Comparison tables between AWS Redshift and Athena and Azure Synapse Analytics

CriterionAmazon RedshiftAmazon AthenaAzure Synapse Analytics
Data StorageUses its own cluster storage. Ideal for large, stable datasets.Queries data directly from Amazon S3. Suitable for a wide range of file sizes and formats.Utilizes Azure Data Lake Storage and other storage options for large-scale data analytics.
PerformanceOptimized for complex queries over large datasets. Performance can be scaled by adjusting the cluster size.Performance depends on the data format, compression, and partitioning in S3. Good for quick, ad-hoc queries.Offers on-demand query performance or provisioned resources, supporting large-scale analytics workloads.
Use CaseSuitable for enterprise-level data warehousing solutions requiring complex joins, frequent data updates, and persistent storage.Ideal for ad-hoc querying and analysis of data stored in S3 without the need for a persistent database.A comprehensive analytics service that combines big data and data warehousing for real-time analytics across large volumes of data.
PricingBased on the number and type of nodes in your cluster. Costs accrue as long as the cluster is running.Charged based on the amount of data scanned by each query. Cost-effective for sporadic querying.Pricing varies based on the provisioned resources or on-demand queries, offering flexibility between performance and cost.
ManagementRequires setting up and managing clusters, including scaling resources to handle workloads.Serverless; no infrastructure management required. Users can focus on querying data.Integrates various analytics and data management tools into a unified platform, reducing management overhead.
IntegrationIntegrates with AWS services for data ingestion, streaming, and ETL processes. Best used within AWS ecosystem.Easily integrates with S3 for storage, making it versatile for data that is already stored or ingested into S3.Seamlessly integrates with other Azure services, offering a broad ecosystem for data ingestion, ETL, and AI.
Query LanguageUses SQL, allowing for complex queries, including window functions and temporary tables.Uses standard SQL for querying, making it accessible without extensive setup.Supports T-SQL for queries, offering comprehensive data analysis capabilities and compatibility with SQL Server.
MaintenanceRequires cluster maintenance, backups, and scaling operations.No maintenance overhead; fully managed by AWS.Managed service with reduced maintenance overhead, especially when using serverless options.
Service Integration– AWS Data Pipeline for ETL
– Amazon QuickSight for BI – AWS Glue for data cataloging
– Amazon Kinesis for data streaming
– Amazon S3 for storage
– AWS Glue for data catalog
– Amazon QuickSight for visualization
– Azure Data Factory for ETL
– Power BI for BI and visualization
– Azure Data Lake Storage for big data storage
– Azure Machine Learning for AI and ML workflows






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