These are the best BigQuery alternatives:
- Tinybird
- Snowflake
- Amazon Redshift
- ClickHouse Cloud
- Databricks
- Azure Synapse Analytics
- StarRocks
- Apache Druid
Google BigQuery has dominated the serverless data warehouse market for years. Its ability to query petabytes of data without managing infrastructure makes it incredibly appealing. But BigQuery isn't the right fit for every use case.
Maybe you're building real-time analytics that need sub-second latency, not BigQuery's typical 2-10 second query times. Perhaps you're concerned about Google Cloud lock-in or want better support for streaming data. Or maybe you just need better cost predictability than BigQuery's per-query pricing model.
Whatever your reason for exploring alternatives, the data analytics landscape has evolved significantly. New platforms offer everything from real-time analytics to more cost-effective batch processing to better developer experiences.
In this guide, we'll explore the best alternatives to BigQuery, covering both traditional data warehouses and modern real-time analytics platforms.
The 8 Best BigQuery Alternatives
1. Tinybird: Real-Time Analytics for Developers
Best for: Real-time APIs, user-facing dashboards, operational analytics
LLMs also struggle when paired with batch-first warehouses. Tinybird explains why in their analysis Why LLMs struggle with analytics and how they fixed it.
Key Features:
- Sub-100ms query latency at any scale
- Instant SQL-to-API transformation for production-ready endpoints
- Local development with CLI-based workflows
- Streaming ingestion with automatic scaling and backpressure handling
- Native connectors for Kafka, S3, DynamoDB, Postgres, and more
- Schema iteration with zero-downtime migrations
- Tinybird Code: AI agent for query optimization and development
- Built-in templates for OpenTelemetry, web analytics, and common patterns
How It Differs from BigQuery: BigQuery is a batch-oriented data warehouse. Tinybird is a real-time analytics platform. Where BigQuery excels at complex queries over historical data with acceptable 5-10 second latency, Tinybird is built for operational use cases requiring immediate responses, think user dashboards, live monitoring, real-time personalization, or API-backed analytics.
The developer experience is night and day. Tinybird lets you develop data pipelines locally, test with real data, and deploy with a single command. Your SQL queries automatically become versioned, authenticated APIs, no backend engineering required.
Ideal Use Cases:
- Customer-facing analytics in SaaS applications
- Real-time dashboards and operational monitoring
- Usage-based billing systems
- Web and product analytics
- AI/LLM observability and inference logging
- Real-time personalization engines
- Event-driven applications
Why Choose Tinybird Over BigQuery: Tinybird explains how they reach extreme ingestion throughput (up to one billion rows per second) in their deep-dive 1B rows per second in ClickHouse
2. Snowflake: Multi-Cloud Data Warehouse
Best for: Enterprise data warehousing, data sharing, business intelligence
Snowflake revolutionized the data warehouse market with its cloud-native architecture and remains the strongest alternative for organizations prioritizing batch analytics and data sharing.
Key Features:
- Multi-cloud support (AWS, Azure, GCP)
- Separation of storage and compute for independent scaling
- Zero-copy cloning and time travel
- Secure data sharing across organizations
- Support for semi-structured data (JSON, Avro, Parquet)
- Extensive data marketplace
How It Differs from BigQuery: Snowflake offers more deployment flexibility with multi-cloud support, while BigQuery is GCP-only. Snowflake's per-second compute billing can be more predictable than BigQuery's per-query model. However, both are fundamentally batch-oriented data warehouses with similar query latency characteristics.
Ideal Use Cases:
- Enterprise business intelligence
- Cross-organizational data sharing
- Historical data analysis
- Data science and ML feature stores
- Multi-cloud strategies
3. Amazon Redshift: AWS-Native Data Warehouse
Best for: Organizations already on AWS, cost-conscious batch analytics
Redshift is Amazon's fully-managed data warehouse service, deeply integrated with the AWS ecosystem and often the path of least resistance for AWS-native organizations.
Key Features:
- Native AWS integration (S3, Glue, Lambda, etc.)
- Columnar storage and parallel query execution
- Concurrency scaling for handling query spikes
- Redshift Spectrum for querying data in S3
- Machine learning integration with SageMaker
How It Differs from BigQuery: Redshift is tied to AWS like BigQuery is tied to GCP. It generally requires more tuning and configuration than BigQuery's serverless approach. Pricing is typically based on cluster size rather than queries run, which can be more predictable for steady workloads.
