BigQuery vs Redshift: Key Differences and When to Use Each

BigQuery is a fully managed, serverless data warehouse by Google Cloud that scales automatically and charges by data processed, while Redshift is Amazon's managed data warehouse that requires cluster management and charges by reserved capacity. BigQuery excels in ease of use and scalability, whereas Redshift offers more control over infrastructure and can be cost-effective for steady workloads.

Quick Comparison

Here is a quick side-by-side comparison of BigQuery and Redshift on key factors.

Factor	BigQuery	Redshift
Service Type	Serverless, fully managed	Managed cluster-based
Scaling	Automatic, on-demand	Manual, via cluster resizing
Pricing Model	Pay per query (data scanned)	Pay per cluster node-hour
Data Storage	Separation of storage and compute	Tightly coupled storage and compute
Performance Optimization	Automatic query optimization	User-managed distribution styles and sort keys
Integration	Native GCP services	Native AWS services

Key Differences

BigQuery is designed as a serverless data warehouse, meaning you don't have to manage any servers or clusters. It automatically scales to handle any amount of data and charges you based on how much data your queries scan. This makes it very easy to start and scale without upfront capacity planning.

Redshift, on the other hand, requires you to provision and manage clusters of nodes. You pay for the cluster size regardless of usage, which can be cost-effective for steady workloads but less flexible for variable demand. Redshift stores data on the cluster nodes, so storage and compute are tightly linked.

BigQuery separates storage and compute, allowing independent scaling and potentially better cost control. Redshift requires manual tuning of distribution styles and sort keys to optimize query performance, while BigQuery handles optimization automatically. Both integrate well with their cloud ecosystems, with BigQuery fitting naturally into Google Cloud and Redshift into AWS.

Code Comparison

Example: Query to get total sales by product category.

sql

SELECT product_category, SUM(sales_amount) AS total_sales
FROM sales_data
GROUP BY product_category
ORDER BY total_sales DESC
LIMIT 10;

Output

A table showing top 10 product categories with their total sales sums.

Redshift Equivalent

The same SQL query runs in Redshift with identical syntax.

sql

SELECT product_category, SUM(sales_amount) AS total_sales
FROM sales_data
GROUP BY product_category
ORDER BY total_sales DESC
LIMIT 10;

Output

A table showing top 10 product categories with their total sales sums.

When to Use Which

Choose BigQuery when you want a fully managed, serverless experience that scales automatically and you prefer paying per query without managing infrastructure. It is ideal for variable workloads and quick setup.

Choose Redshift when you need more control over your data warehouse infrastructure, have steady workloads, and want to optimize costs by managing cluster size. It fits well if you are already invested in AWS services.

Key Takeaways

BigQuery is serverless and scales automatically, charging per query data scanned.

Redshift requires cluster management and charges per node-hour, better for steady workloads.

BigQuery separates storage and compute; Redshift tightly couples them.

BigQuery automates performance tuning; Redshift needs manual optimization.

Choose BigQuery for ease and scalability; choose Redshift for control and AWS integration.