How to Choose BigQuery Over Redshift in PostgreSQL

Why use BigQuery over Redshift?

BigQuery’s serverless, autoscaling architecture, pay-per-scan pricing, and native support for semi-structured data often make it a better choice than Amazon Redshift.

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Description

Example H2

Example H3

Why use BigQuery instead of Redshift?

BigQuery eliminates cluster management, autoscales to zero, and charges only for data scanned. Redshift requires node sizing, resizing, and reserved-instance planning—overhead many teams want to avoid.

Does serverless architecture cut ops costs?

Yes. BigQuery handles provisioning, patching, and tuning automatically. Teams focus on SQL, not cluster health or WLM queues.

Can BigQuery separate storage and compute better?

Compute slots scale independently of Colossus storage. You never buy more storage to get CPU.Redshift Serverless narrows the gap but still bundles capacity tiers.

How does pay-per-scan pricing work?

You pay $5 per TiB of data processed, not for idle hardware. Predictable cost controls like MAX_BYTES_BILLED and table partitioning limit spend.

What if my data is semi-structured?

BigQuery stores JSON natively and supports UNNEST for array handling. Redshift needs SUPER/JSON columns with Spectrum or late-binding views, adding complexity.

Do I lose PostgreSQL-style SQL features?

No. BigQuery supports standard SQL with window functions, CTEs, and STRUCT types.Minor syntax tweaks—mainly date functions—are easy to learn.

Is regional availability a concern?

Both warehouses cover major regions.BigQuery’s default multi-region datasets simplify DR; Redshift needs cross-region snapshots.

Practical example: querying ecommerce data

Run ad-hoc analysis without provisioning a cluster:

-- Estimate daily revenue SELECT order_date, SUM(total_amount) AS daily_revenue FROM `ecom.Orders` GROUP BY order_date ORDER BY order_date;

Add a bytes cap to avoid runaway scans:

#standardSQL DECLARE opt OPTIONS(max_bytes_billed=1e9); SELECT COUNT(*) FROM `ecom.OrderItems`;

Best practices for migration

Export Redshift tables to Parquet in S3, load into BigQuery with gcloud bq load, then convert sort/dist keys to partitioned and clustered tables.

Replace Redshift’s sortkey with BigQuery PARTITION BY DATE(order_date) and CLUSTER BY customer_id for similar performance.

Why How to Choose BigQuery Over Redshift in PostgreSQL is important

How to Choose BigQuery Over Redshift in PostgreSQL Example Usage


-- Find top products by revenue in BigQuery
SELECT p.name AS product,
       SUM(oi.quantity * p.price) AS revenue
FROM   `ecom.OrderItems`  oi
JOIN   `ecom.Products`    p ON p.id = oi.product_id
GROUP  BY product
ORDER  BY revenue DESC
LIMIT 5;

How to Choose BigQuery Over Redshift in PostgreSQL Syntax


BigQuery standard SQL example using ecommerce tables:

SELECT
    c.name,
    SUM(oi.quantity * p.price) AS lifetime_value
FROM `ecom.Customers` c
JOIN `ecom.Orders` o   ON o.customer_id = c.id
JOIN `ecom.OrderItems` oi ON oi.order_id = o.id
JOIN `ecom.Products` p    ON p.id = oi.product_id
WHERE o.order_date BETWEEN '2024-01-01' AND '2024-06-30'
GROUP BY c.name
ORDER BY lifetime_value DESC
LIMIT 10;

Options and parameters:
• PARTITION BY <column|expression> – create ingest-time or column partitions.
• CLUSTER BY <col1, col2> – sort data for predicate filtering.
• MAX_BYTES_BILLED=<int> – caps query cost.
• OPTIONS(job_priority='BATCH') – runs the query in cheap batch mode.

Common Mistakes

Ignoring cost caps—running a SELECT * without MAX_BYTES_BILLED can scan terabytes. Always set a bytes limit or preview table size first.
Lifting Redshift sort/dist keys directly—BigQuery needs PARTITION BY and CLUSTER BY instead. Redesign schemas for columnar scans, not row storage.