The 8 best Amazon Redshift alternatives for modern analytics

These are the best Amazon Redshift alternatives:

1. Tinybird
2. Snowflake
3. Google BigQuery
4. ClickHouse^® Cloud
5. Databricks
6. Azure Synapse Analytics
7. Apache Druid / Imply
8. Dremio

Amazon Redshift has been a cornerstone of AWS data infrastructure for over a decade, providing a fully-managed data warehouse for analytics and business intelligence. Its tight integration with AWS services, columnar storage, and parallel query execution make it a natural choice for organizations building on Amazon Web Services.

But Redshift isn't always the right fit. Maybe you need real-time analytics with sub-second latency instead of Redshift's typical 5-10 second query times. Perhaps you're looking to avoid AWS lock-in with multi-cloud flexibility. Or you might need simpler operations than managing Redshift clusters and workload management.

The data analytics landscape has evolved significantly beyond traditional data warehouses. Modern platforms offer everything from real-time operational analytics to serverless batch processing to multi-cloud data lakehouses, each optimized for different use cases and operational models.

In this guide, we'll explore the best alternatives to Amazon Redshift, covering real-time analytics platforms, competing data warehouses, lakehouse solutions, and specialized options.

The 8 Best Amazon Redshift Alternatives

1. Tinybird

Best for: Real-time analytics, operational dashboards, user-facing features

If Redshift's 5-10 second query latency is too slow for your use case, Tinybird offers a fundamentally different approach: managed ClickHouse^® optimized for sub-100ms real-time analytics.

Key Features:

• Sub-100ms query latency at any scale
• Managed ClickHouse^® infrastructure with automatic scaling
• Instant SQL-to-API transformation for production endpoints
• Streaming ingestion with automatic backpressure handling
• Local development with CLI-based workflows
• Native connectors for Kafka, S3, DynamoDB, Postgres, and more
• Schema iteration with zero-downtime migrations
• Tinybird Code: AI agent for query optimization
• Simple usage-based pricing without cluster management

Architecture: Tinybird is built on ClickHouse^®, a columnar analytical database designed for real-time queries. Unlike Redshift's batch-oriented architecture, Tinybird provides continuous data ingestion with immediate queryability and consistent sub-second performance.

How It Differs from Redshift: Redshift is a batch data warehouse optimized for complex analytical queries over massive historical datasets. Tinybird is a real-time analytics platform optimized for operational use cases requiring immediate insights.

Key Differences:

Query Latency:

• Redshift: 2-30 seconds
• Tinybird: <100 milliseconds

Use Case Focus:

• Redshift: Business intelligence, historical analysis, internal reporting
• Tinybird: User-facing dashboards, operational monitoring, API-backed analytics

Development Workflow:

• Redshift: SQL in AWS console or BI tools, build APIs separately
• Tinybird: Local development with CLI, SQL automatically becomes APIs

Operational Model:

• Redshift: Cluster or serverless management, workload configuration
• Tinybird: Fully managed, automatic scaling, no cluster concepts

Cloud Strategy:

• Redshift: AWS-only
• Tinybird: Available on major cloud providers

When to Choose Tinybird Over Redshift:

• You need sub-second query latency for operational use cases
• You're building user-facing analytics that customers interact with
• You need APIs serving analytics to applications
• Real-time monitoring driving immediate decisions
• Usage-based billing requiring up-to-the-minute accuracy
• Developer velocity and modern workflows are priorities
• You want to avoid cluster management and table design optimization

When Redshift Makes More Sense:

• You need complex analytical queries over petabyte-scale data
• Internal business intelligence is the primary use case
• Deep AWS integration is essential
• 5-10 second query latency is acceptable
• Your data team is experienced with Redshift

Ideal Use Cases for Tinybird:

• Customer-facing SaaS analytics
• Real-time operational monitoring
• Usage-based billing systems
• Web and product analytics
• API-backed analytics features
• Event-driven applications
• Real-time personalization

2. Snowflake

Best for: Multi-cloud data warehousing, enterprise analytics

Snowflake is the leading cloud-agnostic data warehouse, offering similar capabilities to Redshift but with multi-cloud support and different architectural choices.

Key Features:

• Multi-cloud support (AWS, Azure, GCP)
• Complete separation of storage and compute
• Zero-copy cloning and time travel
• Secure data sharing across organizations
• Support for semi-structured data (JSON, Avro, Parquet)
• Extensive data marketplace
• Per-second billing for compute

Architecture: Snowflake's multi-cluster, shared-data architecture separates storage from compute more completely than Redshift, allowing independent scaling.

