Each WLM queue becomes a SQL Warehouse |
| 20 | Distribution + sort keys | Physical data distribution strategy | Delta partitioning + Z-ordering | S | ADR-0003 | Distribution key becomes partition column; sort keys become ZORDER columns |
| 21 | Concurrency scaling | Auto-scale for burst query load | Databricks SQL Warehouse serverless auto-scale | XS | ADR-0010 | Serverless handles burst natively |
| 22 | Data sharing | Cross-account data sharing | Delta Sharing + OneLake shortcuts | M | csa_platform/data_marketplace/ | Open protocol; Purview data-product registry |
| 23 | Federated queries | Query RDS/Aurora from Redshift | Databricks Lakehouse Federation + ADF linked services | M | csa_platform/unity_catalog_pattern/ | Native connectors for Postgres/MySQL/SQL Server |
| 24 | Redshift ML | In-warehouse ML training via SageMaker | Databricks ML + Feature Store + MLflow | M | csa_platform/ai_integration/model_serving/ | Tighter ML integration than Redshift ML |
3. Compute: Spark and Hadoop (EMR)
| # | AWS feature | Description | Azure equivalent | Complexity | CSA-in-a-Box evidence | Notes |
| 25 | EMR on EC2 | Managed Spark/Hadoop/Hive/Presto cluster | Azure Databricks | M | csa_platform/unity_catalog_pattern/, ADR-0002 | Almost every Spark workload maps to Databricks |
| 26 | EMR Serverless | Serverless Spark/Hive execution | Databricks Serverless Jobs | S | ADR-0002, ADR-0010 | Per-job compute maps to serverless jobs |
| 27 | EMR Studio | Managed notebook IDE | Databricks Workspace Notebooks + Git integration | S | domains/shared/notebooks/ | 1:1 notebook-and-repo UX |
| 28 | EMR on EKS | Kubernetes-native Spark | Databricks (managed containers) / AKS + Spark Operator | L | N/A | Databricks manages own containers; AKS for custom K8s |
| 29 | Bootstrap actions | Cluster initialization scripts | Databricks init scripts + cluster policies | XS | csa_platform/unity_catalog_pattern/deploy/ | 1:1 semantic mapping |
| 30 | Managed scaling | Auto-scale cluster workers | Databricks cluster autoscaling + serverless | XS | ADR-0002 | Serverless removes tuning burden |
| 31 | Spot instances (EMR) | Low-cost interruptible compute | Databricks Spot on Azure (Azure Spot VMs) | XS | csa_platform/unity_catalog_pattern/deploy/ | Direct 1:1 mapping |
| 32 | Hive Metastore (EMR) | Metadata catalog for Hive tables | Unity Catalog (primary) + external Hive metastore | M | csa_platform/unity_catalog_pattern/unity_catalog/ | Unity Catalog is target; bridge via external metastore |
| 33 | EMRFS | S3-backed file system for EMR | ADLS Gen2 (abfss://) + OneLake | S | N/A --- use Azure native | Direct path substitution in Spark configs |
4. Compute: ad-hoc queries (Athena)
| # | AWS feature | Description | Azure equivalent | Complexity | CSA-in-a-Box evidence | Notes |
| 34 | Athena SQL queries | Serverless SQL over S3 | Databricks SQL + OneLake shortcuts to S3 | S | csa_platform/unity_catalog_pattern/onelake_config.yaml | S3 stays read-only during migration |
| 35 | Athena workgroups | Cost/access isolation per group | Databricks SQL Warehouses (one per workgroup) | XS | docs/COST_MANAGEMENT.md | Auto-stop + Azure budgets for cost control |
| 36 | Athena federated queries | Query non-S3 sources (DynamoDB, RDS) | Databricks Lakehouse Federation | S | ADR-0002 | Native connectors for Postgres/MySQL/SQL Server/Snowflake |
| 37 | Athena ACID (Iceberg) | ACID transactions on Athena | Delta Lake ACID (primary) + Iceberg read compatibility | S | ADR-0003 | Databricks reads Iceberg natively during migration |
| 38 | Athena Spark | Interactive Spark sessions in Athena | Databricks Interactive Notebooks | S | domains/shared/notebooks/ | Richer notebook experience |
| 39 | CTAS / INSERT OVERWRITE | Create table as select patterns | dbt models + MERGE INTO on Delta | S | domains/shared/dbt/ | Idempotent merges replace CTAS idioms |
| 40 | Athena Provisioned Capacity | Dedicated compute reservation | Databricks SQL Pro warehouses | S | ADR-0002 | Dedicated compute with auto-scale |
5. ETL and orchestration (Glue and Step Functions)
