Cloud data warehousing has become the foundation of modern analytics. Organizations are moving away from on-premise data centers toward scalable, managed solutions that handle petabytes of data without the infrastructure headaches. The two dominant players in this space are Amazon Redshift and Microsoft Azure Synapse Analytics.
Choosing between these platforms isn’t straightforward. Both offer powerful capabilities, but they differ significantly in architecture, pricing, and ecosystem integration. The right choice depends on your existing cloud investments, workload types, and long-term analytics strategy.
This guide is for data engineers evaluating platform options, CTOs making infrastructure decisions, and analysts who need to understand the technical tradeoffs. We break down each platform’s strengths, limitations, and ideal use cases so you can make an informed decision.
TL;DR: AWS Redshift delivers consistent performance for high-volume, predictable analytics workloads with straightforward cluster-based pricing. Azure Synapse offers more flexibility with serverless options and stronger Microsoft ecosystem integration. Choose Redshift if you’re AWS-native with steady workloads. Choose Synapse if you’re Microsoft-centric with variable analytics demands.
Key Takeaways:
Redshift uses cluster-based architecture with reserved pricing saving up to 75% on 3-year terms. Best for predictable, heavy structured data workloads . Synapse combines dedicated pools, serverless SQL, and Spark in one platform. Best for mixed workloads and organizations standardized on Power BI . Both platforms meet enterprise security and compliance requirements (SOC, PCI, HIPAA, GDPR). Implementation differs based on cloud ecosystem. Migration complexity depends on source system. SQL Server moves easier to Synapse. PostgreSQL expertise transfers well to Redshift. Hidden costs matter. Redshift charges for concurrency scaling and idle clusters. Synapse serverless costs can spike with unoptimized queries. Amazon Redshift launched in 2012 as AWS’s fully managed cloud data warehouse. It has since become one of the most widely adopted data warehousing solutions, powering analytics for tens of thousands of organizations worldwide. Redshift is purpose-built for running complex analytical queries against structured data at scale.
Fully managed infrastructure: AWS handles provisioning, patching, backups, and maintenance, letting teams focus on analytics rather than operations.Deep AWS ecosystem integration: Redshift connects natively with S3, Glue, Lambda, SageMaker, and other AWS services for end-to-end data pipelines .Columnar storage architecture: Data is stored in columns rather than rows, enabling faster aggregations and analytical queries on large datasets.Massive parallel processing: Queries are distributed across multiple nodes simultaneously, reducing execution time for complex analytics.Azure Synapse Analytics, formerly SQL Data Warehouse , represents Microsoft’s unified approach to enterprise analytics. Launched in its current form in 2019, Synapse combines data warehousing, big data processing, and data integration into a single platform. It is designed for organizations that need both SQL-based analytics and Apache Spark workloads.
Unified analytics service: Synapse brings together data warehousing, data lakes, and big data analytics under one roof, eliminating the need for separate platforms.Flexible compute options: Choose between dedicated SQL pools for predictable workloads, serverless SQL for ad-hoc queries, or Apache Spark pools for big data processing.Native Microsoft integration: Synapse connects seamlessly with Power BI, Azure Data Factory, Azure Machine Learning , and the broader Microsoft ecosystem.Code-free data pipelines: Built-in data integration capabilities allow teams to build ETL workflows without extensive coding through a visual interface.
AWS Redshift vs Azure Synapse: Core Feature Comparison 1. Architecture The architectural differences between Redshift and Synapse reflect their underlying design philosophies. Redshift follows a traditional cluster-based model optimized for structured data warehousing. Synapse takes a more flexible approach with multiple compute engines that can be mixed based on workload requirements.
Redshift cluster model: Redshift uses a leader node that manages connections and query planning, with compute nodes that store data and execute queries in parallel.RA3 nodes with managed storage: Redshift’s RA3 instances separate compute from storage, allowing independent scaling and using S3 for managed storage with local SSD caching.Redshift Spectrum: Query data directly in S3 without loading it into Redshift tables, extending your warehouse to the data lake.