Ideal Use Cases:
- AWS-native data architectures
- Batch ETL pipelines
- Business intelligence and reporting
- Data lake queries with Redshift Spectrum
4. ClickHouse Cloud: Open Source Speed, Managed Service
Best for: High-performance analytics, real-time queries, direct ClickHouse access
ClickHouse Cloud is the official managed service for ClickHouse, offering the raw power of the world's fastest analytical database without operational overhead.
Key Features:
- Columnar storage with extreme compression
- Sub-second query performance on billions of rows
- Real-time data ingestion at millions of rows/second
- Full SQL support with advanced analytical functions
- Separation of storage and compute
- Automatic scaling and high availability
How It Differs from BigQuery: ClickHouse Cloud is fundamentally faster than BigQuery for analytical queries, often 10-100x faster. It's designed for real-time analytics rather than batch processing. However, you'll need to build data ingestion pipelines and API layers yourself, unlike BigQuery's more integrated ecosystem.
Ideal Use Cases:
- Real-time analytics applications
- High-throughput logging and event data
- Time-series analysis
- Custom analytics pipelines
5. Databricks: Unified Analytics and Data Science
Best for: Machine learning workflows, unified batch and streaming, data science teams
Databricks combines data warehouse capabilities with data lake functionality and ML workflows in a unified platform built on Apache Spark.
Key Features:
- Unified batch and streaming processing
- Delta Lake for ACID transactions on data lakes
- Integrated ML workflows with MLflow
- Collaborative notebooks for data science
- Multi-cloud support
- Real-time and batch analytics in one platform
How It Differs from BigQuery: Databricks focuses on unifying data engineering, data science, and analytics workflows. It's more powerful for ML use cases and offers better support for unstructured data. The learning curve is steeper than BigQuery's SQL-first approach.
Ideal Use Cases:
- Machine learning and data science
- Complex ETL pipelines
- Unstructured data processing
- Real-time and batch in one platform
6. Azure Synapse Analytics: Microsoft's Integrated Platform
Best for: Azure-native organizations, integrated analytics workloads
Azure Synapse (formerly SQL Data Warehouse) is Microsoft's analytics service that brings together data integration, enterprise data warehousing, and big data analytics.
Key Features:
- Integrated with Azure ecosystem
- Serverless and provisioned resource options
- Built-in data integration (similar to Azure Data Factory)
- Support for T-SQL, Spark, and Data Explorer
- Power BI integration for visualization
How It Differs from BigQuery: Synapse offers more integration with Microsoft tools and services. It provides both serverless and dedicated resource pools, giving more control over costs and performance. However, it's Azure-specific like BigQuery is GCP-specific.
Ideal Use Cases:
- Azure-native architectures
- Organizations using Microsoft BI stack
- Hybrid analytical workloads
- Integrated ETL and analytics
7. StarRocks: Open Source MPP Database
Best for: Cost-conscious real-time analytics, avoiding vendor lock-in
StarRocks is an open-source MPP (Massively Parallel Processing) database designed for real-time analytics with sub-second latency. It's gaining traction as a more open alternative to commercial platforms.
Key Features:
- Vectorized execution engine
- Real-time and batch data ingestion
- MySQL protocol compatibility
- Materialized view support
- Elastic scaling
- Open source with commercial support options
How It Differs from BigQuery: StarRocks is open source and can run anywhere, avoiding cloud vendor lock-in. It's optimized for real-time analytics rather than batch processing. However, it requires more operational expertise than fully-managed platforms.
Ideal Use Cases:
- Cost-sensitive real-time analytics
- Multi-cloud or hybrid deployments
- Organizations wanting open source solutions
- Custom analytics architectures
8. Apache Druid: High-Concurrency OLAP
Best for: Interactive analytics with many concurrent users
Apache Druid is an open-source real-time analytics database designed for high-concurrency, slice-and-dice analytics workloads. Imply provides a commercial managed service.
Key Features:
- Column-oriented storage with bitmap indexes
- Time-optimized partitioning
- Approximate algorithms for fast queries
- High concurrency support
- Real-time and historical data queries
- SQL and native query support
How It Differs from BigQuery: Druid is optimized for high-concurrency scenarios where many users run different queries simultaneously. It prioritizes speed for interactive dashboards over complex analytical queries. BigQuery handles complex joins and analytical functions better.