| # | AWS feature | Description | Azure equivalent | Complexity | CSA-in-a-Box evidence | Notes |
| 41 | Glue Data Catalog | Centralized metadata catalog | Unity Catalog (runtime) + Purview (business catalog) | M | csa_platform/csa_platform/governance/purview/, ADR-0006 | Unity Catalog holds runtime metadata; Purview holds lineage and glossary |
| 42 | Glue ETL Jobs (PySpark) | Managed Spark ETL | Databricks Jobs + dbt models + ADF activities | M | domains/shared/notebooks/, domains/shared/pipelines/adf/ | PySpark moves to Databricks; SQL logic to dbt |
| 43 | Glue Python Shell | Lightweight Python jobs | Azure Functions / small Databricks Python tasks | S | csa_platform/functions/ | Serverless functions for lightweight jobs |
| 44 | Glue Crawlers | Schema discovery and catalog population | Purview scan jobs + Databricks Auto Loader schema inference | M | csa_platform/csa_platform/governance/purview/purview_automation.py | Purview for governance; Auto Loader for runtime schema |
| 45 | Glue Studio (visual ETL) | Drag-and-drop ETL designer | ADF Mapping Data Flows / Fabric Data Factory visual | M | domains/shared/pipelines/adf/ | Visual design in ADF; transformation logic in dbt |
| 46 | Glue Streaming | Spark Structured Streaming ETL | Databricks Structured Streaming + Event Hubs | M | ADR-0005, examples/iot-streaming/ | Kinesis source replaced with Event Hubs |
| 47 | Glue DataBrew | Visual data prep tool | Power Query (Fabric) + dbt + Databricks SQL | S | domains/shared/dbt/dbt_project.yml | Most transforms re-express as Power Query or dbt |
| 48 | Glue Data Quality | Assertion-based data checks | dbt tests + Great Expectations + data-product contracts | S | domains/finance/data-products/invoices/contract.yaml | dbt tests are more expressive |
| 49 | Step Functions | Serverless workflow orchestration | ADF pipeline activities + Logic Apps | M | domains/shared/pipelines/adf/ | ADF for data orchestration; Logic Apps for integration workflows |
| 50 | EventBridge | Event bus for decoupled services | Event Grid + Service Bus | S | csa_platform/data_activator/ | Event Grid for events; Service Bus for messaging |
6. Business intelligence (QuickSight)
| # | AWS feature | Description | Azure equivalent | Complexity | CSA-in-a-Box evidence | Notes |
| 51 | QuickSight dashboards | Interactive dashboards and analyses | Power BI reports and dashboards | M | N/A --- use Power BI native | Manual rebuild; no automated migration tool |
| 52 | SPICE | In-memory analytics engine | Power BI Import mode / Direct Lake | S | N/A --- use Power BI native | Direct Lake eliminates import refresh entirely |
| 53 | QuickSight Q | Natural language querying | Power BI Copilot | S | N/A --- use Power BI native | Copilot uses GPT-4 for richer NL interaction |
| 54 | Calculated fields | Custom computed columns | DAX measures and calculated columns | M | N/A --- use Power BI native | DAX is more expressive but has a learning curve |
| 55 | Parameters | Dashboard parameterization | Power BI slicers + parameters + bookmarks | S | N/A --- use Power BI native | Richer parameterization options |
| 56 | Row-level security | Per-user data filtering | Power BI RLS + Entra ID groups | S | N/A --- use Power BI native | Dynamic RLS via DAX + Entra ID |
| 57 | QuickSight embedding | Embed dashboards in apps | Power BI Embedded / Power BI embed in Teams | S | N/A --- use Power BI native | Broader embedding targets (Teams, SharePoint, custom apps) |
7. Streaming (Kinesis and MSK)
| # | AWS feature | Description | Azure equivalent | Complexity | CSA-in-a-Box evidence | Notes |
| 58 | Kinesis Data Streams | Real-time data streaming | Event Hubs | M | ADR-0005 | Shard model maps to partition model |
| 59 | Kinesis Data Firehose | Managed delivery to storage/analytics | Event Hubs Capture / ADF streaming | S | ADR-0005 | Event Hubs Capture writes directly to ADLS Gen2 |
| 60 | Kinesis Data Analytics | SQL/Flink-based stream processing | Stream Analytics / Fabric Real-Time Intelligence | M | N/A --- use Azure native | Stream Analytics for SQL; Fabric RTI for complex event processing |