How It Differs from Redshift: Snowflake is multi-cloud; Redshift is AWS-only. Snowflake's virtual warehouses are more flexible than Redshift clusters. Both are batch-oriented data warehouses with similar query latencies. Snowflake is generally simpler to operate with less table design overhead.

When to Choose Snowflake:

• Multi-cloud strategy is important
• You want simpler operations than Redshift
• Data sharing across organizations matters
• You're not committed to AWS exclusively

Ideal Use Cases:

• Enterprise business intelligence
• Cross-organizational data sharing
• Multi-cloud architectures
• Data science feature stores

3. Google BigQuery

Best for: Google Cloud users, serverless analytics

BigQuery is Google's fully-managed, serverless data warehouse that eliminates infrastructure management entirely.

Key Features:

• Truly serverless with no cluster management
• Pay-per-query pricing model
• Built-in machine learning (BigQuery ML)
• Real-time streaming ingestion
• Federated queries across sources
• Deep GCP integration

Architecture: BigQuery uses a serverless architecture that automatically parallelizes queries across thousands of workers without user configuration.

How It Differs from Redshift: BigQuery is serverless; Redshift requires cluster management (unless using Redshift Serverless). BigQuery is GCP-native; Redshift is AWS-native. BigQuery charges per query; Redshift charges for compute time. Both are batch-oriented warehouses.

When to Choose BigQuery:

• You're on Google Cloud Platform
• You want completely serverless analytics
• Pay-per-query pricing aligns with usage
• You need BigQuery ML capabilities

Ideal Use Cases:

• GCP-native analytics
• Ad-hoc exploratory analysis
• Serverless data processing
• ML integration with GCP

4. ClickHouse^® Cloud

Best for: High-performance real-time analytics

ClickHouse^® Cloud is the official managed service for ClickHouse^®, offering the same underlying database that powers Tinybird but without the platform layer.

Key Features:

• Sub-second query performance on billions of rows
• Columnar storage with extreme compression
• Real-time data ingestion
• Full SQL support with advanced functions
• Automatic scaling and high availability
• Direct cluster access

Architecture: Pure ClickHouse^® with managed infrastructure. You get the database and cloud management but need to build ingestion pipelines and API layers yourself.

How It Differs from Redshift: ClickHouse^® Cloud is optimized for real-time analytics; Redshift for batch. ClickHouse^® queries are typically 10-100x faster for analytical workloads. ClickHouse^® requires building more infrastructure yourself; Redshift has more integrated AWS tooling.

When to Choose ClickHouse^® Cloud:

• You need real-time query performance
• You want raw ClickHouse^® access
• You have engineering resources for pipelines and APIs
• Query speed is critical

Ideal Use Cases:

• Real-time analytics applications
• High-performance dashboards
• Custom analytics architectures
• Performance-critical queries

5. Databricks

Best for: Unified data engineering, analytics, and machine learning

Databricks combines data warehouse capabilities with data lake functionality and ML workflows in a lakehouse platform.

Key Features:

• Unified batch and streaming processing
• Delta Lake for ACID transactions
• Integrated ML workflows with MLflow
• Collaborative notebooks
• Multi-cloud support (AWS, Azure, GCP)
• Photon query engine

Architecture: Built on Apache Spark with Delta Lake providing warehouse features on data lakes, unifying data engineering, analytics, and ML.

How It Differs from Redshift: Databricks focuses on unifying data engineering, analytics, and ML. Redshift is SQL-first for analytics. Databricks is better for ML workflows and complex transformations. Redshift is simpler for pure analytics. Both run on AWS but Databricks is multi-cloud.

When to Choose Databricks:

• You need unified data engineering and ML platform
• Complex transformations and pipelines are required
• Machine learning workflows are important
• You want one platform for everything

Ideal Use Cases:

• Machine learning and data science
• Complex data engineering
• Unified lakehouse architectures
• Organizations wanting end-to-end platform

6. Azure Synapse Analytics

Best for: Microsoft Azure users, integrated analytics

Azure Synapse brings together data warehousing, big data processing, and data integration in Microsoft's unified analytics service.

Key Features:

• Integration with Azure ecosystem
• Serverless and dedicated SQL pools
• Apache Spark integration
• Built-in data pipelines
• Power BI integration
• T-SQL and Spark support

Architecture: Synapse provides both dedicated SQL pools (data warehouse) and Spark pools (distributed processing) with integrated data movement.

How It Differs from Redshift: Synapse is Azure-native; Redshift is AWS-native. Synapse offers both SQL warehousing and Spark in one platform. Redshift is more focused on SQL analytics. Different cloud ecosystems.