Synapse dedicated SQL pools: Pre-provisioned compute resources for predictable, high-performance data warehousing workloads with consistent performance.Synapse serverless SQL: Query data in Azure Data Lake without provisioning infrastructure, paying only for data processed per query.Synapse Spark pools: Run Apache Spark workloads for big data processing, machine learning, and data engineering alongside SQL analytics.Both platforms deliver strong performance for analytical workloads, but they optimize for different scenarios. Redshift excels at structured SQL queries on large datasets. Synapse offers more flexibility for mixed workloads but requires careful configuration to achieve optimal performance.
Query optimization engines: Redshift uses a cost-based query optimizer with automatic workload management. Synapse leverages the SQL Server optimizer with adaptive query processing.Parallel processing: Both platforms distribute queries across multiple nodes, but Redshift’s MPP architecture is specifically tuned for data warehouse patterns.Concurrency scaling: Redshift automatically adds transient capacity during peak demand, handling virtually unlimited concurrent queries without performance degradation.Result caching: Both platforms cache query results to accelerate repeated queries. Redshift caches at the leader node level, while Synapse caches within dedicated pools.Materialized views: Both support materialized views for pre-computing expensive aggregations, significantly improving dashboard and reporting performance.3. Scalability Scalability approaches differ significantly between the two platforms. Redshift offers elastic resize and concurrency scaling within its cluster model. Synapse provides more granular control with its separation of compute and storage across multiple engine types.
Redshift elastic resize: Add or remove nodes in minutes to handle changing workloads, though some resizing operations may cause brief interruptions.Redshift concurrency scaling: Automatically spin up additional clusters during peak periods to maintain query performance without manual intervention.Synapse compute independence: Scale dedicated SQL pools up or down without affecting stored data, pausing compute entirely when not in use to save costs.Synapse serverless auto-scale: Serverless SQL automatically scales resources based on query complexity, requiring no capacity planning.Storage scalability: Both platforms offer virtually unlimited storage. Redshift uses managed storage with RA3 nodes, while Synapse leverages Azure Data Lake Storage.
4. Pricing Model Pricing structures differ substantially, making direct cost comparisons challenging. Redshift uses a more traditional compute-hour model, while Synapse offers multiple pricing options depending on which compute engines you use.
Redshift on-demand pricing: Pay hourly rates based on node type and quantity, with no upfront commitment. Costs are predictable based on cluster size.Redshift reserved instances: Commit to one or three-year terms for 30-75% discounts compared to on-demand pricing.Synapse dedicated pool pricing: Pay per Data Warehouse Unit (DWU) hour, which bundles compute, memory, and IO resources.Synapse serverless pricing: Pay per terabyte of data processed, ideal for sporadic or unpredictable query patterns.Storage costs: Redshift managed storage costs approximately $0.024 per GB/month. Synapse uses Azure Data Lake pricing at $0.02-0.03 per GB/month depending on tier.Data transfer costs: Both platforms charge for data transfer out of the cloud region, which can add up for heavy data export workloads.5. Data Integration Integration capabilities determine how easily each platform fits into your broader data architecture. Redshift integrates deeply with AWS services, while Synapse connects natively with the Microsoft ecosystem.
Redshift + S3: Native integration allows direct loading from S3 using COPY commands and querying S3 data through Spectrum.Redshift + AWS Glue: Serverless ETL service that catalogs data and transforms it before loading into Redshift.Redshift + Lambda: Trigger serverless functions based on Redshift events for real-time data processing and notifications.Synapse + Azure Data Factory: Enterprise ETL service with 90+ connectors for ingesting data from virtually any source.Synapse + Power BI: Direct integration enables live queries and automated dataset refreshes without data movement.Synapse + Azure Data Lake: Query data lake files directly using serverless SQL or load them into dedicated pools for better performance.6. Security Both platforms provide enterprise-grade security features. The choice often depends on your existing security infrastructure and compliance requirements rather than capability gaps.
Encryption at rest: Redshift uses AWS KMS for key management. Synapse uses Azure Key Vault. Both support customer-managed keys.Encryption in transit: Both platforms encrypt all data in transit using TLS/SSL by default.Network isolation: Redshift supports VPC deployment and private endpoints. Synapse offers VNet integration and private link connections.Role-based access control: Both platforms provide granular permissions at database, schema, table, and column levels.Row-level security: Both support row-level security policies to restrict data access based on user attributes.Compliance certifications: Both hold SOC 1/2/3, ISO 27001, HIPAA, PCI DSS, and FedRAMP certifications. Synapse adds additional Microsoft compliance frameworks.AWS Redshift vs Azure: Ecosystem and Integrations Platform choice affects how easily you connect analytics infrastructure to the rest of your technology stack.