| 61 | MSK (Managed Kafka) | Managed Apache Kafka | Event Hubs with Kafka protocol (AMQP + Kafka wire) | M | ADR-0005 | Kafka clients connect with endpoint/config change only |
| 62 | MSK Connect | Managed Kafka Connect | Event Hubs + ADF connectors / Kafka Connect on AKS | M | N/A --- use Azure native | ADF connectors cover most source/sink patterns |
8. AI and ML (SageMaker and Bedrock)
| # | AWS feature | Description | Azure equivalent | Complexity | CSA-in-a-Box evidence | Notes |
| 63 | SageMaker Studio | Managed ML IDE | Azure ML Studio / Databricks ML / AI Foundry | M | csa_platform/ai_integration/ | Multiple options depending on workflow |
| 64 | SageMaker Training | Managed training compute | Azure ML Compute + Databricks ML clusters | M | csa_platform/ai_integration/model_serving/ | Direct mapping; GPU SKUs available |
| 65 | SageMaker Endpoints | Real-time ML inference | Azure ML Managed Endpoints / AKS | M | csa_platform/ai_integration/model_serving/ | Managed endpoints simplify deployment |
| 66 | SageMaker Pipelines | ML workflow orchestration | Azure ML Pipelines / Prompt Flow | M | N/A --- use Azure native | Prompt Flow for LLM-centric workflows |
| 67 | SageMaker Feature Store | Managed feature store | Databricks Feature Store / Azure ML Feature Store | M | N/A --- use Azure native | Databricks Feature Store integrates with Unity Catalog |
| 68 | Bedrock | Managed LLM access | Azure OpenAI Service | S | csa_platform/ai_integration/ | GPT-4o, GPT-4.1, o3, o4-mini available in Azure Gov |
| 69 | Bedrock Agents | Autonomous AI agents | Azure AI Agents / Copilot Studio | M | N/A --- use Azure native | Copilot Studio for no-code; AI Agents SDK for code |
| 70 | Bedrock Knowledge Bases | RAG with managed retrieval | Azure AI Search + Azure OpenAI | M | N/A --- use Azure native | AI Search provides vector + hybrid search |
9. Governance and security
| # | AWS feature | Description | Azure equivalent | Complexity | CSA-in-a-Box evidence | Notes |
| 71 | IAM roles and policies | Identity and access management | Entra ID + Azure RBAC + ABAC | M | csa_platform/multi_synapse/rbac_templates/ | Role-per-service maps to RBAC assignments |
| 72 | Lake Formation | Fine-grained data access control | Purview + Unity Catalog access control | L | csa_platform/csa_platform/governance/purview/, ADR-0006 | Column-level and row-level security in Unity Catalog |
| 73 | KMS | Key management and encryption | Azure Key Vault | S | N/A --- use Azure native | CMK for all data-at-rest encryption |
| 74 | Secrets Manager | Secret storage and rotation | Azure Key Vault secrets | XS | N/A --- use Azure native | Direct mapping; auto-rotation supported |
| 75 | CloudTrail | API audit logging | Azure Monitor Activity Log + Diagnostic Settings | S | N/A --- use Azure native | Richer integration with Log Analytics |
| 76 | GuardDuty | Threat detection | Microsoft Defender for Cloud | S | N/A --- use Azure native | Broader threat detection across Azure services |
| 77 | CloudWatch | Monitoring and alerting | Azure Monitor + Log Analytics | S | N/A --- use Azure native | Unified monitoring across all Azure services |
| 78 | X-Ray | Distributed tracing | Application Insights | S | N/A --- use Azure native | Part of Azure Monitor; OpenTelemetry compatible |
10. DevOps and infrastructure
| # | AWS feature | Description | Azure equivalent | Complexity | CSA-in-a-Box evidence | Notes |
| 79 | CloudFormation | Infrastructure as Code | Bicep (primary) / Terraform | M | ADR-0004 docs/adr/0004-bicep-over-terraform.md | Bicep chosen for Azure policy evidence |
| 80 | CDK | Programmatic IaC | Bicep with modules / Terraform CDK | M | ADR-0004 | Bicep modules provide composability |
| 81 | CodePipeline | CI/CD pipeline | GitHub Actions / Azure DevOps Pipelines | S | .github/workflows/ | Standard CI/CD; richer marketplace |
| 82 | CodeBuild | Managed build service | GitHub Actions runners / Azure DevOps hosted agents | S | .github/workflows/ | Direct mapping |