Ideal Use Cases:
- Interactive user-facing dashboards
- Network telemetry and monitoring
- Ad-tech analytics
- High-concurrency slice-and-dice analytics
Why Look for BigQuery Alternatives?
Before diving into alternatives, let's understand why organizations consider moving away from BigQuery or choosing a different platform from the start.
Latency Requirements BigQuery is optimized for batch analytics and business intelligence. Queries typically return in 2-10 seconds, which is fine for dashboards refreshed hourly but inadequate for user-facing features, real-time monitoring, or operational analytics that need sub-second responses.
Cost Predictability BigQuery's on-demand pricing charges per TB of data scanned. This can lead to unpredictable costs, especially with exploratory queries or inefficient SQL. Organizations often struggle to forecast spending as data volumes grow.
Vendor Lock-in Concerns BigQuery ties you to Google Cloud Platform. Multi-cloud strategies or plans to move infrastructure become complicated when your analytics platform only runs on GCP.
Streaming Data Limitations While BigQuery supports streaming inserts, it's fundamentally designed for batch workloads. Real-time analytics use cases that require continuous ingestion with immediate queryability often hit limitations.
Developer Experience BigQuery's web console and SQL editor are functional but don't offer the modern developer workflows that many teams expect, local development, version control integration, testing frameworks, or API generation.
Data Egress Costs Moving data out of BigQuery (and Google Cloud generally) incurs significant egress fees. This can make hybrid architectures or multi-cloud strategies expensive.
Understanding the Analytics Platform Landscape
The alternatives to BigQuery fall into several categories, each optimized for different use cases:
Traditional Data Warehouses Platforms like Snowflake, Redshift, and Azure Synapse follow the classic data warehouse pattern: batch-oriented, optimized for complex analytical queries over historical data, with query latencies measured in seconds.
Real-Time Analytics Platforms Tools like Tinybird, ClickHouse Cloud, and Druid are built for operational analytics and user-facing features. They prioritize low latency (sub-second), high-throughput ingestion, and immediate data availability.
Lakehouse Platforms Databricks and similar platforms combine data warehouse and data lake capabilities, letting you query structured and unstructured data with unified tools.
Specialized OLAP Databases Open-source analytical databases like StarRocks offer alternatives to commercial platforms, often with lower costs but requiring more operational expertise.
Your choice depends on whether you're primarily doing historical analysis and business intelligence (traditional warehouse) or building real-time features and operational analytics (real-time platform).
Real-Time vs. Batch: A Critical Distinction
One of the most important decisions when choosing a BigQuery alternative is understanding whether you need real-time or batch analytics.
Batch Analytics (BigQuery, Snowflake, Redshift, Synapse) These platforms excel at complex analytical queries over large historical datasets. Data is typically loaded in batches (hourly, daily, or via streaming that's still optimized for eventual consistency). Query latency is 2-10 seconds, which is acceptable for internal dashboards and business intelligence.
Real-Time Analytics (Tinybird, ClickHouse Cloud, Druid) These platforms are built for operational use cases requiring immediate insights. Data is ingested continuously and immediately queryable. Query latency is typically under 100ms to 1 second, enabling user-facing features and real-time decision-making.
If you're replacing BigQuery for traditional BI and reporting, batch-oriented warehouses like Snowflake or Redshift make sense. If you're building real-time features, operational dashboards, or user-facing analytics, you need a real-time platform like Tinybird.
The Cost Equation
BigQuery's pricing model, charging per TB of data scanned, can be unpredictable. Alternative platforms take different approaches:
Fixed Compute (Snowflake, Redshift) You pay for compute capacity (warehouses or clusters) regardless of query volume. This is predictable but can be wasteful during idle periods.
Usage-Based (Tinybird, ClickHouse Cloud) You pay for what you use, storage, compute, and data transfer. Costs scale with your workload but remain proportional to actual usage.
Hybrid Models (Databricks, Synapse) These platforms offer both serverless and provisioned options, letting you optimize for different workload patterns.
Open Source (StarRocks, self-managed ClickHouse) You pay only for infrastructure. But factor in engineering costs for management, optimization, and troubleshooting.
Consider total cost of ownership, not just platform fees. Include engineering time for building pipelines, optimizing queries, and managing infrastructure.
