ClickHouse^® vs BigQuery for real-time analytics

When you're choosing an analytical database for real-time analytics applications, you might consider both ClickHouse and BigQuery. Both handle massive datasets and complex queries, but they take fundamentally different approaches to architecture, performance, and cost.

This article compares ClickHouse and BigQuery across the factors that matter for real-time analytics: query latency, concurrency handling, streaming ingestion, developer experience, and total cost of ownership. You'll learn when each database fits best and how managed services like Tinybird simplify ClickHouse deployment for developers building analytics into their applications.

What makes ClickHouse and BigQuery different

ClickHouse and BigQuery are both analytical databases designed for large-scale data processing, but they differ in architecture, performance, and deployment. BigQuery is Google's serverless data warehouse where Google handles all infrastructure, scaling, and maintenance automatically. ClickHouse is an open-source columnar database that you can self-host, run on ClickHouse Cloud, or use through managed services like Tinybird.

The key difference comes down to how they handle compute and storage. BigQuery completely separates these two resources, letting you scale them independently. ClickHouse keeps them together on the same nodes, which reduces network overhead and often delivers faster query times for real-time workloads.

Architecture for real-time analytics

Coupled vs. decoupled compute

BigQuery's separation of storage and compute means you can query petabytes of data without provisioning specific resources ahead of time. The system allocates slots dynamically based on your query needs. ClickHouse takes a different path by keeping storage and compute on the same nodes, which means data and processing sit together without network hops between them.

This matters when you're running queries that need results in milliseconds. When data lives next to the compute resources, ClickHouse can read from local disk and return results faster than systems that fetch data over a network.

Columnar storage formats and compression

Both systems store data in columns rather than rows, which makes analytical queries faster. BigQuery uses its proprietary Capacitor format with automatic compression. ClickHouse offers multiple compression algorithms like LZ4, ZSTD, and specialized codecs such as Delta and DoubleDelta that you can pick based on your data patterns.

Serverless vs. managed cluster options

With BigQuery, you never think about cluster sizing or node counts. You write SQL queries and BigQuery figures out resource allocation. ClickHouse requires infrastructure decisions whether you self-host, use ClickHouse Cloud, or pick a managed service.

Tinybird handles cluster management while keeping ClickHouse's low-latency performance. You define data pipelines as code, test them locally, and deploy to production without configuring nodes or managing scaling policies.

Speed and latency at high concurrency

Vectorized execution and materialized views

ClickHouse processes data in batches using vectorized execution, which takes advantage of CPU cache and SIMD instructions. This approach delivers faster results, especially for queries scanning large datasets. BigQuery also uses columnar processing but optimizes for throughput over latency, making it better for batch analytics than real-time dashboards.

Both systems support materialized views to pre-compute expensive aggregations. In ClickHouse, materialized views update incrementally as new data arrives, which works well for streaming scenarios. BigQuery's materialized views refresh on a schedule or when underlying data changes significantly.

Slot scheduling vs. always-on nodes

BigQuery allocates compute using a slot-based scheduling system. When you submit a query, BigQuery assigns slots based on availability and your pricing tier, which can introduce variable latency. ClickHouse clusters run continuously with dedicated resources, providing more consistent query performance even during peak usage.

For user-facing analytics where predictable latency matters, ClickHouse's always-on architecture delivers more reliable sub-second response times. You can size your cluster based on expected concurrency rather than competing for shared slots.

Query-level caching strategies

ClickHouse caches query results in memory and serves identical queries from cache without re-executing them. The cache invalidates automatically when underlying data changes, keeping results fresh. BigQuery also caches query results for 24 hours by default, though cached results may not reflect the most recent data.

For real-time analytics where data arrives continuously, ClickHouse's cache invalidation keeps dashboards showing current data. You can also configure cache TTL policies per query to balance freshness with performance.

Cost model and predictability

On-demand scan pricing in BigQuery

BigQuery charges based on the amount of data your queries scan, $5 per TB processed. This pay-per-query model works well for occasional analytics workloads. Storage costs run separately, starting at $0.02 per GB monthly for active storage and $0.01 per GB for long-term storage untouched for 90 days.

Provisioned capacity in ClickHouse Cloud and Tinybird

ClickHouse pricing follows a resource-based model where you pay for provisioned compute and storage rather than per-query costs. Both ClickHouse Cloud and Tinybird charge based on the resources your cluster uses, making costs more predictable for high-frequency query workloads.

Tinybird's pricing bundles infrastructure, ingestion, and API hosting together. This eliminates separate services for data ingestion, query execution, and API endpoints, simplifying both cost estimation and operational overhead.

