BigQuery vs. Redshift: Comparing Two Leading Data Warehouse Solutions

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June 9, 2025

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20 Mins

As businesses continue to scale their data infrastructures, the need for robust and flexible cloud data warehouse solutions has never been more pressing. In the landscape of cloud computing, Google BigQuery and Amazon Redshift remain top contenders.

However, with increasing focus on data sovereignty, data integration, and seamless data processing systems, choosing the right solution requires careful consideration of performance, cost, and security—especially when managing vast volumes of data for business intelligence.

This guide will compare BigQuery vs Redshift, including recent developments in both platforms and how Airbyte facilitates this decision by enhancing data workflows.

Google BigQuery Overview

Google BigQuery is a fully managed cloud data warehouse that decouples storage and compute resources. As part of Google Cloud Platform, it provides a serverless architecture, which means that businesses don’t have to worry about provisioning and managing compute resources. This makes it an ideal option for those looking to efficiently ingest data and scale without added complexity.

Key Features of BigQuery

Federated Queries: BigQuery supports querying data stored across multiple Google Cloud services like Google Cloud Storage and Bigtable, allowing teams to analyze data without moving it into the warehouse first.
BigQuery BI Engine: This in-memory analysis engine allows teams to perform business intelligence queries rapidly, even on massive datasets.
On-Demand Pricing: BigQuery offers on-demand pricing, where businesses pay for data processed, or they can opt for flat-rate pricing for better cost management.
Native Streaming Data Ingestion: BigQuery excels in streaming data ingestion capabilities, processing millions of rows per second with minimal latency, making it ideal for real-time analytics and big data analytics.
Security: BigQuery integrates with Google Cloud IAM for identity and access management, ensuring robust data security with encryption at rest and in transit.

Amazon Redshift Overview

Amazon Redshift, part of the AWS ecosystem, is a petabyte-scale data warehouse that uses massively parallel processing (MPP) to process large datasets. Redshift continues to evolve, with new features such as RA3 nodes for decoupled storage and Redshift Serverless, which enhances its flexibility for handling various data workloads.

Redshift is a data warehousing service offered by Amazon Web Services, designed to handle large-scale data analytics workloads. It utilizes a massively parallel processing (MPP) architecture to deliver high-performance querying and analysis of datasets ranging from gigabytes to petabytes. Redshift offers seamless integration with other AWS services, enabling you to ingest data from various sources, including Amazon S3 and DynamoDB, to perform complex analytics.

Key Features of Redshift

Automated Snapshots & Backups: Redshift’s automated backup system provides point-in-time recovery and integrates with AWS services like Amazon S3 for data storage and recovery.
Managed Storage: With Redshift managed storage, Redshift offers optimized storage capacity, automatically shifting cold data to low-cost storage tiers as data volumes grow.
Fine-Grained Access Control: Using AWS IAM and Lake Formation, Redshift ensures secure and scalable access management for large data operations.
Deep Integration with AWS: Redshift is built for enterprises already using other AWS services, such as Kinesis for streaming data ingestion and Amazon SageMaker for machine learning.

💡Related read: Redshift Concurrency Scaling

Architecture & Provisioning

When selecting a cloud data warehouse, understanding how each platform handles architecture and provisioning is crucial for optimizing data workflows. Redshift vs BigQuery offers distinct approaches, with BigQuery’s serverless model providing automatic scaling, while Redshift gives users control over compute resources for specific workloads, making it better suited for businesses with specific performance needs and large-scale data operations.

Aspect	Google BigQuery	Amazon Redshift
Core Design	Serverless, decoupled storage/compute	Cluster-centric (provisioned nodes)
Provisioning	Zero—slots auto-assigned	Choose node type/count or Redshift Serverless RPUs
Scaling	Automatic, real-time	Manual resize or concurrency scaling
Maintenance	No vacuuming or indexing	Periodic management tasks (vacuum, analyze)
Storage Model	Columnar storage on Colossus distributed file system	Columnar storage on SSDs + Redshift managed storage

BigQuery’s serverless architecture eliminates the need for cluster management, while Redshift gives more control over compute resources and query performance—ideal for niche business workloads that require optimization.

BigQuery vs Redshift: Head-to-Head Comparison

In a Redshift vs BigQuery comparison, evaluating the key features helps determine the best fit for an organization's data needs. BigQuery is designed for real-time analytics and scaling, whereas Redshift excels in providing fine-grained control for data analysts and data scientists working with complex query processing and massive datasets.

