These are the best alternatives to Google Cloud BigQuery for analytics and data warehousing:

1. Tinybird
2. Snowflake
3. Amazon Redshift
4. Azure Synapse Analytics
5. Databricks SQL
6. ClickHouse®
7. Amazon Athena
8. Trino
9. Dremio
10. Apache Druid

When you need serverless SQL analytics at scale, BigQuery has become Google Cloud's flagship offering. It promises no infrastructure management, flexible pricing models, and integrated ML capabilities—all wrapped in a familiar SQL interface.

BigQuery's core appeal is simplicity: execute SQL without provisioning clusters, pay per query or reserve capacity with slots, and leverage BI Engine for accelerated dashboards. For many analytics workloads, it delivers exactly what teams need.

But BigQuery's model creates friction points that drive teams toward alternatives. High-concurrency workloads hit quota limits. Embedded analytics with thousands of concurrent users stress the cost model. Multi-cloud organizations resist GCP lock-in. And teams building real-time product features need latency guarantees that warehouse architectures can't provide.

Teams evaluating BigQuery alternatives typically fall into four categories: those needing cost predictability at scale, those requiring lower latency for user-facing analytics, those pursuing multi-cloud or lakehouse strategies, and those wanting more control over compute isolation.

We evaluate each alternative based on execution model, pricing structure, concurrency handling, and real-time capabilities to help you choose the right platform for your specific needs.

Need real-time analytics APIs with predictable latency?

If you're evaluating BigQuery alternatives because your real need is user-facing analytics—embedded dashboards, customer-facing metrics, operational APIs with sub-100ms latency—consider Tinybird. It's a real-time data platform built on ClickHouse® that handles everything from streaming ingestion to instant API publication. No slot management, no query quotas, just SQL queries that become production-ready HTTP endpoints in seconds.

1. Tinybird: Real-Time Analytics Platform for Product Features

Before diving into data warehouse alternatives, let's address a fundamentally different approach that solves the underlying problem many teams face when evaluating BigQuery.

Tinybird isn't a data warehouse—it's a complete real-time data platform built on ClickHouse® that handles ingestion, transformation, and API publication in one integrated service. If your actual need is analytics serving for products rather than ad-hoc exploration, Tinybird eliminates the architectural mismatch that makes BigQuery challenging for these workloads.

Why Product Analytics Doesn't Fit Warehouse Architecture

BigQuery executes queries through a distributed tree model with slots as compute units. This works brilliantly for analytical exploration and batch reporting. But product-facing analytics has different requirements:

• Consistent sub-100ms latency, not "usually fast"
• Thousands of concurrent requests from end users
• Fresh data in milliseconds, not minutes
• API serving without additional infrastructure

BigQuery's quota limits on concurrent queries and jobs become bottlenecks at product scale. Even with BI Engine caching, the architecture wasn't designed for high-QPS serving workloads.

Purpose-Built for Serving Analytics

Tinybird uses ClickHouse® under the hood—a columnar OLAP database optimized for fast aggregations with high concurrency. The difference is architectural:

• Sparse primary indexes enable sub-millisecond data location
• Vectorized execution processes columns in batches
• Granule-based storage allows precise data skipping
• No slot contention—queries execute independently

The result: consistent sub-100ms queries on billions of rows with thousands of concurrent users, maintaining low latency performance even under heavy concurrent workloads.

Instant APIs from SQL Queries

One of Tinybird's most powerful features is the instant API layer. Write a SQL query, publish it as a secure HTTP endpoint with one click. No backend service to build, no API framework to maintain, no infrastructure to scale.

For teams building customer-facing dashboards, embedded analytics, or operational monitoring, this capability replaces months of development.

Streaming-First Ingestion

While BigQuery's Storage Write API enables real-time ingestion with committed streams, Tinybird is streaming-first by design. Streaming data flows continuously from Kafka, webhooks, S3, or direct HTTP and becomes immediately queryable.

No batch windows. No slot reservation for ingestion. One unified system handles both ingestion and serving with consistent low latency.

Fully Managed with Predictable Pricing

With BigQuery, you choose between on-demand pricing (bytes scanned) and capacity pricing (slots with autoscaling). Both can surprise you: on-demand costs scale with query complexity, while autoscaling bills for slots scaled, not slots used.

Tinybird offers fixed monthly plans with included compute and storage. You know costs upfront, regardless of query patterns or concurrency spikes.