Modeling total cost of ownership

Total cost includes more than database pricing. When comparing ClickHouse and BigQuery, consider:

• Query frequency: How often you run analytics affects whether per-query or per-resource pricing costs less
• Data volume: Storage requirements and growth patterns influence both systems differently
• Operational overhead: Self-hosted ClickHouse requires DevOps time for cluster management, while managed services eliminate this cost

For real-time analytics with high query concurrency, ClickHouse's resource-based pricing often costs less than BigQuery's per-TB model. A cluster handling 10,000 queries per second costs the same whether it processes 1,000 or 10,000 queries.

Real-time ingestion and streaming workflows

Built-in Kafka, Pub/Sub, and HTTP streams

ClickHouse connects directly to Apache Kafka topics using the Kafka table engine for continuous data ingestion. You can read from multiple topics, apply SQL transformations, and write results to ClickHouse tables without external ETL tools. BigQuery integrates with Google Cloud Pub/Sub for streaming ingestion and supports direct streaming inserts via API at $50 per TB.

Exactly-once guarantees and deduplication

ClickHouse provides at-least-once delivery by default when consuming from Kafka. You can implement exactly-once processing using ReplacingMergeTree or CollapsingMergeTree table engines, which deduplicate rows based on primary key during background merges. BigQuery's streaming inserts support best-effort deduplication using an insertId field that prevents duplicates within a short time window.

Managing retention with TTL policies

ClickHouse supports TTL (time-to-live) policies that automatically delete old data based on timestamp columns. You can configure TTL at the column level to remove specific fields after a retention period, or at the table level to delete entire rows.

BigQuery handles retention through table expiration settings and partition expiration for partitioned tables. You can set an expiration time when creating a table, and BigQuery deletes the table or partition after that period. Column-level retention isn't supported, so you can't selectively remove fields while keeping the rest of a row.

Developer experience and tooling

Local dev and CI/CD with pipes and workspaces

Tinybird lets you define data pipelines as code using pipe files. These files contain SQL queries, query parameters, and API configuration in text format that works with Git. You can test pipes locally using the Tinybird CLI before deploying to production, enabling the same development practices you use for application code.

BigQuery takes a cloud-first approach where you typically develop queries in the web console or using the bq command-line tool. While you can version control SQL files, there's no built-in local runtime for testing queries without connecting to BigQuery itself.

Parameterized API endpoints for apps

Tinybird automatically generates REST API endpoints from your SQL queries, complete with parameter validation and authentication. You write SQL with templated parameters, and Tinybird handles the HTTP layer, rate limiting, and token management.

BigQuery can serve API requests through the BigQuery API or by building a custom service layer, but you're responsible for implementing the HTTP interface, parameter handling, and security. For developers building analytics into applications, Tinybird's API generation eliminates weeks of infrastructure work.

A typical developer workflow looks like:

• Local development: Write and test SQL queries using tb dev against local data
• Version control: Commit pipe files and data source definitions to Git
• CI/CD validation: Run tb build in your CI pipeline to validate syntax
• Deployment: Use tb deploy to push changes to production

Observability, tracing, and schema changes

ClickHouse provides detailed query logs and system tables showing query execution plans, resource usage, and performance metrics. You can analyze slow queries using the system.query_log table and identify optimization opportunities. Tinybird adds built-in observability with real-time metrics on API endpoint performance, query latency, and error rates.

BigQuery offers query execution details through the web console and Cloud Monitoring integration. You can see query plans, slot usage, and bytes processed for each query. Schema evolution in BigQuery allows adding columns without rewriting tables, while ClickHouse requires more careful planning for schema changes on large tables.

Common use cases and when to choose each engine

Interactive product analytics dashboards

ClickHouse works well for dashboards that update in real-time with sub-second query latency. When users filter, group, or drill down into data, they expect immediate responses. ClickHouse's vectorized execution and columnar storage deliver consistent performance even when dashboards query billions of rows.

BigQuery's higher query startup latency of 1-2 seconds minimum makes it less suitable for interactive dashboards where users actively explore data. The time spent allocating slots and initializing query execution can add seconds to each query, creating a sluggish experience.

ML feature stores and AI inference

BigQuery integrates tightly with Google's AI Platform and BigQuery ML, allowing you to train models directly on data in BigQuery without moving it. This works well for batch training jobs processing large datasets periodically.

ClickHouse excels at low-latency feature retrieval for real-time inference. When a model needs features for a prediction request, ClickHouse can return results in single-digit milliseconds, keeping end-to-end inference latency low. This matters for applications like fraud detection or recommendation engines where prediction speed affects user experience.

