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AWSAZURE

AWS to Azure Data Engineering Bridge

Translate your existing knowledge of Amazon S3, Redshift, Athena, Kinesis, and Glue into ADLS Gen2, Azure Synapse, Event Hubs, and ADF.

Source Cloud
AWS (Known)
Target Cloud
AZURE (Mastery)
Focus Domain
Data Engineering
Comparison Depth
5 Core Modules

Data Engineering Translation Tracks

Select a domain below to enter the Synchronized Side-by-Side Learning Studio.

Data Lake & Object Storage

From Amazon S3 flat object keys to ADLS Gen2 POSIX Hierarchical Namespaces.

AWS (You Know)
Amazon Simple Storage Service (S3)
AZURE (You Learn)
Azure Data Lake Storage Gen2 (ADLS Gen2)
The Key Paradigm Shift:

In AWS S3, folders are virtual prefixes with slashes in their string names. In ADLS Gen2 with HNS, folders are real directory trees. Renaming a directory with 100,000 files in S3 takes 100,000 CopyObject and DeleteObject calls; in ADLS Gen2, it takes 5 milliseconds!

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Data Warehousing & SQL Analytics

From Amazon Redshift & Athena to Azure Synapse MPP & Serverless T-SQL.

AWS (You Know)
Amazon Redshift & Amazon Athena
AZURE (You Learn)
Azure Synapse Analytics
The Key Paradigm Shift:

In AWS, you choose between Amazon Redshift (MPP cluster) or Amazon Athena (serverless Presto over S3). In Azure Synapse, both capabilities live in the exact same workspace: Dedicated SQL Pools (MPP like Redshift) and Serverless SQL Pools (pay-per-TB lake queries like Athena).

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Real-Time Streaming & Event Ingestion

From Amazon Kinesis Data Streams to Azure Event Hubs.

AWS (You Know)
Amazon Kinesis Data Streams
AZURE (You Learn)
Azure Event Hubs
The Key Paradigm Shift:

Kinesis and Event Hubs are architectural cousins! Both use the partitioned append-only log model with partition keys. The major advantage in Azure Event Hubs is its built-in native Apache Kafka protocol endpoint, allowing standard Kafka clients to connect without proxy layers.

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Data Integration & ETL Orchestration

From AWS Glue & Step Functions to Azure Data Factory (ADF).

AWS (You Know)
AWS Glue & AWS Step Functions
AZURE (You Learn)
Azure Data Factory (ADF)
The Key Paradigm Shift:

In AWS, ETL and orchestration are split between AWS Glue (Spark data processing) and AWS Step Functions (state machine orchestration). Azure Data Factory combines both: visual pipeline orchestration DAGs + visual Mapping Data Flows (Spark) in one tool.

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IAM Roles, Service Principals & App Access

From AWS IAM Users, Access Keys & Roles to Microsoft Entra ID Service Principals & DefaultAzureCredential.

AWS (You Know)
AWS IAM (Users, Access Keys & Roles)
AZURE (You Learn)
Microsoft Entra ID (Azure AD) & Service Principals
The Key Paradigm Shift:

In AWS, applications authenticate using an IAM User with an Access Key/Secret or assume an IAM Role via STS. In Azure, you create an 'App Registration & Service Principal' with a 'Client Secret' (or use `az login` locally). In Python, `DefaultAzureCredential()` gives you the exact same automatic multi-environment discovery as the AWS Boto3 credentials chain!

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