When Tinybird Makes Sense

Tinybird is ideal when:

• Your need is user-facing analytics, not ad-hoc exploration
• You require consistent sub-100ms latency at scale
• You want instant APIs without backend development
• Predictable pricing matters more than maximum flexibility
• You're building product features powered by analytics

2. Snowflake: Multi-Cloud Data Warehouse with Compute Isolation

Snowflake has become BigQuery's primary competitor in the cloud data warehouse space, with strong multi-cloud support and explicit compute isolation.

Virtual Warehouses for Workload Separation

Snowflake's central concept is the virtual warehouse—a cluster of compute resources that you can start, stop, resize, and multiply independently. Unlike BigQuery's shared slot pool, Snowflake lets you isolate workloads by design.

Create separate warehouses for ETL, BI, and ad-hoc. Assign different warehouses to different teams or customers. Control costs and performance through explicit compute boundaries.

Multi-Cloud Deployment

Snowflake runs on AWS, Azure, and GCP—the same platform across clouds. For organizations with multi-cloud strategies or those avoiding GCP lock-in, Snowflake provides consistent experience regardless of underlying infrastructure.

Data sharing across clouds and organizations extends this flexibility for collaborative analytics.

Micro-Partitions and Pruning

Snowflake stores data in micro-partitions with column-level metadata for min/max values. The optimizer uses this metadata for partition pruning, skipping irrelevant data without explicit partitioning schemes.

Clustering keys let you optimize pruning for specific query patterns when natural data ordering degrades.

Concurrency and Caching

Snowflake handles concurrency through warehouse scaling (bigger or more warehouses) and aggressive caching: result cache for identical queries and data cache for recently accessed micro-partitions.

For high-concurrency BI, you scale warehouses rather than fighting a shared resource pool.

When Snowflake Fits

Consider Snowflake when:

• Multi-cloud deployment is strategic
• You need explicit workload isolation by warehouse
• Data sharing across organizations matters
• Your team prefers managing compute explicitly
• BI and ETL workloads dominate your use case

3. Amazon Redshift: AWS-Native Data Warehouse

Amazon Redshift is AWS's data warehouse, with deep ecosystem integration and recent architectural improvements for serverless and elastic scaling.

Serverless and Provisioned Options

Redshift offers both provisioned clusters (you manage node types and counts) and Redshift Serverless (pay per compute-second with automatic scaling). For teams wanting BigQuery-like simplicity on AWS, Serverless reduces operational burden.

RA3 nodes separate compute and storage, allowing independent scaling and managed storage that grows automatically.

Concurrency Scaling

Redshift's Concurrency Scaling automatically adds compute capacity during query spikes, supporting thousands of concurrent users without manual intervention. This addresses one of BigQuery's key limitations for high-concurrency BI workloads.

You get one hour of free Concurrency Scaling credits per day per provisioned cluster, with additional usage billed per-second.

Sort Keys and Distribution

Redshift performance depends heavily on sort keys (physical data ordering) and distribution styles (how data spreads across nodes). Well-designed tables with compound or interleaved sort keys enable efficient range scans and zone map pruning.

This is more explicit than BigQuery's partitioning—you trade simplicity for control.

AWS Ecosystem Integration

Redshift integrates natively with S3, Glue, Lake Formation, Kinesis, and QuickSight. For AWS-centric organizations, this reduces data movement and simplifies security configuration through IAM.

Redshift Spectrum queries S3 data directly, bridging warehouse and data lake.

When Redshift Fits

Consider Amazon Redshift when:

• AWS is your primary cloud platform
• You need Concurrency Scaling for BI workloads
• Deep AWS integration simplifies your architecture
• Your team can handle sort key and distribution design
• Serverless simplicity on AWS is appealing

4. Azure Synapse Analytics: Microsoft's Unified Analytics Platform

Azure Synapse combines dedicated SQL pools (traditional warehousing) with serverless SQL pools (query data lake directly) in one platform.

Dual Execution Models

Synapse's dedicated SQL pools provision compute for predictable, high-performance warehousing. Serverless SQL pools query Parquet, CSV, and JSON in Azure Data Lake Storage without provisioning, paying per TB scanned.

This duality lets you choose per workload: provision for production, serverless for exploration.

