BigQuery vs Redshift: Comparing Costs, Performance & Scalability

Differences Between BigQuery and Redshift

After a brief overview of these two cloud data warehouses, let’s closely examine their differences in different areas. 

Architecture

The platform architecture outlines how systems should function. Here, I will highlight the distinction between BigQuery's serverless, query-based pricing model and Redshift's cluster-based approach.

BigQuery

If you prefer a hands-off approach with automatic scaling, BigQuery is your go-to for data warehousing.

BigQuery allows you not to manage any infrastructure; Google handles everything from provisioning to scaling. With BigQuery, you only pay for the queries you run and the storage you use. This pay-as-you-go pricing approach is cost-effective and helps you not incur idle resource costs.

BigQuery architecture (Source: Google Cloud blog).

Redshift

If you need more control over your infrastructure and can manage your clusters effectively, Amazon Redshift will be a better fit for you. Redshift requires you to set up and manage clusters by choosing the instance type, number of nodes, and configuration. This gives you control over the infrastructure, but, in my experience, it also adds complexity.

Redshift offers both a reserved and on-demand pricing approach. With reserved instances, you receive a discount and commit to a specific capacity for a predetermined period (such as one or three years). On-demand pricing allows you to pay for the capacity you use hourly, but improper management can make it more costly.

Amazon Redshift architecture (Source: AWS).

Performance

Both Google BigQuery and Amazon Redshift provide impressive performance for large-scale queries, but they perform best in different cases. Let's look at how both platforms manage performance for large-scale queries, highlighting BigQuery's optimized performance for ad-hoc queries and Redshift's control over clusters for predictable workloads.

BigQuery

BigQuery is built to easily handle dynamic workloads due to its serverless architecture. This allows BigQuery to autoscale workloads, enabling high performance for large-scale ad-hoc queries. BigQuery's columnar storage is highly efficient for analytical queries. This format reduces the amount of data read from the disk, speeding up query performance.

Redshift

Redshift can be a better option if you can manage clusters for reliable performance in environments with predictable workloads. You can tune clusters for consistent query performance, ensuring your resources are optimized for your business requirements. 

Redshift offers various performance tuning options, such as sort and distribution keys, to optimize query execution. This feature can lead to better performance for predictable workloads, but only if you know what you’re doing! In my experience, the learning curve can be steep. 

Cost structure

Understanding price and cost structures is essential when selecting a data warehouse because we want to be responsible for every dollar we spend. 

Let’s review how Google BigQuery's pay-per-query model and storage expenses compare to Amazon Redshift's cluster-based pricing with reserved instance savings:

Scalability

One of the most important factors we should consider while selecting our data warehouse is scalability. 

Let's examine how BigQuery automatically increases storage and computing capacity in response to demand and how Redshift demands manual cluster scaling, which can take longer.

BigQuery

BigQuery is the preferred platform when you are sure your business will expand along with its workloads and infrastructures. BigQuery's autoscaling functionality relieves you of this burden, saving you time and effort so you can concentrate entirely on data analysis.

Redshift

In ideal circumstances, Redshift will be better if your company has enough data engineers. Although Redshift demands more active management, it might be advantageous for your company, particularly if you need more precise control and wish to manage resources. 

The drawback is that management takes a lot of time, even if it gives you freedom. Your workflow may become more complex due to the requirement to plan, track performance, and act when scale is required.

Ecosystem integration

Both Google BigQuery and Amazon Redshift offer benefits specific to their ecosystems when integrating with their cloud computing platforms.

BigQuery

BigQuery works smoothly for teams using GCP and its services, such as Google Compute Engine, Cloud Storage, and Cloud Run; then, it may be beneficial to use BigQuery to keep your data pipelines within the same environment. 

This integration with Google's suite of tools and services makes BigQuery the preferred option for data warehousing if your business already uses the Google ecosystem because it offers a smooth workflow with its services.

Redshift

Amazon Redshift will sync well with other services in the AWS ecosystem. It can integrate with Amazon S3, AWS Lambda, and AWS Glue, giving you easy access to other AWS services and resources. In my opinion, this is a great advantage!

Ease of use

The key difference between Google BigQuery and Amazon Redshift is the operational responsibility these services place on their customers.

BigQuery

Since we don’t need to worry about managing underlying infrastructure, Google handles everything from provisioning to scaling using BigQuery. This feature makes BigQuery stand out for businesses with few infrastructure engineers trying to avoid operational responsibilities.

Redshift

Redshift, on the other hand, demands more technical know-how and expertise. If your team has infrastructure engineers, there will be fewer issues handling backups, manual scaling, and provisioning clusters. As a business, this gives you control and flexibility over your infrastructure.

When to Use BigQuery

There are various use cases and scenarios where Google BigQuery becomes the go-to data warehousing solution. Choose BigQuery if you:

• Already use Google Cloud services. 

Since it is built on the Google Cloud Platform, BigQuery is more compatible with individuals who are heavily invested in GCP. Suppose you have most of your resources within the Google Cloud Platform, such as Google Compute Engine, Cloud Storage, and Cloud Run. Using your data pipelines within the same environment may be beneficial. 

• Require real-time analytics or ad-hoc queries.


BigQuery is a powerful tool for handling large datasets for ad-hoc queries or real-time analytics. Since you don’t need to worry about managing infrastructure, this ensures that your queries are processed fast and efficiently; your workload auto-scales regardless of the size or complexity of your data.

• Lack of DevOps resources to manage infrastructure.


If your team doesn’t have the necessary DevOps resources, BigQuery is a clear winner here. You don’t need to bother yourself with the technicality of managing infrastructures; Google does that for you. This helps you focus solely on the data insights.

When to Use Redshift

There are some scenarios and use cases where Amazon Redshift is the clear choice data warehousing solution. Choose Redshift if you:

• Are heavily invested in the AWS ecosystem. 

If your organization has deployed its resources and integrated into the AWS ecosystem, Redshift is a natural fit. Amazon Redshift will work with other AWS services like Amazon S3, AWS Lambda, and AWS Glue, making it possible to rely solely on AWS for data management, such as data storage, processing, and automation needs.

• Require consistent performance for predictable query patterns. 

Redshift is compatible with heavy workloads that demand consistent performance for predictable query patterns and a smooth and efficient run. Since Redshift’s clusters are customizable and you can control the infrastructure, you can tune your Redshift to meet any specific performance requirements.

• Have ETL-heavy workflows and strong infrastructure management capabilities. 

If your team handles complex ETL workflows with data engineers who manage infrastructure, then Redshift is the right fit. This suits companies with DevOps expertise well for handling heavy workflows; you will have control over scaling, backups, and performance.

BigQuery vs. Redshift: Summary Table

Now that we’ve reviewed some significant components of both tools let's review their key highlights. This should help in deciding which tool to utilize for your specific needs:

Conclusion

This article explored the key comparisons between BigQuery and Redshift, two cloud data warehousing solutions with unique strengths and trade-offs. The best choice depends on your needs, including data volume, query patterns, and budget.

If you're interested in diving deeper into these platforms, check out Introduction to Redshift and Introduction to BigQuery on DataCamp. These hands-on courses will help you master the fundamentals of each tool and gain practical skills to work effectively with modern data warehouses.