Data and AI
This article compares the core Azure data and AI services to the corresponding Amazon Web Services (AWS) services.
- For links to articles that compare other AWS and Azure services, see Azure for AWS professionals.
- For a complete comparison of Azure services with AWS services, see AWS to Azure services comparison.
Data governance, management, and platforms
Both Microsoft Purview and the combination of AWS services described in the following table aim to provide comprehensive data governance solutions. These solutions enable organizations to effectively manage, discover, classify, and provide security for their data assets.
| Microsoft service | AWS services | Description |
|---|---|---|
| Microsoft Purview | AWS Glue Data Catalog, AWS Lake Formation, Amazon Macie, AWS Identity and Access Management (IAM), AWS Config | Both options provide robust data governance, cataloging, and compliance features. Microsoft Purview is a unified data governance solution that allows organizations to discover, classify, and manage data across on-premises, multicloud, and SaaS environments. It also provides data lineage and compliance capabilities. AWS provides similar functionalities with multiple services: AWS Glue Data Catalog for metadata management, AWS Lake Formation for data lake creation and governance, Amazon Macie for data classification and protection, AWS IAM for access control, and AWS Config for configuration management and compliance tracking. |
All-in-one platform vs. AWS services
Microsoft Fabric provides an all-in-one platform that unifies the data and AI services required for modern analytics solutions. It streamlines the process of moving data between services, provides unified governance and security, and simplifies pricing models. This unified approach contrasts with the AWS approach, in which services are often used separately and require more effort to integrate. Fabric provides seamless integration across these functions that can help your organization accelerate your data-driven initiatives in the Azure ecosystem.
Both AWS and Fabric provide services for data integration, processing, analytics, machine learning, and business intelligence.
| AWS services | Fabric | Description |
|---|---|---|
| AWS Glue, AWS Data Pipeline | Data integration with Azure Data Factory | AWS provides a suite of individual services that can be combined to build data and analytics solutions. This approach provides flexibility but requires more effort to integrate the services into an end-to-end solution. Fabric provides these capabilities within a single unified platform to simplify workflows, collaboration, and management. |
Detailed comparison of AWS services with Fabric components
Data integration and ETL tools
Data integration and extract, transform, load (ETL) tools help you extract, transform, load data from multiple sources into a unified system for analysis.
| AWS service | Azure service | Analysis |
|---|---|---|
| AWS Glue | Data Factory | AWS Glue and Azure Data Factory are fully managed ETL services that facilitate data integration across various sources. |
| Amazon Managed Workflows for Apache Airflow (MWAA) | Data Factory with Azure Synapse Analytics pipelines | Apache Airflow provides managed workflow orchestration for complex data pipelines. Azure Synapse Analytics pipelines integrate Apache Airflow with Azure Data Factory for a more integrated experience. AWS MWAA is a managed Airflow solution. |
| AWS Data Pipeline | Data Factory | AWS Data Pipeline and Azure Data Factory enable the movement and processing of data across services and locations. |
| AWS Database Migration Service (DMS) | Azure Database Migration Service | These services can help you migrate databases to the cloud with minimal downtime. The main difference is that the Azure service is optimized for seamless migration to Azure databases, providing assessment and recommendation tools, whereas AWS DMS focuses on migrations within the AWS environment. AWS DMS provides ongoing replication features for hybrid architectures. |
| Amazon AppFlow | Azure Logic Apps | These services enable automated data flows between cloud applications and services without requiring code. Logic Apps provides extensive integration capabilities with a wide range of connectors and a visual designer. AppFlow focuses on secure data transfer between specific SaaS applications and AWS services and provides built-in data transformation features. |
| AWS Step Functions | Data Factory with Logic Apps | These services provide workflow orchestration for coordinating distributed applications and microservices. Step Functions is designed for orchestrating AWS services and microservices in serverless applications. Logic Apps is used for both data integration and enterprise workflow automation. |
Data warehousing
These solutions are designed to store and manage large volumes of structured data that's optimized for querying and reporting.