Your existing tools matter more than feature lists. A platform that connects seamlessly to your current BI tools , machine learning frameworks, and data sources reduces implementation time and ongoing maintenance.
BI Tools Compatibility: Both platforms support Tableau, Looker, and most major BI tools through standard connectors. You won’t face compatibility issues with mainstream visualization tools regardless of which platform you choose. Power BI has native, optimized integration with Synapse. DirectQuery mode allows real-time dashboard updates without data duplication, and Azure AD authentication flows seamlessly between services. QuickSight integrates natively with Redshift. This combination offers a cost-effective alternative to third-party BI tools for organizations fully committed to AWS. Synapse supports DirectQuery for real-time Power BI dashboards. This eliminates the need for scheduled data refreshes and ensures executives always see current numbers. Machine Learning Integration: Redshift connects to SageMaker for ML model training and inference. Data scientists can build models on Redshift data without complex export processes or data movement. Synapse integrates with Azure Machine Learning for automated ML. Business analysts can run predictions without writing code using the AutoML capabilities built into the platform. Both support Python and R for in-database analytics. Running analytics code where the data lives reduces transfer overhead and speeds up iterative analysis workflows. Redshift ML brings ML predictions directly into SQL queries. Analysts can call machine learning models using familiar SQL syntax without switching tools or learning new frameworks. Third-Party Tool Support: Redshift has broader third-party connector ecosystem due to longer market presence. Most data tools built Redshift connectors first, resulting in more mature and battle-tested integrations. Synapse’s connector library is growing rapidly. Microsoft’s market push means new tools increasingly prioritize Synapse compatibility alongside Redshift. Both support standard JDBC/ODBC connections. Any tool that connects to databases through standard protocols will work with minimal configuration on either platform. dbt, Fivetran, and Airbyte work with both platforms. The modern data stack tools your engineering team likely prefers integrate equally well with Redshift and Synapse. Partner with Kanerika to Modernize Your Enterprise Operations with High-Impact Data & AI Solutions Call or Text Us Now
AWS Redshift vs Azure: Ease of Use Setup complexity and ongoing management burden affect both implementation timelines and long-term operational costs.
Setup Process Amazon Redshift:
Launch a cluster in minutes through the console. The guided setup walks you through node selection, networking, and security configuration without requiring deep AWS expertise. Configure node types, cluster size, and networking. Choosing the right configuration upfront matters because resizing later involves some downtime and planning. Load data using COPY commands or AWS Glue. The COPY command handles parallel loading from S3 efficiently, while Glue provides visual ETL for more complex transformations. Redshift Serverless simplifies setup for smaller workloads. Teams can start querying data immediately without capacity planning or cluster management overhead. Azure Synapse:
Create a Synapse workspace as the central hub. The workspace organizes all your analytics assets including databases, pipelines, and notebooks in one manageable location. Provision dedicated or serverless SQL pools as needed. You can start with serverless for exploration and add dedicated pools later as workloads stabilize. Use Synapse Studio for integrated development experience. SQL scripts, Spark notebooks, and data pipelines all live in one browser-based interface with version control integration. Linked services connect to external data sources. Pre-built connectors to Azure services and third-party systems reduce the integration code you need to write and maintain. Management Experience Amazon Redshift:
Automatic backups and maintenance windows. Snapshots happen continuously, and you can restore to any point in the retention period without manual intervention. Query performance insights identify optimization opportunities. The console highlights slow queries and suggests distribution keys or sort keys to improve performance. Advisor recommendations suggest configuration improvements. Automated analysis flags underutilized resources, missing statistics, and other optimization opportunities. CloudWatch integration for monitoring and alerting. Set up dashboards tracking query throughput, storage usage, and cluster health alongside your other AWS resources. Azure Synapse:
