GCP’s BigQuery vs. Amazon Redshift: A Performance and Cost Comparison

As businesses collect, process, and analyze data at increasingly massive scales, choosing the right cloud data warehouse becomes a crucial decision. Two of the most popular solutions in the market are Google Cloud Platform’s BigQuery and Amazon Web Services’ Redshift. Both offer powerful data analytics capabilities but differ in terms of performance, pricing, architecture, and real-world applicability.

In this blog, we'll break down these differences, compare real-world use cases, and give you a guide to help make the best decision for your organization. Let's dive into the performance and costs of BigQuery and Redshift, and how they measure up to real business needs.

Understanding Cloud Data Warehousing

Before diving into the technicalities, let’s briefly understand the significance of cloud data warehouses. They are essential in the modern data landscape because they help businesses store, manage, and analyze large amounts of data without the need to maintain on-premise infrastructure. This results in cost savings, faster data insights, and flexibility in handling changing data volumes.

Cloud data warehouses like BigQuery and Redshift enable businesses to run analytical queries on massive datasets in seconds or minutes, where traditional databases might take hours or days. Let’s now compare the architecture of these two platforms, as this is crucial to understanding their performance and cost implications.

BigQuery vs. Redshift: A Step-by-Step Architecture Breakdown

BigQuery: A Serverless, Scalable Approach

BigQuery is a fully managed, serverless data warehouse that automates infrastructure provisioning and scales resources dynamically based on query needs. This architecture is designed to abstract away the complexities of managing a warehouse while offering practically limitless scaling capabilities.

● Separation of Storage and Compute: BigQuery’s architecture is built on a separation of storage and compute. Storage is handled by Colossus, Google’s global distributed file system, which provides high durability and infinite scalability. Compute resources, on the other hand, are provisioned automatically when queries are executed, meaning you only pay for the data processed.

● Query Execution: BigQuery uses Dremel, a query execution engine, which allows for parallel querying across thousands of nodes, resulting in faster query times. This engine is optimized for ad-hoc, analytical queries that scan large datasets.

● Multi-cloud Flexibility: One of BigQuery's standout features is its multi-cloud support, allowing you to query data across AWS, Azure, and Google Cloud without having to move or duplicate data.

Strengths of BigQuery's Architecture:

● No infrastructure management: No need to provision or manage clusters or nodes.

● Dynamic scaling: Handles huge data loads automatically, making it ideal for variable workloads.

● Pay-as-you-go pricing: You only pay for the resources you use, both in terms of storage and queries.

Amazon Redshift: A Cluster-Based Data Warehouse

In contrast, Amazon Redshift operates on a cluster-based architecture, where users must provision and manage nodes. It follows a more traditional approach to cloud data warehousing.

● Cluster and Node Setup: Redshift requires users to choose and configure clusters with nodes, which have their own compute and storage resources. Data is distributed across the nodes within a cluster, and performance depends on the cluster configuration (type and number of nodes).

● Columnar Storage: Redshift stores data in a columnar format, which allows for faster query execution as only the relevant columns are read during queries. This is optimized for data warehousing workloads.

● RA3 Nodes: With the RA3 node type, Redshift now supports a separation of compute and storage. RA3 nodes allow you to scale compute and storage independently, much like BigQuery. However, Redshift’s approach to this is not fully automated, as users must still manage clusters and storage scaling manually.

Strengths of Redshift's Architecture:

● Cluster-based control: Offers granular control over compute resources, which can be advantageous for predictable, consistent workloads.

● RA3 Nodes: Flexibility to scale storage and compute separately with newer node types.

● Optimized for structured data: Redshift excels when querying well-organized, structured datasets.

Performance: BigQuery vs. Redshift

Query Speed and Optimization

When it comes to query speed, both platforms have distinct strengths depending on the workload.

● BigQuery is optimized for ad-hoc queries and unpredictable workloads. Thanks to its dynamic scaling capabilities, BigQuery can handle huge datasets without the need for performance tuning or manual provisioning. Queries are executed in parallel across multiple nodes automatically, delivering faster results for large-scale analytics tasks.