| AWS service | Azure service | Analysis |
|---|---|---|
| Amazon Redshift | Azure Synapse Analytics | Amazon Redshift and Azure Synapse Analytics are fully managed, petabyte-scale data warehousing services that are designed for large-scale data analytics and reporting. The main difference is that Azure Synapse Analytics provides a unified analytics platform that combines data warehousing and big data processing, whereas Redshift focuses primarily on data warehousing. |
| Amazon Redshift Spectrum | Azure Synapse Analytics with Data Lake integration | These services enable you to query data across data warehouses and data lakes without moving data. Azure Synapse Analytics provides integrated SQL and Spark engines. Redshift Spectrum extends Redshift's SQL querying to data in Amazon S3. |
| AWS Lake Formation | Azure Synapse Analytics with Azure Data Lake Storage | These services can help you create secure data lakes for analytics. Azure combines data lake and data warehouse functionalities in Azure Synapse Analytics. AWS provides Lake Formation for data lakes and Redshift as a separate data warehouse service. |
| Amazon RDS with Redshift Federated Query | Azure SQL Database | These services support querying across operational databases and data warehouses. Azure Synapse Analytics provides a unified, built-in analytics experience. AWS requires you to combine RDS and Redshift for similar cross-service querying capabilities. |
| Amazon Aurora with Redshift integration | Azure Synapse Link for Azure Cosmos DB | These services provide high-performance analytics over operational data. AWS requires that you set up data pipelines between Aurora and Redshift. With Azure Synapse Link, you don't need to move data. |
Data lake solutions
These platforms store vast amounts of raw unstructured and structured data in its native format for future processing.
| AWS service | Azure service | Analysis |
|---|---|---|
| Amazon S3 | Azure Data Lake Storage | Amazon S3 and Azure Data Lake Storage are scalable storage solutions for building data lakes to store and analyze large volumes of data. Data Lake Storage provides a hierarchical namespace. Amazon S3 uses a flat structure. |
| AWS Lake Formation | Azure Synapse Analytics | AWS Lake Formation and Azure Synapse Analytics can help you set up, manage, and secure data lakes for analytics. The main difference is that Azure Synapse Analytics provides an all-in-one analytics service that combines data lake, data warehouse, and big data analytics, whereas Lake Formation focuses on streamlining data lake creation and management with robust security and governance features. |
| Amazon Athena | Azure Synapse Analytics serverless SQL pools | These services enable you to query data that's stored in data lakes by using SQL, without setting up infrastructure. Amazon Athena is a standalone solution that integrates with other AWS services. Serverless SQL pools are part of the Azure Synapse Analytics platform. |
| AWS Glue Data Catalog | Microsoft Purview | These services provide a centralized metadata repository for storing and managing data schemas and metadata for data lakes. AWS Glue provides a subset of the Microsoft Purview features. Microsoft Purview supports data cataloging, lineage tracking, and sensitive data classification, whether the data resides on-premises, in a cloud, or in a SaaS application. |
Big data analytics
These services process and analyze large and complex datasets to uncover patterns, insights, and trends. The following table provides direct comparisons of individual big data services. Microsoft Fabric is an all-in-one service for big data and analytics. It provides the following services and more.
| AWS service | Azure service | Analysis |
|---|---|---|
| Amazon EMR | Azure HDInsight | Both services provide managed big data frameworks for processing data that's stored in data lakes. EMR provides managed Hadoop and Spark frameworks. HDInsight is a fully managed enterprise solution that supports Hadoop, Spark, Kafka, and other open source analytics. |
| Amazon EMR | Azure Databricks | These services enable big data processing via Apache Spark in a managed environment. EMR enables you to run Apache Spark clusters with flexible configuration and scaling options. Azure Databricks provides an optimized Apache Spark platform with collaborative notebooks and integrated workflows. |
| Amazon Kinesis | Azure Event Hubs and Azure Stream Analytics | These services provide real-time data streaming and analytics for processing and analyzing high-volume data streams. |
| AWS Glue with AWS Glue Studio | Azure Synapse Analytics with Apache Spark pools | Both services provide big data processing capabilities with integrated data transformation and analytics. |
Business intelligence and reporting
These services provide data visualization, reporting, and dashboards to help businesses make informed decisions.
| AWS service | Azure service | Analysis |
|---|---|---|
| Amazon QuickSight | Power BI | QuickSight and Power BI provide business analytics tools for data visualization and interactive dashboards. |
| Amazon Managed Grafana | Azure Managed Grafana | These services provide managed Grafana, which enables you to visualize metrics, logs, and traces across multiple data sources. |
| AWS Data Exchange | Azure Data Share | These services facilitate the secure sharing and exchange of data between organizations. Data Exchange provides a marketplace model. Data Share focuses on cross-tenant data sharing. |
| Amazon OpenSearch Service with Kibana | Azure Data Explorer with dashboards | These services provide real-time data exploration and interactive analytics over large volumes of data. OpenSearch uses Kibana for search and visualization. Azure Data Explorer uses Kusto, which is optimized for fast data ingestion and querying. |
Real-time data processing
These systems ingest and analyze data as it's generated to provide immediate insights and responses.
