Why Use ClickHouse Over BigQuery

Why should I use ClickHouse instead of BigQuery?

ClickHouse delivers sub-second analytics at lower, predictable cost when you control infrastructure, whereas BigQuery excels at fully-managed, bursty workloads.

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Description

Example H2

Example H3

Why choose ClickHouse instead of BigQuery for high-volume analytics?

ClickHouse’s columnar engine pushes vectorized execution directly on local disks or NVMe, delivering millisecond scans for billions of rows. Teams needing always-on dashboards or user-facing analytics benefit from this raw speed without per-query billing.

Is ClickHouse faster than BigQuery for real-time dashboards?

Yes, because data sits on your own cluster with no cold start.A MergeTree table can read compressed blocks straight from disk, while BigQuery often stages data from Colossus storage to slots, adding latency.

How does ClickHouse pricing compare to BigQuery on-demand?

ClickHouse is capacity-based. You pay for the nodes you run, not the bytes you scan. Heavy, predictable workloads typically run 3-10× cheaper than BigQuery’s on-demand pricing, which charges per read column-byte.

Can ClickHouse run on-premises and in Kubernetes?

Yes.ClickHouse can be self-hosted on bare metal, VMs, or Kubernetes with operators like Altinity or ClickHouse Cloud for SaaS convenience.BigQuery is only available as a Google-managed service.

When should I still pick BigQuery?

Choose BigQuery when workloads are spiky, teams lack ops bandwidth, or you need seamless integration with other GCP services (Dataflow, Looker, Vertex AI) and international compliance managed by Google.

What does a ClickHouse vs BigQuery query look like?

Syntax is similar ANSI SQL, but ClickHouse adds ENGINE, SETTINGS, and TTL clauses while BigQuery relies on OPTIONS and partition decorators.See examples below.

ClickHouse example

CREATE TABLE Orders (id UInt64, customer_id UInt64, order_date DateTime, total_amount Decimal(10,2)) ENGINE=MergeTree() PARTITION BY toYYYYMM(order_date) ORDER BY (customer_id, order_date);

SELECT customer_id, sum(total_amount) AS lifetime_valueFROM OrdersGROUP BY customer_idORDER BY lifetime_value DESCLIMIT 10;

BigQuery equivalent

CREATE OR REPLACE TABLE `myproj.ecom.Orders` (id INT64, customer_id INT64, order_date TIMESTAMP, total_amount NUMERIC) PARTITION BY DATE(order_date);

SELECT customer_id, SUM(total_amount) AS lifetime_valueFROM `myproj.ecom.Orders`GROUP BY customer_idORDER BY lifetime_value DESCLIMIT 10;

Why Why Use ClickHouse Over BigQuery is important

Why Use ClickHouse Over BigQuery Example Usage


-- ClickHouse: Top 5 products by revenue
SELECT p.name, sum(oi.quantity * p.price) AS revenue
FROM OrderItems oi
JOIN Products p ON p.id = oi.product_id
GROUP BY p.name
ORDER BY revenue DESC
LIMIT 5;

Why Use ClickHouse Over BigQuery Syntax


ClickHouse: 
CREATE TABLE Orders (
    id UInt64,
    customer_id UInt64,
    order_date DateTime,
    total_amount Decimal(10,2)
) ENGINE = MergeTree()
PARTITION BY toYYYYMM(order_date)
ORDER BY (customer_id, order_date);

BigQuery:
CREATE OR REPLACE TABLE `project.dataset.Orders` (
    id INT64,
    customer_id INT64,
    order_date TIMESTAMP,
    total_amount NUMERIC
) PARTITION BY DATE(order_date);

ClickHouse query:
SELECT customer_id, sum(total_amount) AS lifetime_value
FROM Orders
GROUP BY customer_id
ORDER BY lifetime_value DESC
LIMIT 10;

BigQuery query:
SELECT customer_id, SUM(total_amount) AS lifetime_value
FROM `project.dataset.Orders`
GROUP BY customer_id
ORDER BY lifetime_value DESC
LIMIT 10;

Common Mistakes

Assuming ClickHouse is always cheaper. If clusters are oversized or replication is misconfigured, infrastructure cost can exceed BigQuery. Right-size nodes and use compression settings.
Ignoring maintenance overhead. ClickHouse requires monitoring, backups, and upgrades. Use managed ClickHouse Cloud or operators to automate routine tasks.