● Redshift performs exceptionally well for structured workloads that can benefit from cluster optimization. For businesses running consistent, repetitive queries on structured datasets, Redshift allows you to optimize cluster sizes for the best performance. Redshift’s Materialized Views and Automatic Table Optimization features can speed up query times considerably when used correctly.

Real-World Case:

● A large retail company might use BigQuery to analyze unstructured clickstream data from their eCommerce website, as traffic can vary greatly and BigQuery’s serverless architecture scales to meet demand.

● Meanwhile, a financial services firm that runs daily reports on structured transactional data would benefit from Redshift, as it can provision just the right amount of compute resources for predictable workloads.

Scalability

● BigQuery has an advantage in scalability because of its serverless model. It automatically scales storage and compute resources independently, which is great for companies with growing or fluctuating data needs. There’s no need to worry about over-provisioning or under-provisioning clusters.

● Redshift offers scalability, but it requires manual intervention. As data grows, users need to manually resize clusters or add nodes. Redshift’s newer RA3 instances decouple storage from compute, allowing for greater flexibility, but scaling compute still requires some degree of user oversight.

Cost Comparison: BigQuery vs. Redshift

Pricing Models

BigQuery uses a pay-as-you-go pricing model. This is advantageous for businesses with unpredictable workloads, as you only pay for the amount of data processed during queries and the storage consumed. BigQuery also offers flat-rate pricing for enterprises that want to purchase a set amount of compute resources for a fixed monthly price, which helps control costs for large-scale projects.

Redshift uses a cluster-based pricing model, which means you pay for the nodes you provision, regardless of whether they are fully utilized. This can result in overpaying if the clusters are underutilized. However, if workloads are steady and predictable, Redshift’s pricing can be more cost-effective. Redshift also offers Reserved Instances (RIs), which provide discounts for long-term commitments (1 or 3 years).

Real-World Cost Example

Example 1: Ad-hoc Queries with BigQuery A media company wants to run analytics on audience engagement across multiple platforms. With unpredictable traffic and data loads, BigQuery is the better option because it scales automatically, and they only pay for the data they query. They avoid costs associated with provisioning and managing clusters.

Example 2: Consistent Workloads with Redshift An enterprise that runs daily financial reports might choose Redshift. They can optimize cluster sizes to fit their structured queries and leverage Reserved Instances to lower long-term costs.

Security and Compliance

Both platforms offer strong security features, including encryption (both at rest and in transit), role-based access control, and VPC integration.

● BigQuery integrates with Google Cloud’s identity and access management (IAM) for fine-grained security controls and supports HIPAA and PCI-DSS compliance. It also offers Data Loss Prevention and Cloud Armor for protection against threats.

● Redshift offers similar encryption standards and integrates with AWS services for access control. It also meets compliance standards like HIPAA, SOC, FedRAMP, and PCI DSS.

Conclusion: Which Should You Choose?

Choosing between BigQuery and Redshift comes down to your business needs:

● Choose BigQuery if:

○ You need a serverless solution that automatically scales.

○ Your workloads are unpredictable and require fast, ad-hoc querying.

○ You prefer a pay-as-you-go pricing model without cluster management.

● Choose Redshift if:

○ You have structured, consistent workloads.

○ You prefer more control over your cluster configuration.

○ You can leverage Reserved Instances for cost savings.

Both platforms are robust, scalable solutions that can handle massive datasets, but their architectural differences mean they fit different types of workloads.

Disclaimer

The views expressed in this blog are based on general comparisons between Google Cloud’s BigQuery and AWS Redshift as of 2024. Prices and features may change over time, and specific use cases should be consulted with cloud providers for accurate information.

References

Google Cloud BigQuery Documentation:
Amazon Redshift Documentation
Real-time use cases and pricing retrieved from GCP and AWS dashboards for the year 2023.