How to Choose BigQuery over ClickHouse in PostgreSQL

Why use BigQuery over ClickHouse for analytics?

Explains practical reasons, cost, and feature differences that make BigQuery a better fit than ClickHouse for certain large-scale analytics workloads.

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Description

Why pick BigQuery instead of ClickHouse?

BigQuery eliminates cluster sizing and node management; Google’s serverless architecture auto-scales storage and compute, letting teams focus on SQL, not infra. ClickHouse requires manual cluster tuning, replicas, and shard planning, which slows small teams.

Does BigQuery’s pricing model lower total cost?

Yes. BigQuery charges for scanned bytes and stored data. You avoid paying for idle CPUs that ClickHouse clusters accrue during off-peak hours. Flat-rate reservations give predictable spend for heavy dashboards without over-provisioning.

How does serverless impact bursty ecommerce traffic?

Holiday sales jolts demand suddenly. BigQuery scales to thousands of slots instantly, ensuring ‘Orders’ and ‘OrderItems’ reports stay fast without pre-sizing shards. ClickHouse needs advance replica capacity or queries queue.

What SQL features help ecommerce analytics?

BigQuery supports ARRAYs and STRUCTs, helpful for JSON product specs. Standard SQL window functions, APPROX_TOP_COUNT, and ML.EXPLAIN let analysts build retention funnels and pricing models without external ETL. ClickHouse lacks built-in ML.

How do I migrate ClickHouse queries to BigQuery?

Replace ClickHouse’s "ARRAY JOIN" with BigQuery’s UNNEST, swap LIMIT n BY for QUALIFY ROW_NUMBER(), and use PARTITION BY ORDER BY in window functions. Re-write MergeTree tables as partitioned tables on order_date.

Can I keep real-time inserts?

Yes. Stream events into a BigQuery ingestion-time partitioned table. Use MATERIALIZED VIEW for fast aggregates, replacing ClickHouse materialized views.

Best practices for BigQuery adoption

Set table partitioning on order_date, cluster on customer_id; enable table expiration for staging data; use parameterized queries in Galaxy SQL editor to reduce scanned bytes.

Common mistakes to avoid

See below.

Why How to Choose BigQuery over ClickHouse in PostgreSQL is important

How to Choose BigQuery over ClickHouse in PostgreSQL Example Usage


-- Identify top 5 customers by lifetime spend in BigQuery
SELECT
  c.id,
  c.name,
  SUM(o.total_amount) AS lifetime_spend
FROM `ecom.Customers` c
JOIN `ecom.Orders` o ON o.customer_id = c.id
GROUP BY c.id, c.name
ORDER BY lifetime_spend DESC
LIMIT 5;

How to Choose BigQuery over ClickHouse in PostgreSQL Syntax


-- BigQuery example: daily revenue per product
SELECT
  p.name,
  SUM(oi.quantity * p.price) AS daily_revenue
FROM `ecom.Orders` o
JOIN `ecom.OrderItems` oi ON oi.order_id = o.id
JOIN `ecom.Products`   p ON p.id = oi.product_id
WHERE o.order_date = DATE_SUB(CURRENT_DATE(), INTERVAL 1 DAY)
GROUP BY p.name;

-- ClickHouse equivalent for comparison
SELECT
  p.name,
  SUM(oi.quantity * p.price) AS daily_revenue
FROM Orders o
INNER JOIN OrderItems oi ON oi.order_id = o.id
INNER JOIN Products p ON p.id = oi.product_id
WHERE o.order_date = yesterday()
GROUP BY p.name;

Common Mistakes

Ignoring partitioning: Loading ‘Orders’ as an unpartitioned table skyrockets scan costs. Fix by partitioning on order_date and clustering on customer_id.
Using SELECT * for ad-hoc analysis: This scans entire tables and defeats BigQuery’s cost model. Always select needed columns or use APPROX_COUNT_DISTINCT.