Long-tail ad-hoc BI queries

BigQuery excels at exploratory analysis where you don't know what queries you'll run ahead of time. Data analysts can join multiple tables, aggregate across billions of rows, and run complex analytical functions without worrying about query optimization. The serverless model handles unpredictable workloads effectively.

ClickHouse requires more upfront planning for ad-hoc queries. While it handles complex analytics, you get better performance by designing table schemas and indexes around expected query patterns.

Migration paths and ClickHouse to BigQuery sync

Dual-write pattern for zero downtime

A reliable migration approach involves writing data to both platforms simultaneously while gradually shifting read traffic. You modify your ingestion pipeline to send events to both ClickHouse and BigQuery, ensuring both systems have identical data during migration.

Batch backfill and incremental loads

For historical data, you can export tables from BigQuery to Google Cloud Storage and load them into ClickHouse using the S3 table function. ClickHouse reads Parquet files directly from S3-compatible storage, making it possible to backfill large datasets without intermediate processing.

Validating results and rolling cutover

Before cutting over production traffic, validate that both systems return identical results for important queries. Run the same queries against both BigQuery and ClickHouse and compare outputs to catch differences in data processing, time zones, or aggregation logic.

Once you've verified correctness, gradually shift query traffic from BigQuery to ClickHouse by updating application configuration or using feature flags. Monitor query performance, error rates, and resource usage during cutover to catch issues before they affect all users.

Security, compliance, and ecosystem

Data encryption and network isolation

Both systems encrypt data at rest and in transit by default. BigQuery stores data in Google's infrastructure with automatic encryption, while ClickHouse supports encryption for both storage and network communication. You can configure ClickHouse to require TLS for all client connections and encrypt data files on disk.

Network isolation works differently between platforms. BigQuery runs in Google's network and uses IAM policies to control access, while ClickHouse can be deployed in your own VPC with full control over network topology. Tinybird offers VPC peering and private endpoints for secure connectivity from your application infrastructure.

Access controls and row-level policies

BigQuery implements fine-grained access control through IAM roles and column-level security policies. You can restrict which users see specific columns or rows based on conditions like user attributes or data sensitivity labels.

ClickHouse provides role-based access control and row-level security through SQL grants and row policies. You can define policies that filter rows based on user context, allowing different users to see different subsets of the same table. However, implementing complex access control logic often requires more manual configuration than BigQuery's built-in policy framework.

Open source community and vendor ecosystem

ClickHouse benefits from an active open-source community that contributes features, fixes bugs, and shares optimization techniques. The open-source nature means you can inspect the codebase, understand exactly how queries execute, and contribute improvements. A growing ecosystem of tools supports ClickHouse, including connectors for popular data sources and visualization platforms.

BigQuery's ecosystem centers on Google Cloud Platform services. It integrates deeply with Data Studio, Looker, and other Google tools, simplifying workflows if you're already using GCP. However, you depend on Google's roadmap for new features and can't modify the underlying system.

Final thoughts and next steps

Choosing between ClickHouse and BigQuery depends on your performance requirements, operational preferences, and cost constraints. ClickHouse delivers faster query performance for real-time analytics applications where sub-second latency and high concurrency matter. BigQuery offers more flexibility for ad-hoc analysis and eliminates operational overhead through its serverless architecture.

For developers building real-time analytics into applications, ClickHouse provides the performance characteristics needed for user-facing dashboards and APIs. Tinybird makes ClickHouse accessible without infrastructure complexity, offering a developer experience that prioritizes speed and ease of integration. You can define data pipelines as code, test locally, and deploy production APIs in minutes rather than weeks.

If you're evaluating ClickHouse for real-time analytics, consider starting with Tinybird's managed service to avoid infrastructure complexity while maintaining performance benefits. Sign up for a free Tinybird plan to test ClickHouse capabilities without setting up clusters or managing DevOps.

FAQs about ClickHouse vs BigQuery

Can I use BigQuery for cold storage and ClickHouse for hot data?

Yes, many organizations implement a tiered architecture where ClickHouse handles real-time queries on recent data while BigQuery stores historical data for long-term analysis. This approach optimizes both performance and cost by using each system for what it does best.

Does ClickHouse support standard SQL syntax like BigQuery?

ClickHouse supports most standard SQL operations but uses its own SQL dialect with some differences from BigQuery's syntax. Migration typically requires query rewrites, though basic SELECT statements often work with minimal changes.

How do Tinybird's usage limits compare to BigQuery's quotas?

Tinybird provides predictable resource-based limits tied to your plan, while BigQuery uses slot-based quotas that can vary based on demand. Tinybird's approach offers more consistent performance for real-time applications where query latency matters.