| AWS service | Azure service | Analysis |
|---|---|---|
| Amazon Kinesis | Azure Event Hubs and Azure Stream Analytics | These services provide real-time data streaming and analytics for processing and analyzing high-volume data streams. Kinesis provides an integrated suite for data streaming and analytics within AWS. Azure separates ingestion (Event Hubs) and processing (Stream Analytics). |
| Amazon Managed Streaming for Apache Kafka (MSK) | Azure HDInsight with Apache Kafka | These services provide managed Apache Kafka clusters for creating real-time streaming data pipelines and applications. |
| AWS Lambda | Azure Functions | These serverless compute platforms run code in response to events and automatically manage the underlying compute resources. |
| Amazon DynamoDB Streams | Azure Cosmos DB change feed | These services enable real-time data processing by capturing and providing a stream of data modifications. |
| Amazon ElastiCache with Redis streams | Azure Cache for Redis with Redis streams | These services provide managed Redis instances that support Redis streams for real-time data ingestion and processing. |
| Amazon IoT Analytics | Azure IoT Hub with Azure Stream Analytics | These services enable you to process and analyze data from IoT devices in real time. AWS IoT Analytics provides built-in data storage and analysis capabilities. Azure provides modular services: IoT Hub handles ingestion, and Stream Analytics processes the data. |
Machine learning services
These tools and platforms enable the development, training, and deployment of machine learning models.
| AWS service | Azure service | Analysis |
|---|---|---|
| Amazon SageMaker | Azure Machine Learning | These comprehensive platforms enable you to build, train, and deploy machine learning models. |
| AWS Deep Learning AMIs | Azure Data Science Virtual Machines | These services provide preconfigured virtual machines that are optimized for machine learning and data science workloads. |
| Amazon SageMaker Autopilot | Automated machine learning (AutoML) | These services provide automated machine learning for building and training models. |
| Amazon SageMaker Studio | Azure Machine Learning studio | These services provide integrated development environments for machine learning. SageMaker Studio provides a unified interface for all machine learning development steps, including debugging and profiling tools. |
AI services
AI services provide prebuilt, customizable AI capabilities to applications, including vision, speech, language, and decision making.
| AWS service | Azure service | Analysis |
|---|---|---|
| Amazon Rekognition | Azure AI Vision with OCR and AI | These services provide image and video analysis capabilities, including object recognition and content moderation. |
| Amazon Polly | Azure AI Speech (text-to-speech) | You can use these services to convert text into lifelike speech to enable applications to interact with users with natural-sounding voices. |
| Amazon Transcribe | Azure AI Speech | These services convert spoken language into text, which enables applications to transcribe audio streams. |
| Amazon Translate | Azure AI Translator | These services provide machine translation capabilities for translating text from one language to another. |
| Amazon Comprehend | Azure AI Language | These services analyze text to extract insights like sentiment, key phrases, entities, and language detection. |
| Amazon Lex | Azure AI Bot Service | You can use these services to create conversational interfaces and chatbots that use natural language understanding. Azure provides a modular approach with separate services for the bot development framework and language understanding. Amazon Lex provides an integrated solution for building conversational interfaces within AWS. |
| Amazon Textract | Azure AI Document Intelligence | Both of these services automatically extract text and data from scanned documents and forms by using machine learning. Azure provides customizable models for specific document types, which enables tailored data extraction. Textract provides out-of-the-box extraction of complex data structures. |
| Amazon OpenSearch Service | Azure AI Search (generative search) | OpenSearch and AI Search provide powerful search and analytics capabilities. You can use them for common AI patterns, like retrieval-augmented generation (RAG). |
Generative AI services
These AI services create new content or data that resembles human-generated output, like text, images, or audio.
| AWS service | Azure services | Analysis |
|---|---|---|
| Amazon Bedrock | Azure OpenAI Service, Azure AI Studio | Amazon Bedrock, Azure AI Studio, and Azure OpenAI Service provide foundation models for creating and deploying generative AI applications. |
Contributors
This article is maintained by Microsoft. It was originally written by the following contributors.
Principal author:
- Regina Hackenberg | Senior Technical Specialist
Other contributor:
- Adam Cerini | Director, Partner Technology Strategist
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