Microsoft Ecosystem Integration

Synapse integrates deeply with Power BI, Azure Data Lake Storage, Azure Machine Learning, and Microsoft Purview for governance. For Microsoft-centric organizations, this reduces friction across the analytics stack.

Azure Active Directory provides unified identity management across services.

Spark Integration

Beyond SQL, Synapse includes Apache Spark pools for data engineering and ML workloads. You can mix SQL and Spark in the same platform, though each has separate compute models.

Distribution and Indexing

Like Redshift, Synapse dedicated pools require distribution (hash, round-robin, replicated) and index design for performance. The tradeoffs are similar: more control, more design decisions.

When Synapse Fits

Consider Azure Synapse when:

• Azure is your primary cloud platform
• Power BI integration is critical
• You want serverless and provisioned in one platform
• Spark and SQL workloads coexist
• Microsoft governance tools align with your strategy

5. Databricks SQL: Lakehouse Analytics

Databricks SQL represents the lakehouse approach—analytics directly on open formats like Delta Lake and Iceberg, with SQL warehouse compute that scales independently.

Lakehouse Architecture

Instead of loading data into a proprietary warehouse, Databricks queries Delta tables in cloud storage. This preserves data portability while providing warehouse-like performance through optimizations like Z-ordering and liquid clustering.

Unity Catalog provides governance across all Databricks assets.

SQL Warehouses

Databricks SQL runs on SQL warehouses—compute clusters optimized for BI workloads. Serverless SQL warehouses eliminate cluster management, scaling automatically based on query load.

Photon, Databricks' vectorized engine, accelerates queries significantly over standard Spark SQL.

Unified Platform

The key differentiator: Databricks unifies data engineering, ML, and SQL analytics on one platform. If your organization runs notebooks for data science alongside SQL for BI, this reduces platform sprawl.

You don't move data between systems; you change the compute layer over the same tables.

When Databricks SQL Fits

Consider Databricks SQL when:

• Lakehouse architecture aligns with your strategy
• You want open formats (Delta, Iceberg) as source of truth
• Data engineering and ML coexist with SQL analytics
• Unity Catalog governance meets your needs
• Photon performance justifies the investment

6. ClickHouse®: Open-Source OLAP Performance

ClickHouse® is an open-source columnar database purpose-built for high-performance OLAP, offering an alternative to managed warehouses for teams needing maximum query speed.

Columnar Architecture Optimized for Aggregations

ClickHouse®'s MergeTree engine family stores data in sorted parts divided into granules. A sparse primary index enables binary search to locate relevant granules, while data skipping indexes allow pruning based on secondary columns.

This architecture delivers sub-second queries on billions of rows for well-modeled data.

Performance at Any Scale

ClickHouse® consistently benchmarks among the fastest OLAP databases. For workloads where query latency is critical—real-time dashboards, operational analytics, user-facing features—ClickHouse® often outperforms warehouse alternatives by orders of magnitude.

Self-Managed vs. Managed Options

ClickHouse® is open source, allowing self-hosted deployment. ClickHouse® Cloud provides a managed experience, while platforms like Tinybird add additional layers for API publication and streaming ingestion.

The choice depends on your operational capacity and whether you need capabilities beyond raw database performance.

When ClickHouse® Fits

Consider ClickHouse® when:

• Query performance is the primary requirement
• You're building real-time analytics or product features
• Your team can handle data modeling for columnar optimization
• Open source matters for your organization
• You want maximum control over configuration

7. Amazon Athena: Serverless SQL on S3

Amazon Athena provides serverless SQL directly on data in S3, with no infrastructure to manage and pay-per-query pricing.

Query Data Where It Lives

Athena queries Parquet, ORC, JSON, CSV, and Avro files in S3 without loading them into a database. For data lake architectures, this eliminates the warehouse loading step entirely.

You define external tables over S3 paths and query immediately.

Pricing Model

Athena charges per TB scanned, incentivizing columnar formats, compression, and partitioning. Well-structured Parquet with appropriate partitions can reduce costs by 90% or more compared to raw formats.

This aligns costs with query efficiency, rewarding good data engineering.

Federated Queries

Athena can query beyond S3: DynamoDB, RDS, Redshift, and other sources through federated query connectors. This enables cross-source analytics without centralizing all data.

