Azure to AWS Data Engineering Bridge
Translate your existing knowledge of ADLS Gen2, Azure Synapse, Event Hubs, ADF, and Entra ID directly into Amazon S3, Redshift, Athena, Kinesis, Glue, and AWS IAM.
Data Engineering Translation Tracks
Select a domain below to enter the Synchronized Side-by-Side Learning Studio.
Data Lake & Object Storage
From ADLS Gen2 POSIX Hierarchical Namespaces to Amazon S3 high-throughput flat object prefixes.
In Azure, ADLS Gen2 with HNS gives you real physical directories and atomic O(1) renames. In AWS S3, there are NO physical folders! 'Folders' are simply virtual string prefixes with slashes. Renaming a 'folder' in S3 requires copying and deleting every single object (O(N) operations). However, S3 scales throughput per prefix (3,500 PUTs / 5,500 GETs/sec per unique prefix), so wide partitioning distributes load automatically.
Data Warehousing & SQL Lakehouse Analytics
From Azure Synapse 60 fixed distributions to Amazon Redshift slices & serverless Athena queries.
Azure Synapse divides every Dedicated SQL Pool into exactly 60 fixed distributions regardless of cluster size. Amazon Redshift divides data across compute node slices (e.g. 2 to 16 slices per node depending on instance type). In Redshift, distribution styles are `DISTSTYLE KEY`, `ALL`, `EVEN`, or `AUTO`, and sorting is governed by `SORTKEY` zone maps (which act like Synapse Columnstore Indexes). For Synapse Serverless SQL (`OPENROWSET`), the exact architectural twin is Amazon Athena!
Real-Time Event Streaming & Ingestion
From Azure Event Hubs Throughput Units & Capture to Amazon Kinesis Shards & Firehose.
Both Event Hubs and Kinesis share the same append-only partitioned log architecture. In Azure, you size namespaces using Throughput Units (1 TU = 1 MB/s in, 2 MB/s out) or dedicated Processing Units (PUs). In AWS Kinesis, you size streams by Shards (1 Shard = 1 MB/s in, 2 MB/s out) or select On-Demand mode. For Event Hubs Capture, AWS provides Amazon Kinesis Data Firehose, which can also convert incoming JSON to Parquet on the fly!
Workflow Orchestration & Data Integration
From Azure Data Factory (ADF) visual pipelines to AWS Glue, Step Functions & MWAA.
In Azure, ADF does everything in one studio: visual ETL transformations (Mapping Data Flows), pipeline DAG orchestration, and hybrid on-premises connectivity (Self-Hosted IR). In AWS, these responsibilities are modular: AWS Glue provides serverless Spark ETL and the central Data Catalog; AWS Step Functions coordinates multi-step DAG workflows; and Amazon MWAA provides enterprise Apache Airflow Python DAGs.
Security, IAM & Identity Governance
From Microsoft Entra ID & Managed Identities to AWS IAM Roles & Instance Profiles.
In Azure, identity is centralized in Microsoft Entra ID, and permissions inherit hierarchically from Subscription down to Resource Group. In AWS, every AWS Account is an independent security boundary. Applications running in AWS do not use passwords; they assume an IAM Role with an attached JSON policy via AWS STS (Security Token Service). In AWS SDKs, the Boto3 credential chain automatically resolves credentials in order (Environment ➔ ~/.aws/credentials ➔ IAM Instance Profile), exactly like `DefaultAzureCredential()` in Azure!