Synapse Studio provides unified management interface. Monitor queries, manage security, and develop pipelines without switching between multiple Azure portal blades. Built-in monitoring dashboards track query performance. Visualizations show query duration trends, resource utilization, and bottlenecks without additional configuration. Azure Monitor integrates with existing Azure alerting. Teams already using Azure monitoring tools can add Synapse metrics to existing dashboards and alert rules. Automatic pause and resume for dedicated pools. Configure inactivity timeouts to stop billing during nights and weekends without manual intervention. Learning Curve Amazon Redshift:
PostgreSQL-based SQL feels familiar to most data professionals. If your team knows PostgreSQL or any standard SQL, they can write Redshift queries on day one. AWS ecosystem knowledge helps but isn’t required. You can operate Redshift independently, though understanding S3, IAM, and VPC concepts improves your architecture decisions. Extensive documentation and community resources available. AWS’s documentation depth and Stack Overflow coverage mean most questions have answered examples already. Azure Synapse:
T-SQL syntax familiar to SQL Server users. Organizations with SQL Server history can migrate queries with minimal modification and leverage existing team expertise. Synapse Studio combines multiple tools in one interface. The learning curve is steeper initially but pays off by reducing context switching between separate applications. Microsoft Learn provides structured training paths. Free, role-based learning paths guide data engineers and analysts through platform capabilities systematically. Existing Azure or SQL Server experience transfers well. Teams already managing Azure resources or SQL Server databases adapt to Synapse faster than starting from scratch. Is Microsoft Fabric Data Analytics Right for Your Team in 2026? A guide to Microsoft Fabric for modern data analytics , workflows, and business insights.
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AWS Redshift vs Azure: Use Case Comparison Platform strengths align with specific organizational contexts and workload characteristics.
Best for AWS Redshift Organizations already invested in AWS infrastructure gain the most from Redshift’s native integrations. Data flows seamlessly between S3, Glue, and Redshift without complex connector configurations.
Heavy structured data workloads: Financial transaction processing, healthcare claims analysis, retail sales reporting. Redshift’s columnar storage and MPP architecture handle billion-row tables efficiently with consistent query performance.Predictable analytics patterns: Nightly batch processing, scheduled report generation, regular dashboard updates. Reserved instance pricing rewards consistent usage with up to 75% savings compared to on-demand rates.Multi-cloud strategies: Broader third-party tool support suits organizations avoiding vendor lock-in. Redshift’s PostgreSQL foundation and mature connector ecosystem integrate well with non-AWS services.Existing PostgreSQL expertise: Familiar syntax reduces training time and migration complexity. Teams can transfer skills directly without learning new query languages or management paradigms.Best for Azure Synapse Microsoft-centric organizations benefit from seamless integration with existing tools and identity management. Teams already using Power BI, Azure Active Directory, and Office 365 face minimal adoption friction.
Mixed workloads: Organizations needing both traditional SQL analytics and big data processing. Synapse’s unified platform handles structured reporting and Spark-based data science without separate infrastructure.Variable usage patterns: Seasonal businesses, project-based analytics, experimental workloads. Serverless pricing means you pay nothing during quiet periods and scale instantly when demand spikes.Power BI standardization: Native integration delivers better performance and simpler administration. DirectQuery connections, single sign-on, and optimized data transfer make the combination work smoothly out of the box.Unified analytics needs: Teams wanting data warehousing, data lakes, and Spark in one platform. Synapse eliminates the complexity of managing separate systems for different analytics workloads.Pricing Comparison Table Understanding total cost requires looking beyond compute pricing. Storage, data transfer, and hidden costs significantly impact long-term expenses.
Cost Category AWS Redshift Azure Synapse Compute (on-demand) $0.25-$13.04 per hour depending on node type $1.20-$360 per hour depending on DWU level Compute (serverless) Redshift Serverless: $0.36-$0.45 per RPU hour $5 per TB processed Storage $0.024 per GB/month (managed storage) $0.02-$0.03 per GB/month (ADLS) Backup storage Free up to cluster size, then $0.024/GB Free for 7-day retention, then standard storage rates Data transfer out $0.09 per GB (first 10TB) $0.087 per GB (first 10TB) Concurrency scaling Same as on-demand compute Included in dedicated pool pricing Spectrum queries $5 per TB scanned N/A (use serverless SQL)
Migration Considerations Switching from On-Premise Warehouse Migrating from on-premise systems like Teradata, Oracle, or SQL Server requires careful planning regardless of target platform. Both Redshift and Synapse offer migration tools and services.