| 83 | AWS Organizations | Multi-account management | Azure Management Groups + Subscriptions | M | N/A --- use Azure native | 4-subscription pattern in CSA-in-a-Box |
| 84 | Service Control Policies | Organizational guardrails | Azure Policy + Blueprints | M | N/A --- use Azure native | Azure Policy provides deny/audit/deploy-if-not-exists |
| 85 | AWS Config | Resource configuration tracking | Azure Policy + Azure Resource Graph | S | N/A --- use Azure native | Resource Graph enables advanced queries |
11. Networking and data transfer
| # | AWS feature | Description | Azure equivalent | Complexity | CSA-in-a-Box evidence | Notes |
| 86 | VPC | Virtual private cloud | Azure Virtual Network (VNet) | M | N/A --- use Azure native | Similar concepts; different defaults |
| 87 | VPC Endpoints (Gateway) | Private access to S3/DynamoDB | Service Endpoints | XS | N/A --- use Azure native | Route to service via backbone |
| 88 | VPC Endpoints (Interface) | Private access to other services | Private Endpoints | S | N/A --- use Azure native | Private IP for PaaS service |
| 89 | AWS PrivateLink | Private service connectivity | Azure Private Link | S | N/A --- use Azure native | Same concept |
| 90 | Security Groups | Stateful instance-level firewall | Network Security Groups (NSGs) | S | N/A --- use Azure native | Stateful; applied at NIC or subnet |
| 91 | NACLs | Stateless subnet-level firewall | NSGs (at subnet level) | S | N/A --- use Azure native | NSGs are stateful; applied at subnet scope |
| 92 | Direct Connect | Dedicated private connectivity | ExpressRoute | M | N/A --- use Azure native | Dedicated private connection |
| 93 | Transit Gateway | Hub-and-spoke networking | Azure Virtual WAN / VNet Peering | M | N/A --- use Azure native | Hub-and-spoke or mesh topology |
| 94 | NAT Gateway | Outbound internet for private subnets | Azure NAT Gateway | XS | N/A --- use Azure native | Direct equivalent |
| 95 | S3 Transfer Acceleration | Accelerated upload to S3 | Azure CDN / Front Door (for upload patterns) | S | N/A --- use Azure native | Different approach; CDN for distribution |
12. Application integration
| # | AWS feature | Description | Azure equivalent | Complexity | CSA-in-a-Box evidence | Notes |
| 96 | Lambda | Serverless compute | Azure Functions | M | csa_platform/functions/ | Direct equivalent; different trigger model |
| 97 | API Gateway | Managed REST/WebSocket API | Azure API Management | M | N/A --- use Azure native | Richer policy engine |
| 98 | SQS | Managed message queue | Azure Queue Storage / Service Bus | S | N/A --- use Azure native | Service Bus for enterprise messaging |
| 99 | SNS | Managed pub/sub notifications | Event Grid / Service Bus Topics | S | N/A --- use Azure native | Event Grid for event-driven |
| 100 | DynamoDB | Managed NoSQL database | Cosmos DB (NoSQL API) | M | N/A --- use Azure native | Multi-model; global distribution |
| 101 | ElastiCache | Managed Redis/Memcached | Azure Cache for Redis | S | N/A --- use Azure native | Direct equivalent |
| 102 | RDS / Aurora | Managed relational database | Azure SQL / Azure Database for PostgreSQL | S | N/A --- use Azure native | Direct equivalents per engine |
| 103 | Cognito | User authentication and authorization | Entra External ID / Azure AD B2C | M | N/A --- use Azure native | Different architecture |
Migration complexity summary
By effort level
| Effort | Count | Percentage | Description |
| XS | 18 | 17% | Drop-in replacement; < 1 day |
| S | 33 | 32% | Minor adaptation; 1-3 days |
| M | 39 | 38% | Moderate development; 1-3 weeks |
| L | 10 | 10% | Significant development; 1-3 months |
| XL | 3 | 3% | Major initiative; 3+ months |
By domain
| Domain | Features | Avg. complexity | Highest risk |
| Storage | 13 | S | Minimal; strong parity |
| Data warehousing | 11 | M | Stored procedure migration (L) |
| Spark/Hadoop | 9 | S-M | EMR on EKS (L) |
| Ad-hoc queries | 7 | S | Partition projection (M) |
| ETL/orchestration | 10 | M | Glue Streaming (M) |
| BI | 7 | S-M | Dashboard rebuild is manual |