When to Choose Synapse:

• You're on Microsoft Azure
• You use Microsoft BI tools
• You need SQL and Spark together
• Azure integration is valuable

Ideal Use Cases:

• Azure-native architectures
• Power BI users
• Hybrid SQL and Spark workloads
• Microsoft ecosystem users

7. Apache Druid / Imply

Best for: High-concurrency real-time analytics

Apache Druid is a real-time OLAP database optimized for scenarios with many concurrent users. Imply provides the managed service.

Key Features:

• Optimized for extreme concurrency
• Column-oriented storage with bitmap indexes
• Sub-second queries on real-time data
• Time-series optimizations
• Lambda architecture
• Multi-tenancy support

Architecture: Distributed architecture with specialized node types designed for thousands of concurrent queries with consistent latency.

How It Differs from Redshift: Druid is real-time; Redshift is batch. Druid handles thousands of concurrent users better. Redshift handles more complex analytical queries. Different architectural philosophies.

When to Choose Druid:

• You need thousands of concurrent users
• Real-time ingestion and queries required
• Interactive dashboards with many users
• High concurrency is critical

Ideal Use Cases:

• Massive-scale user-facing dashboards
• Network telemetry
• Interactive analytics at scale
• Multi-tenant analytics

8. Dremio

Best for: Self-service analytics on data lakes

Dremio is a data lakehouse platform that queries data lakes directly without requiring data movement or transformation into warehouses.

Key Features:

• Query data lakes without ETL
• Apache Arrow-based engine
• Data reflections for acceleration
• Semantic layer for business logic
• Query federation across sources
• Eliminates data duplication

Architecture: Uses Apache Arrow for columnar processing and provides a semantic layer over data lakes, eliminating traditional ETL.

How It Differs from Redshift: Dremio queries data in place; Redshift requires loading data into the warehouse. Dremio eliminates data movement; Redshift is the destination. Different approaches to analytics.

When to Choose Dremio:

• You want to query data lakes directly
• Reducing data movement matters
• Self-service analytics is priority
• Cost-conscious analytics

Ideal Use Cases:

• Self-service BI on data lakes
• Reducing data duplication
• Multi-source federation
• Cost optimization

Understanding Amazon Redshift and Its Architecture

Before exploring alternatives, it's important to understand what Redshift provides and where it excels.

What Redshift Is: Amazon Redshift is a fully-managed, petabyte-scale data warehouse service designed for analytics and business intelligence workloads. It's part of the AWS data platform ecosystem and integrates deeply with other AWS services.

Redshift's Architecture Redshift uses a cluster-based architecture with:

• Leader node for query planning and coordination
• Compute nodes for parallel query execution
• Columnar storage with zone maps for pruning
• Massively parallel processing (MPP)
• Integration with S3 for data lake queries (Redshift Spectrum)
• Serverless option (Redshift Serverless) eliminating cluster management

Redshift's Strengths

• Deep AWS integration (S3, Glue, Lambda, EMR, SageMaker)
• Familiar SQL interface with PostgreSQL compatibility
• Mature ecosystem with extensive tooling support
• Predictable costs with reserved instances
• Redshift Spectrum for querying data lakes
• Strong performance for batch analytics

Redshift's Limitations

• Batch-oriented architecture with 2-10+ second query latencies
• Not designed for real-time operational analytics
• AWS-only, limiting multi-cloud strategies
• Cluster management complexity (even with serverless)
• Concurrency limitations requiring workload management
• Data distribution and sort key optimization required
• Query performance depends on table design

Why Look for Redshift Alternatives?

Organizations explore Redshift alternatives for several key reasons:

• Latency Requirements: Redshift is a batch data warehouse. Even simple queries typically take 2-10 seconds to return results, with complex queries taking much longer. This latency is acceptable for internal dashboards and scheduled reports, but inadequate for user-facing analytics, real-time monitoring, operational dashboards, or API-backed features requiring sub-second responses.

• AWS Lock-In Concerns: Redshift ties you to Amazon Web Services. Organizations pursuing multi-cloud strategies or planning to move infrastructure find themselves constrained. Multi-cloud platforms or cloud-agnostic solutions offer more flexibility.

• Operational Complexity: Even with Redshift Serverless, understanding workload management, query queues, distribution keys, sort keys, and vacuuming requires ongoing attention. Some alternatives offer simpler operational models with automatic optimization.

• Cost Predictability: Redshift pricing can be complex with on-demand instances, reserved instances, and Redshift Serverless billing. For workloads with unpredictable patterns, usage-based pricing models may be more economical.