Feature	BigQuery	Redshift
User-Defined Functions	SQL & JavaScript	SQL SELECT clause or Python
Encryption	Always-on, customizable keys	Enabled via AWS KMS
Table Partitioning	Native support	Limited (via Spectrum or date truncation)
Streaming Data Ingestion	Built-in API (≈100 MB/s per table)	Amazon Kinesis Firehose required
Managed Storage Limits	Virtually unlimited	8 PB with RA3 managed storage
Max Columns per Table	10,000	1,600
Identity & Access Management	Google Cloud IAM	AWS IAM / Lake Formation
Query Performance Tuning	Mostly automatic (slot scheduler, BI Engine)	Manual (sort/dist keys, workload management)
Pricing Model	On-demand, slot-based	On-demand, Reserved Instances, or Serverless RPUs
Total Maintenance Effort	Very low	Moderate to high
Ecosystem Fit	Google Cloud services	AWS services

Performance & Scalability

Performance and scalability are crucial when evaluating Redshift vs BigQuery, especially when handling massive datasets and fluctuating data volumes. BigQuery is optimized for automatic scaling with streaming data ingestion, while Redshift offers more manual control over compute resources, ideal for organizations with specific, steady data processing needs and heavy reliance on structured data.

BigQuery Performance Highlights

Distributed Query Engine: BigQuery’s data processing systems use a distributed query engine to handle massive datasets and query data across multiple nodes with minimal latency, making it ideal for big data analytics.
Automatic Scaling: BigQuery can scale to handle unexpected spikes in query patterns, such as ad hoc queries or streaming data.
BigQuery BI Engine: This engine improves query performance, providing rapid responses even for complex SQL queries that deal with massive datasets.

Redshift Performance Highlights

MPP Architecture: Redshift’s massively parallel processing architecture excels in handling data stored in a distributed environment, using massively parallel processing to perform complex queries efficiently.
RA3 Nodes: Redshift’s separation of storage from compute resources leads to better query performance with reduced data movement and more efficient data storage.
Concurrency Scaling: Redshift automatically adds compute resources during demand spikes, making it a great choice for niche business workloads that require flexibility and consistent performance.

Redshift vs BigQuery: Choosing Based on Data Types and Processing Requirements

When evaluating Redshift vs BigQuery, one key factor to consider is how each platform handles different types of data and the specific needs of your data operations. Depending on your data types, business processes, and workloads, one platform may serve your needs better than the other.

Data Types and Query Flexibility

BigQuery excels in processing semi-structured data, such as nested data classes, making it a preferred choice for teams that need to handle complex, unstructured data. Standard SQL data types are supported in BigQuery, allowing seamless integration with various data sources and simplifying data operations.

With Google Cloud services backing it, BigQuery offers strong support for massive datasets and can scale dynamically to meet the growing needs of enterprises, particularly when you need to load data quickly and efficiently.

On the other hand, Redshift is built to handle more structured OLTP databases (Online Transaction Processing databases), making it ideal for traditional relational databases where you need to manage structured and highly transactional data. Data scientists often choose Redshift for its ability to provide deep query processing capabilities and fine-tuned performance for massive datasets that require detailed, predictable query patterns.

Handling Different Volumes of Data

If your organization needs to process large volumes of data, especially with frequent and complex queries, BigQuery’s serverless architecture is advantageous as it can run queries across how much data you need to process, scaling automatically to meet demand. This makes it ideal for businesses that require flexibility when working with varying data sizes or need to analyze massive datasets in real-time.

Redshift, while powerful, relies on provisioning and management of compute resources and may require more effort in terms of infrastructure management and data storage. However, for use cases where the data is more structured and transactional, Redshift's ability to handle and analyze data using OLTP databases offers advantages, particularly in organizations already invested in AWS services.

Data Integration for Analytics and Reporting

Both BigQuery and Redshift can integrate seamlessly with various data analysis tools, but BigQuery shines when dealing with semi-structured data sources, and is often used by businesses looking to integrate with Google Cloud services to quickly scale their analytics infrastructure.

If your team works with data scientists who require a flexible environment for experimentation and the ingestion of both structured and unstructured data, BigQuery provides an easy-to-manage solution that integrates well with other Google Cloud services like Google Cloud Storage and Dataflow.

In contrast, Redshift offers better support for business-critical OLTP database operations and is a top choice for those who require high performance on structured transactional data that demands fine-grained query optimization.

Manageability & Security

The ease of managing your cloud data warehouse solution, along with robust data security, can significantly impact your organization's data operations. BigQuery offers a serverless model that reduces the need for manual management, while Redshift provides more control over cluster management, making it suitable for businesses requiring high levels of customization and granular security features.

BigQuery

Serverless: BigQuery requires no management of clusters or compute resources, reducing the operational load on teams while maintaining high performance.
Security: BigQuery is encrypted by default, integrating with Google Cloud IAM for identity and access management. Columnar storage also enhances security by providing access at the column level.
Compliance: BigQuery meets various compliance standards, including HIPAA, SOC, and ISO/IEC 27001, making it an excellent choice for industries with strict data governance needs.

