ClickHouse^® vs BigQuery: real-time analytics comparison 2025

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.

Storage efficiency, compression and semi structured workloads

Columnar compression and storage footprint

ClickHouse uses a columnar storage engine that lets you pick specific compression codecs per column - for example LZ4, ZSTD or more specialized codecs like Delta y DoubleDelta - so you can align storage with the actual data pattern. This results in very high compression ratios, fewer bytes read per query and therefore lower latency for analytical workloads that scan a lot of rows frequently. Because data of the same type sits together, CPU cache efficiency also improves, which is essential for real-time dashboards and time-series analytics.BigQuery also stores data in columns and applies automatic compression through its Capacitor format, which is convenient because you do not have to choose codecs. However, since you cannot fine tune compression at column level, you have less control over how much you can reduce disk usage for a very specific workload.In scenarios with highly repetitive numeric data or event streams with similar shapes, ClickHouse can typically achieve a smaller storage footprint while still serving sub second queries.

Arrays, nested structures and event shaped data

Modern analytics workloads often receive events that contain lists of items, optional attributes or nested objects. ClickHouse supports Array and Nested types natively, and it ships with functions to filter, aggregate and transform those structures directly in SQL. That means you can ingest the event as it arrives and still query it efficiently without normalizing everything into several tables first.BigQuery supports ARRAY and STRUCT types and makes them easy to query with standard SQL, which is helpful when analysts explore data. The difference is that in ClickHouse those array and nested fields still benefit from the storage order and compression choices, so performance stays high even when the data model is not perfectly flattened. For clickstream, observability, IoT or product analytics data this combination of flexibility and performance is very valuable.

Semi structured and JSON ingestion

A lot of real time pipelines start with JSON events. ClickHouse can ingest JSON directly, keep the original payload and later materialize only the fields you need as separate columns. You can then query those columns at full speed while still having the raw event available for future use. Functions like JSONExtract* let you project new fields without reloading the whole dataset. This is useful when the upstream team adds attributes and you want to start using them right away.BigQuery also lets you query JSON like data and is very comfortable when sources live in Google Cloud Storage or Pub/Sub. The distinction here is that ClickHouse combines JSON handling with column level compression and sorting, so even if your events evolve over time you do not lose the storage and speed benefits.

Selective retention with TTL

ClickHouse supports TTL policies that can be applied at table level or column level. With a column level TTL you can delete only large or sensitive fields after a given period and still keep the rest of the row for historical analytics. That lets you control storage growth very precisely and also helps with privacy requirements.BigQuery offers table and partition expiration so you can manage lifecycle easily, but it does not let you expire just one column and keep the others. For high volume streaming scenarios where you ingest everything first and then decide what to keep, ClickHouse’s TTL model is more granular.

Cost and performance implication

Because you can choose codecs, define sort keys and apply TTLs per column, ClickHouse lets you tune the system to get a good balance between disk usage, query speed and freshness. You can keep recent partitions on faster storage and older partitions in cheaper object storage and still query them. This keeps total cost of ownership low when you have constant ingestion and constant querying.BigQuery keeps the model simpler and predictable inside its managed environment, which is attractive for teams that do not want to operate databases. For high frequency, user facing analytics though, the extra control that ClickHouse provides at storage level often results in better price performance and more stable latencies.Before comparing ClickHouse and BigQuery, it is worth clarifying what a database is in general terms — a structured system designed to store, manage and retrieve data efficiently, forming the backbone of modern analytics and data-driven applications.***

Replication, deployment flexibility and operational control

Replication and high availability

ClickHouse implements replication through ReplicatedMergeTree family tables. You can have several replicas inside the same shard, which gives you redundancy, fault tolerance and fast recovery if a node fails. Replication is asynchronous but designed to keep consistency across nodes while still allowing very fast inserts, which is important when you are ingesting streaming data and exposing it immediately through APIs.BigQuery provides regional and multiregional replication inside Google’s infrastructure, which gives strong durability and availability guarantees with almost no effort from the user. The main difference is that it is fully abstracted. In ClickHouse you can align replication with how your application expects to read data in the same region and keep latency low for interactive workloads.