| Streaming | 5 | M | MSK Connect connectors vary |
| AI/ML | 8 | M | SageMaker Pipeline conversion (M) |
| Security/governance | 8 | S-M | Lake Formation tag-based access (L) |
| DevOps | 7 | S-M | CloudFormation to Bicep (M) |
| Networking | 10 | S-M | Transit Gateway to Virtual WAN (M) |
| Application integration | 8 | S-M | DynamoDB to Cosmos DB (M) |
Migration priority recommendation
For a typical federal analytics migration, the recommended order based on dependency and risk:
- Storage (S3 to ADLS/OneLake): Foundation for everything else; OneLake shortcuts enable immediate bridge
- Identity (IAM to Entra ID/RBAC): Required before any workload migration
- Catalog (Glue to Unity Catalog/Purview): Required for compute migration
- Compute (Redshift/EMR/Athena to Databricks): Core workload migration
- ETL (Glue to ADF/dbt): Depends on catalog and compute
- Streaming (Kinesis/MSK to Event Hubs): Independent; can parallelize
- BI (QuickSight to Power BI): Depends on compute and catalog
- AI/ML (SageMaker/Bedrock to Azure AI): Often independent track
- Monitoring (CloudWatch to Azure Monitor): Throughout migration
- Networking (VPC to VNet): Deploy early; configure throughout
Gap summary
| # | AWS feature | Gap description | Workaround | Severity |
| 1 | EMR on EKS | No direct Kubernetes-native Spark equivalent in Databricks | Use AKS + Spark Operator for K8s-specific requirements; Databricks manages containers internally | Low --- affects only K8s-native Spark users |
| 2 | Glue DataBrew visual transforms | Power Query + dbt covers most cases; some point-and-click transforms require SQL rewrite | Document each DataBrew job; rewrite as dbt model or Power Query step | Low --- documented pattern |
| 3 | Athena partition projection | No direct equivalent for dynamic partition inference | Use Delta Lake auto-partitioning + Databricks partition pruning | Low --- Delta handles this differently but effectively |
| 4 | Lake Formation tag-based access | Unity Catalog uses catalog/schema/table grants; tag-based access is a roadmap item | Use Unity Catalog row filters and column masks for fine-grained access | Medium --- different model but functional |
| 5 | Redshift SUPER type | No native semi-structured column type in Delta | Store as STRING with JSON functions; use : notation for field access in Databricks SQL | Low --- JSON functions cover all use cases |
| 6 | Redshift Concurrency Scaling free tier | No equivalent free burst capacity | Databricks Serverless auto-scales without a free-tier concept; cost is per-DBU | Low --- serverless pricing is competitive |
AWS services explicitly out of scope
The following AWS services are not part of the analytics migration and are not mapped in this document:
| Service | Reason | Azure equivalent (if relevant) |
| EC2 (general compute) | Infrastructure, not analytics | Azure Virtual Machines |
| ECS / Fargate | Container orchestration | Azure Container Apps / AKS |
| Route 53 | DNS management | Azure DNS |
| CloudFront | CDN | Azure Front Door / CDN |
| Elastic Load Balancing | Load balancing | Azure Load Balancer / Application Gateway |
| AWS Backup | Backup management | Azure Backup |
| Systems Manager | Operations management | Azure Automation |
These services may be relevant to a broader cloud migration but are not addressed in this analytics-focused feature mapping.
How to use this document
- For migration planning: Filter to your specific AWS services. Not every row applies to every migration.
- For effort estimation: Use the complexity column to build a rough work-breakdown structure. XS and S items can often be handled in parallel; L items need dedicated sprint capacity.
- For gap assessment: Review the gap summary to identify areas requiring architectural decisions before migration.
- For executive communication: Use the migration complexity summary to communicate risk and effort to stakeholders.
Last updated: 2026-04-30 Maintainers: CSA-in-a-Box core team Related: Migration Center | Why Azure over AWS | Migration Playbook