Schema conversion: AWS Schema Conversion Tool supports Redshift migrations. Azure Database Migration Service handles Synapse conversions.Data transfer methods: Both support offline transfer via physical devices (AWS Snowball, Azure Data Box) for multi-petabyte migrations.Code compatibility: SQL Server workloads migrate more easily to Synapse due to T-SQL compatibility. Oracle/Teradata may require more refactoring for either platform.Testing requirements: Plan for parallel running periods where both old and new systems operate simultaneously to validate results.Performance tuning: On-premise query patterns may need optimization for cloud architecture . Budget time for performance testing and tuning.Moving Between AWS and Azure Cross-cloud migrations are complex and typically driven by strategic platform consolidation rather than feature differences.
Data transfer costs: Egress charges from the source cloud can be substantial for large datasets. Plan for $0.05-$0.09 per GB.Schema differences: While both use SQL, DDL syntax and data type mappings differ. Expect schema conversion effort.ETL pipeline rebuilding: Pipelines built with cloud-native tools (Glue, Data Factory) need complete rebuilding on the target platform.Skill transition: Teams need training on the new platform’s tools, monitoring, and best practices.Phased approach: Consider migrating workloads incrementally rather than attempting a complete cutover to reduce risk.Data Transfer Challenges Moving large datasets between platforms or from on-premise systems presents common challenges regardless of direction.
Network bandwidth: Multi-terabyte transfers can take days or weeks over standard internet connections. Consider dedicated connections or physical transfer.Data validation: Implement row counts, checksums, and sample comparisons to verify data integrity after transfer.Incremental sync: For ongoing migrations, set up change data capture to keep source and target synchronized during transition.Downtime planning: Determine acceptable downtime windows and plan cutover activities accordingly.Rollback strategy: Maintain the ability to revert to the source system if critical issues emerge post-migration.
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FAQs What is the main difference between AWS Redshift and Azure Synapse? AWS Redshift uses a dedicated cluster architecture optimized for consistent, high-volume analytics workloads. Azure Synapse offers a hybrid approach with both dedicated pools and serverless options. Redshift excels at predictable workloads with steady usage patterns. Synapse provides more flexibility for variable workloads and integrates natively with Microsoft tools like Power BI and Azure Active Directory.
Can I migrate from Redshift to Synapse or vice versa? Yes, but migration requires careful planning. Schema translation between PostgreSQL (Redshift) and T-SQL (Synapse) needs attention. Query syntax differs in areas like window functions and date handling. Data transfer costs add up for large datasets. Most organizations run parallel environments during validation. Budget 3-6 months for enterprise migrations including testing and optimization.
How do Redshift and Synapse handle real-time data? Both platforms support near real-time analytics but through different approaches:
Redshift uses streaming ingestion from Kinesis Data Firehose Synapse connects to Event Hubs and supports Stream Analytics integration Neither replaces dedicated streaming platforms for millisecond latency requirements Both work best for micro-batch patterns with seconds-to-minutes latency Is AWS Redshift faster than Azure Synapse? Performance comparisons depend heavily on workload type, data distribution, and optimization. Benchmark tests show comparable results when both platforms are properly tuned. Redshift often edges ahead on complex joins across large tables. Synapse performs well on mixed workloads combining SQL and Spark. Your specific query patterns matter more than generic benchmarks.
Can I use both Redshift and Synapse together? Yes, some organizations run both platforms for different use cases. Common patterns include Redshift for core data warehousing and Synapse for Microsoft-integrated analytics. Third-party tools like Fivetran and dbt work with both platforms. Data sharing between clouds adds complexity and transfer costs. Most organizations eventually consolidate to reduce operational overhead.
How long does implementation typically take? Implementation timelines vary based on complexity:
Basic setup: 1-2 weeks for either platform Data migration: 4-8 weeks depending on volume and source systems Query optimization: 2-4 weeks for performance tuning Full production deployment: 3-6 months for enterprise implementations Factor in team training and integration testing for accurate planning