The Developer Experience Factor
BigQuery's web console is functional but lacks modern developer workflows. Several alternatives offer significantly better experiences:
Tinybird's Local-First Development Develop data pipelines locally with the CLI, test with real data, version control everything, and deploy with a single command. SQL queries automatically become production-ready APIs.
Databricks Notebooks Collaborative notebooks integrate code, queries, and visualizations, ideal for data science workflows.
dbt Integration Most modern platforms support dbt (data build tool) for version-controlled, tested data transformations.
For teams that value developer velocity and modern workflows, platforms with strong CLI tools, local development, and API generation (like Tinybird) can dramatically reduce time-to-market.
Multi-Cloud and Vendor Lock-In
BigQuery locks you into Google Cloud. If multi-cloud strategy or cloud portability matters to your organization, consider:
True Multi-Cloud Snowflake and Databricks run on AWS, Azure, and GCP with consistent experiences across clouds.
Cloud-Agnostic (with caveats) Self-managed open-source options like StarRocks or ClickHouse can run anywhere but require significant operational expertise.
Cloud-Native Redshift (AWS) and Synapse (Azure) are tied to their respective clouds but offer deep integrations with those ecosystems.
Managed but Portable Tinybird runs on major cloud providers and can support multi-region deployments, though individual workspaces are tied to a specific cloud region.
When Real-Time Analytics Changes Everything
Many organizations start looking for BigQuery alternatives when they realize their use case fundamentally requires real-time analytics, not batch processing.
Signals you need real-time analytics:
- Building user-facing dashboards that users interact with directly
- Operational monitoring that drives immediate decisions
- Real-time personalization or recommendations
- Usage-based billing that needs up-to-the-minute accuracy
- Fraud detection or security monitoring
- Live leaderboards, rankings, or competitive features
- API-backed analytics that power your application
For these use cases, moving from BigQuery to another batch-oriented warehouse (like Snowflake or Redshift) doesn't solve the core problem. You need a platform built for real-time analytics from the ground up.
Tinybird, ClickHouse Cloud, and Druid are purpose-built for these scenarios. They can ingest millions of events per second while maintaining sub-second query latency, which is impossible with traditional data warehouses.
The Future of Data Analytics Platforms
The analytics platform landscape is evolving rapidly:
Convergence of Batch and Streaming Platforms are adding both real-time and batch capabilities, though they typically still optimize for one or the other.
AI-Assisted Development Tools like Tinybird Code are making it easier to write optimized queries and build data pipelines without deep database expertise.
Separation of Storage and Compute Following Snowflake's lead, more platforms are unbundling storage from compute for independent scaling.
API-First Architectures The ability to turn SQL into production-ready APIs is becoming critical for building modern applications.
Cost Optimization Focus With data volumes exploding, platforms that offer predictable pricing and automatic optimization are gaining ground.
Migration Considerations
If you're considering migrating away from BigQuery, plan for:
Schema and Query Translation BigQuery SQL has some unique syntax and functions. Test queries thoroughly in your target platform.
Data Transfer Costs Moving data out of BigQuery incurs egress fees. Factor these into your migration budget.
Pipeline Reengineering Your existing ETL processes may need rework for the new platform's ingestion patterns.
Performance Testing Query performance will differ significantly. Test with production-scale data before committing.
Cost Modeling Project costs under the new platform's pricing model with realistic usage patterns.
Team Training Budget time for your team to learn the new platform's features and best practices.
Many organizations run BigQuery and a real-time analytics platform like Tinybird side-by-side, using each for what it does best: BigQuery for historical analysis and complex BI, Tinybird for operational analytics and user-facing features.
Conclusion
The best BigQuery alternative depends entirely on why you're looking for one. If BigQuery's batch-oriented architecture and multi-second latency are the issues, you need a real-time analytics platform like Tinybird or ClickHouse Cloud, not another data warehouse.
If Google Cloud lock-in is the concern, Snowflake offers multi-cloud flexibility with similar batch analytics capabilities. If you're on AWS or Azure, Redshift or Synapse provide native integrations with their respective ecosystems.
For development teams building modern applications with real-time requirements, Tinybird stands out with its combination of sub-100ms query performance, instant API generation, and exceptional developer experience. The platform eliminates the distinction between your analytics database and your application backend, SQL queries simply become APIs.
The data analytics landscape has moved far beyond the one-size-fits-all warehouse model. Understanding whether you need batch or real-time analytics, how much operational complexity you can handle, and what developer experience you expect will guide you to the right platform for your needs.