When Athena Fits

Consider Amazon Athena when:

• Your data lives in S3 data lakes
• Ad-hoc exploration is the primary use case
• You want zero infrastructure management
• Pay-per-query aligns with sporadic workloads
• Federated queries across AWS services matter

8. Trino: Distributed SQL Query Engine

Trino (formerly Presto) is an open-source distributed SQL engine that queries data across heterogeneous sources without moving it.

Federated Query Architecture

Trino's coordinator-worker architecture executes SQL across any data source with a connector: S3, HDFS, PostgreSQL, MySQL, MongoDB, Kafka, and dozens more. You query data where it lives through a unified SQL interface.

This makes Trino ideal for data federation scenarios where centralizing data is impractical.

In-Memory Execution

Trino processes queries in-memory with pipelining between stages. While it can spill to disk under memory pressure, optimal performance requires sizing memory appropriately for your query patterns.

Managed and Self-Hosted Options

Self-hosted Trino requires significant operational expertise: cluster sizing, coordinator configuration, connector tuning. Managed options like Starburst or Amazon EMR reduce this burden.

When Trino Fits

Consider Trino when:

• Data federation across sources is required
• You can't (or won't) centralize data in one warehouse
• Your team has distributed systems expertise
• Open source is strategically important
• You're building a query layer over existing data

9. Dremio: Lakehouse Platform with Acceleration

Dremio provides a lakehouse experience with reflections (transparent acceleration) and native Iceberg support.

Query Acceleration Through Reflections

Dremio's reflections are pre-computed aggregations and sorts that the optimizer uses transparently. You define reflections on frequently queried patterns; Dremio routes queries to reflections when beneficial.

This provides materialized view-like acceleration without query rewriting.

Apache Iceberg Native

Dremio integrates deeply with Apache Iceberg, supporting time travel, schema evolution, and partition evolution natively. For organizations adopting Iceberg as their table format standard, Dremio provides strong compatibility.

Open Standards Philosophy

Dremio emphasizes open formats and open standards: Arrow, Iceberg, Parquet. The pitch is warehouse performance without vendor lock-in—your data stays portable.

When Dremio Fits

Consider Dremio when:

• Apache Iceberg is your table format strategy
• You want acceleration without explicit materialization
• Open standards and portability are priorities
• Your architecture is lakehouse-oriented
• Self-service analytics over lakes is the goal

10. Apache Druid: Real-Time OLAP for Events

Apache Druid provides real-time OLAP optimized for event data with high concurrency and sub-second queries.

Sub-Second Queries at Scale

Druid's architecture combines columnar storage, inverted indexes, and pre-aggregation (roll-ups) for extreme query performance. It's designed for user-facing analytics where latency matters.

Queries over billions of events typically complete in hundreds of milliseconds.

Real-Time and Batch Ingestion

Druid supports both streaming ingestion (from Kafka, Kinesis) and batch loading. Data becomes queryable within seconds of ingestion—true real-time, not micro-batch.

Operational Complexity

Druid's architecture involves multiple node types: historicals, brokers, coordinators, middle managers. Operating Druid at scale requires significant expertise and capacity planning.

For teams without dedicated infrastructure resources, this complexity is substantial.

When Druid Fits

Consider Apache Druid when:

• Sub-second latency on event data is critical
• High concurrent query loads are expected
• Your team can manage operational complexity
• Roll-up aggregations match your query patterns
• You're building real-time dashboards at scale

For large-scale telemetry and Internet of Things (IoT) analytics, Apache Druid’s real-time ingestion and sub-second query engine enable continuous device monitoring, event aggregation, and operational insight without waiting for batch pipelines.

Why Tinybird Is the Best BigQuery Alternative

After evaluating all alternatives, Tinybird emerges as the strongest choice for teams whose real need is user-facing analytics with consistent low latency—the use case where BigQuery's architecture struggles most.

The Right Architecture for Serving Analytics

Many teams adopt BigQuery for exploration and reporting, then try to extend it to embedded analytics and product features. This creates friction:

• Slot contention causes latency variability
• Quota limits on concurrent queries become bottlenecks
• Autoscaling costs surprise with bills for slots scaled, not used
• No native API layer requires additional infrastructure

Tinybird solves this with purpose-built serving architecture. Your BigQuery handles exploration. Tinybird handles user-facing analytics. Each platform does what it was designed for.

Consistent Sub-100ms Performance

Tinybird is built on ClickHouse®, engineered for real-time data processing and fast analytical queries at high concurrency. While BigQuery optimizes for throughput on large scans, ClickHouse® optimizes for latency on targeted aggregations.

