| Criterion | Amazon Redshift | Amazon Athena | Azure Synapse Analytics |
|---|---|---|---|
| Data Storage | Uses 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. |
| Performance | Optimized 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 Case | Suitable 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. |
| Pricing | Based 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. |
| Management | Requires 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. |
| Integration | Integrates 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 Language | Uses 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. |
| Maintenance | Requires 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 |
Comparison tables between AWS Redshift and Athena and Azure Synapse Analytics
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