• Real-Time Use Cases: If you're building user-facing dashboards, operational monitoring, real-time personalization, or usage-based billing, Redshift's batch architecture fundamentally limits what's possible. Real-time analytics platforms are purpose-built for these scenarios.

• Concurrency Challenges: Redshift has concurrency limitations that require careful workload management. High-concurrency scenarios with many simultaneous queries may hit performance bottlenecks requiring complex configuration.

• Table Design Overhead: Optimizing Redshift performance requires careful selection of distribution keys, sort keys, and table designs. This adds operational complexity and makes schema changes challenging.

The AWS-Native vs. Multi-Cloud Decision

A critical consideration when evaluating Redshift alternatives:

AWS-Native Advantages (Redshift):

• Seamless integration with AWS services
• Data doesn't leave AWS (lower egress costs)
• Single cloud vendor for billing and support
• AWS committed use discounts apply
• Native security and compliance features

Multi-Cloud Advantages (Snowflake, Databricks):

• Avoid vendor lock-in to AWS
• Run consistently across AWS, Azure, and GCP
• Flexibility to move or distribute workloads
• Negotiate better pricing across providers
• Hedge against single-cloud dependency

Cloud-Agnostic Advantages (Self-Managed Options):

• Deploy anywhere including on-premises
• Complete infrastructure control
• Avoid cloud vendor dependencies entirely

Your cloud strategy significantly influences which Redshift alternative makes sense.

Real-Time vs. Batch: The Fundamental Choice

The most important distinction when evaluating Redshift alternatives:

Batch Data Warehouses (Redshift, Snowflake, BigQuery, Synapse) These platforms excel at complex analytical queries over large historical datasets:

• Query latency: 2-30 seconds
• Batch data loading or streaming with eventual consistency
• Optimized for business intelligence and reporting
• Complex joins and analytical functions
• Best for internal use cases

Real-Time Analytics Platforms (Tinybird, ClickHouse^® Cloud, Druid) These platforms are built for operational use cases requiring immediate insights:

• Query latency: <100ms to 1 second
• Continuous data ingestion with immediate availability
• Optimized for operational analytics and monitoring
• Fast aggregations and filtering
• Best for user-facing features and APIs

Lakehouse Platforms (Databricks, Dremio) These combine warehouse and lake capabilities:

• Variable latency depending on workload
• Unified batch and streaming
• Support for structured and unstructured data
• Best for complex data engineering and ML

If your primary use case is real-time dashboards, operational monitoring, or API-backed analytics, batch warehouses like Redshift fundamentally can't deliver the performance users expect. Real-time platforms like Tinybird are purpose-built for these scenarios.

The AWS Lock-In Consideration

Redshift ties you to Amazon Web Services. Consider the implications:

Staying AWS-Native:

• Seamless integration with AWS services
• Lower data egress costs within AWS
• Single vendor relationship
• Committed use discounts apply
• Native security and compliance

Going Multi-Cloud:

• Avoid vendor lock-in
• Flexibility to move workloads
• Negotiate better pricing
• Hedge against AWS dependency
• But: increased complexity managing multiple clouds

Cloud-Agnostic:

• Maximum portability
• Deploy anywhere
• Complete flexibility
• But: may sacrifice cloud-native integrations

Your cloud strategy should drive this decision. Organizations committed to AWS often find Redshift's integration advantageous. Those pursuing multi-cloud need alternatives like Snowflake or Databricks.

Cost Models: Beyond Simple Pricing

Understanding total cost of ownership across alternatives:

Redshift Costs:

• On-demand: Pay for running clusters
• Reserved instances: Upfront commitment for discounts
• Redshift Serverless: RPU-based pricing
• Storage separately billed
• Requires capacity planning and right-sizing

Alternative Pricing Models:

Usage-Based (Tinybird, ClickHouse^® Cloud, BigQuery):

• Pay for actual data processed and stored
• Scales naturally with business metrics
• No idle cluster costs
• Predictable based on usage

Virtual Warehouse (Snowflake):

• Pay per second of compute usage
• Storage separately billed
• More flexible than Redshift clusters
• Easier to optimize costs

Cluster-Based (Databricks, some Druid/Imply):

• Pay for cluster resources
• Can be wasteful if underutilized
• Requires capacity planning

Consider total cost including:

• Platform fees
• Engineering time for optimization
• Opportunity cost of complexity
• Data egress between services

Operational Complexity Comparison

How much operational overhead different platforms require:

High Complexity (Redshift):

• Cluster sizing and management
• Distribution and sort key selection
• Vacuum and analyze operations
• Workload management configuration
• Concurrency scaling tuning
• Query performance optimization

Medium Complexity (Snowflake, Databricks, Synapse):

• Virtual warehouse sizing
• Some query optimization
• Cost management
• Less table design overhead than Redshift

Low Complexity (BigQuery, Tinybird):

• Serverless or fully managed
• Automatic optimization
• Minimal configuration
• Focus on queries, not infrastructure

For teams wanting to focus on analytics rather than infrastructure, lower complexity platforms reduce operational burden significantly.