Deployment models and level of control

With ClickHouse you can run the database self hosted, inside your own VPC, in containers, in ClickHouse Cloud or through a managed platform, as explained in detail in ClickHouse deployment options.That means you decide how much control you want over network topology, instance types, disks and scaling policies. If you need strict isolation, private networking or custom hardware to get more IOPS, you can do it.BigQuery is fully managed and runs only in Google Cloud. You do not manage nodes, storage, networks or upgrades. This is ideal if you want to forget about infrastructure, but it also means you cannot tune the underlying resources for extremely latency sensitive API workloads. With ClickHouse and a managed layer on top, you still get low latency and you know exactly what resources you are paying for.

Concurrency behavior and scheduling

ClickHouse clusters run on dedicated always on resources. As long as the cluster is sized properly you can serve hundreds or thousands of queries per second and the latency will be very similar from one request to the next. The system will only slow down when CPU, memory or disk bandwidth are saturated, so it is easy to predict when you need to scale out.BigQuery uses a slot based scheduler. Every query consumes slots. If there are no slots available in your reservation, the query waits or is throttled. This is fine for internal analytics where waiting a bit is acceptable.For user facing applications where every extra second hurts the experience, the predictable behavior of ClickHouse on reserved resources is usually a better fit.

Ingestion surface and streaming pattern

ClickHouse offers native ingestion from Kafka, HTTP, batch files and S3 compatible storage. You can connect a Kafka topic, create a materialized view that transforms the data on the fly and land it into a MergeTree table ready to be queried. This pattern gives you streaming ingest and streaming transform in the same engine without extra services.BigQuery integrates very well with Google Cloud services like Pub/Sub or Dataflow and can also accept streaming inserts, although streaming has its own pricing and can invalidate cached results. When you combine very frequent ingestion with very frequent querying this makes a difference. In ClickHouse both operations are designed to coexist.

Lightweight updates and late arriving data

Although columnar databases are optimized for append only workloads, ClickHouse supports mutations and engines like CollapsingMergeTree or ReplacingMergeTree that let you deduplicate or update rows later without a full rewrite of the table. The mutation happens in the background and does not block reads.BigQuery also supports UPDATE and DELETE, but these operations are more expensive because they often rewrite big portions of the table or partition and they consume slots. For event pipelines where you sometimes need to correct data or collapse duplicates, ClickHouse gives you a lighter mechanism.

Observability, system tables and troubleshooting

ClickHouse exposes extensive system tables, query logs and execution statistics. You can see which queries are slow, how much data they read, how much memory they used and what parts of the table they accessed. This makes it straightforward to identify a bad query or a missing index and fix it.BigQuery offers good visibility in the console and through Cloud Monitoring, but many low level details remain hidden. That is expected in a serverless service. If your workload is latency critical and you want to tune it aggressively, having that extra transparency from ClickHouse is an advantage.

Hybrid and multi cloud flexibility

Because ClickHouse is open source and supports object storage backends, you can run it in different clouds or on premises and keep the same query engine. This is useful when you need data locality, when you want to avoid lock in or when you prefer to bring the compute close to the data that already lives in your VPC.BigQuery is tied to Google Cloud. You can query external sources but the execution model remains inside GCP. If your architecture roadmap includes hybrid or multi cloud analytics, ClickHouse gives you more room to evolve.

Scaling and pricing alignment

Scaling ClickHouse is straightforward - add more nodes or give more resources to existing ones and throughput increases. Pricing then follows a resource based model where you pay for the compute and storage you reserve. There is no per query fee, so high concurrency workloads become much easier to budget.BigQuery usually charges per TB scanned or via slot reservations. This is very good for ad hoc analytics because you only pay for what you query, but it can become expensive when you have an application that is constantly running queries or streaming data. In those cases ClickHouse’s fixed resource model plus a managed control plane is often more predictable.

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.