The difference shows in production:

• Consistent sub-100ms latency, not variable by load
• Thousands of concurrent queries without slot contention
• No quota limits blocking legitimate traffic
• Predictable performance regardless of other workloads

From Query to Production API in Seconds

No BigQuery alternative offers Tinybird's instant API publication. Write a SQL query, click publish, get a production-ready HTTP endpoint.

For teams building customer dashboards or embedded analytics, this capability eliminates backend development entirely. No API framework, no scaling infrastructure, no authentication layer to build.

Streaming Ingestion Without Complexity

BigQuery's Storage Write API enables real-time ingestion but requires careful implementation for exactly-once semantics. Tinybird's ingestion is streaming-first by design: connect Kafka, send webhooks, or POST directly—data is queryable in milliseconds.

No slot reservation for ingestion. No competing with queries for resources.

Predictable Economics

BigQuery's pricing models—on-demand (bytes scanned) or capacity (slots with autoscaling)—both create unpredictability. On-demand costs scale with query complexity. Autoscaling bills for slots scaled, even if queries fail.

Tinybird offers fixed monthly plans. You know costs upfront, regardless of concurrency spikes or query patterns.

Conclusion

Choosing a BigQuery alternative depends on understanding what's actually causing friction in your data warehouses and analytics architecture.

For multi-cloud data warehousing, Snowflake provides consistent experience across AWS, Azure, and GCP with explicit compute isolation.

For AWS-native warehousing, Redshift offers deep ecosystem integration and Concurrency Scaling for high-concurrency BI.

For lakehouse architectures, Databricks SQL and Dremio query open formats with warehouse-like performance.

For data lake queries, Athena and Trino provide serverless or federated SQL without loading data into warehouses.

For user-facing analytics at scale—the hidden driver behind many BigQuery evaluations—Tinybird offers the most compelling solution. Purpose-built OLAP architecture, instant API publication, streaming ingestion, and predictable pricing let teams focus on building products rather than managing warehouse complexity.

The right choice depends on your workload patterns, cloud strategy, and latency requirements. But if your real need is product-facing analytics with consistent sub-100ms latency, starting with a platform designed for that workload will serve you far better than extending a warehouse beyond its design parameters.

Frequently Asked Questions (FAQs)

What is BigQuery and why do teams look for alternatives?

BigQuery is Google Cloud's serverless data warehouse, executing SQL without infrastructure management. Teams seek alternatives for cost predictability, lower latency at high concurrency, multi-cloud deployment, or serving user-facing analytics where warehouse architecture creates friction.

Is Tinybird a data warehouse like BigQuery?

No. Tinybird is a real-time analytics platform built on ClickHouse®, optimized for serving analytics at low latency. If your need is ad-hoc exploration and batch reporting, BigQuery or Snowflake fit better. If your need is user-facing analytics with instant APIs, Tinybird is the better architectural choice.

How does BigQuery pricing compare to alternatives?

BigQuery offers on-demand (per TB scanned) or capacity (slots with autoscaling) pricing. Snowflake charges by warehouse runtime. Athena charges per TB scanned. Tinybird offers fixed monthly plans. The best model depends on your query patterns and predictability needs.

What's the main advantage of staying with BigQuery?

GCP ecosystem integration and serverless simplicity. BigQuery works seamlessly with Pub/Sub, Dataflow, Looker, and other GCP services. For GCP-centric organizations with analytical exploration as the primary use case, BigQuery remains excellent.

When should I use Snowflake instead of BigQuery?

When multi-cloud deployment matters, when you need explicit compute isolation by warehouse, or when data sharing across organizations is important. Snowflake's architecture provides more control over concurrency through separate warehouses.

Can I query BigQuery data from other platforms?

Yes. BigQuery Omni queries data in S3 and Azure. Many alternatives like Trino and Dremio have BigQuery connectors. You can also export BigQuery data to open formats for querying elsewhere. The right approach depends on your data gravity and latency requirements.

How does Tinybird compare to ClickHouse® Cloud?

Tinybird provides managed ClickHouse® with additional layers: streaming ingestion, instant API publication, and developer tooling. ClickHouse® Cloud offers managed database without these platform features. Tinybird is designed for teams building data products; ClickHouse® Cloud for teams needing managed OLAP infrastructure.