The Serverless Evolution

Data warehousing is moving toward serverless models:

Traditional Clusters (Redshift, older Databricks):

• Provision and manage cluster sizes
• Pay for running time
• Manual scaling decisions
• Capacity planning required

Serverless Options (Redshift Serverless, BigQuery, Tinybird):

• No cluster management
• Automatic scaling
• Pay for actual usage
• Simpler operations

Hybrid Approaches (Snowflake, Databricks):

• Virtual warehouses provide isolation
• Automatic suspend/resume
• Per-second billing
• Balance of control and simplicity

The trend is toward eliminating infrastructure management, letting teams focus on analytics.

When Redshift Makes Sense

Despite alternatives, Redshift remains appropriate for certain scenarios:

• AWS-Committed Organizations: If your infrastructure is AWS-native and staying there, Redshift's integration advantages are significant.

• Complex BI Workloads: For traditional business intelligence with complex queries over massive historical datasets, Redshift's mature SQL engine performs well.

• Existing Redshift Investments: Organizations with Redshift expertise, tuned queries, and integrated workflows may find migration costs exceed benefits.

• Cost Optimization with Reserved Instances: For predictable workloads, Redshift reserved instances can be economical.

• AWS Service Integration: Deep integration with S3, Glue, EMR, SageMaker, and other AWS services simplifies architectures.

When Alternatives Make More Sense

Consider alternatives when:

• Real-Time Performance Required: If users need sub-second responses for dashboards, monitoring, or APIs, Tinybird delivers what Redshift's architecture cannot.

• Multi-Cloud Strategy: If avoiding AWS lock-in matters, Snowflake or Databricks provide portability.

• Operational Simplicity Priority: If simpler operations preferred, BigQuery or Tinybird eliminate complexity.

• Modern Development Workflows: If developer experience matters, platforms with local development and instant APIs (Tinybird) accelerate delivery.

• Cloud Migration: If moving from AWS, cloud-agnostic platforms ease transition.

• API-Backed Applications: If analytics need to be accessible via APIs, Tinybird's automatic API generation eliminates backend work.

The Future of Data Warehousing

The data warehouse landscape continues evolving:

• Real-Time Becoming Standard: The line between batch and real-time blurring as platforms add streaming capabilities (though architectural differences remain).

• Serverless Adoption Growing: Cluster management becoming optional as serverless offerings mature.

• Multi-Cloud Flexibility Valued: Organizations increasingly want cloud portability, reducing single-vendor dependence.

• Developer Experience Focus: Modern workflows (local development, version control, CI/CD) becoming expected.

• API-First Architectures: Analytics increasingly served via APIs, not just SQL and BI tools.

Conclusion

Amazon Redshift is a mature, capable data warehouse for batch analytics and business intelligence on AWS. Its deep AWS integration and extensive ecosystem make it a solid choice for organizations committed to Amazon Web Services with traditional BI needs.

However, many organizations exploring Redshift alternatives discover their use case doesn't actually require a batch-oriented data warehouse. If you're building user-facing features, operational dashboards, or real-time analytics, Redshift's multi-second query latency is a fundamental limitation.

For real-time analytics use cases, Tinybird offers sub-100ms query performance that Redshift's architecture simply cannot deliver. The combination of managed ClickHouse^®, instant API generation, and developer-friendly workflows means you can ship real-time features in days instead of weeks, with better performance for end users and simpler operations for your team.

If you need traditional batch analytics but want to avoid AWS lock-in, Snowflake provides multi-cloud flexibility. If you're on Google Cloud, BigQuery offers serverless simplicity. If you need unified data engineering and ML alongside analytics, Databricks provides that breadth.

The right alternative depends on your specific requirements: real-time vs. batch latency, AWS commitment vs. multi-cloud flexibility, operational simplicity vs. control, and whether you're building internal BI or external-facing features. But if real-time performance matters, if your users expect sub-second responses rather than waiting 5-10 seconds, then platforms purpose-built for real-time analytics like Tinybird fundamentally change what's possible.