Redshift

Cluster Management: Redshift offers more control for DBAs, allowing manual cluster management and performance tuning to meet the needs of specific workloads.
Security Features: AWS IAM integrates seamlessly with Redshift for access management, offering fine-grained control over who can access data and how it is stored.
Integration with AWS: Redshift integrates with AWS services like Amazon S3 for data storage, Kinesis for streaming data ingestion, and SageMaker for machine learning, making it ideal for enterprises already invested in the AWS ecosystem.

Ecosystem & Integration

BigQuery integrates seamlessly with Google Cloud services, such as Google Cloud Storage, Dataflow, Pub/Sub for streaming data, and Vertex AI for machine learning.
Redshift fits well within the AWS ecosystem, offering integrations with AWS services like Glue for metadata management, SageMaker for ML, and Kinesis for real-time data ingestion.
Both platforms support JDBC/ODBC and integrate with major business intelligence tools like Looker and Data Studio, ensuring that your existing data processing systems will work seamlessly.

Pricing & Total Cost of Ownership

When evaluating the total cost of ownership for Redshift vs BigQuery, it’s essential to consider both the direct costs of data processing and storage as well as the operational expenses tied to infrastructure management. While BigQuery offers a flexible on-demand pricing model, Redshift provides more predictable pricing options, with discounts for long-term commitments through reserved instances and Redshift Serverless.

BigQuery

On-Demand Pricing: BigQuery offers on-demand pricing for the data processed, starting at $5 per TB, making it ideal for data storage and cloud-based data warehouse needs.
Flat-Rate Slots: For predictable workloads, BigQuery allows customers to purchase slots, offering cost control and scalability.
Storage Costs: BigQuery’s cloud storage is cost-efficient, offering reduced pricing for data that hasn’t been updated in over 90 days.

Redshift

On-Demand Pricing: Redshift charges hourly for compute resources, which can lead to higher costs for unpredictable workloads.
Reserved Instances: Redshift offers discounts of up to 75% for predictable workloads with long-term commitments.
Redshift Serverless: Offers pay-per-second billing, which is great for fluctuating workloads.
Managed Storage: Redshift’s managed storage is priced at $0.024/GB per month, with Redshift managed storage scaling automatically as your data volumes grow.

Cost Comparison

BigQuery typically offers a lower total cost of ownership for teams with variable workloads and minimal infrastructure management. On the other hand, Redshift may be more cost-effective for predictable workloads that can benefit from reserved pricing.

When to Choose Each Cloud Data Warehouse

Consider BigQuery if…	Consider Redshift if…
You need a serverless solution with automatic scaling and low maintenance.	You need control over compute nodes and the ability to fine-tune query performance for specialized workloads.
Your workloads are spiky or ad hoc, and you prefer a pay-per-query pricing model.	Your workloads are steady and predictable, and you are willing to commit to reserved instances to save costs.
You rely on Google Cloud services or you use a cloud data warehouse for real-time analytics and big data analytics.	You are already using AWS services and want tight integration with other AWS services for data management.
You prefer a low-touch platform with minimal operational overhead and fast data processing.	You have dedicated DBAs and require manual control over cluster management and data processing systems.

Confidently Choose The Cloud Data Warehouse That Aligns With Your Business

As businesses move to modernize their data infrastructure in 2025, choosing between Google BigQuery and Amazon Redshift comes down to the specific needs of your organization. BigQuery’s serverless architecture, combined with its ability to process large amounts of data without manual intervention, makes it an excellent choice for teams looking for ease of use and cost flexibility.

Meanwhile, Redshift offers granular control and integration with AWS, making it the go-to solution for organizations already embedded in the AWS ecosystem and those that require fine-tuned data analysis and performance.

When choosing the right cloud data warehouse for your team, consider your cloud platform preferences, data processing requirements, and the type of workload you plan to run. With the comparison in hand, you’ll be equipped to select the platform that best suits your data management and business intelligence needs.

Frequently Asked Questions (FAQ)

Can I use both BigQuery and Redshift together in a hybrid data warehouse setup?

Yes, many organizations use both platforms in a hybrid setup, with BigQuery handling everyday business processes and real-time analytics for unstructured data, while Redshift manages niche business workloads and transactional data. Integration tools like Airbyte can help synchronize the data, enabling businesses to rapidly analyze terabytes of information and streamline data workflows.

How do BigQuery and Redshift compare in terms of data governance and compliance?

BigQuery is compliant with regulations such as HIPAA and SOC 2, making it ideal for businesses that need to manage everyday business processes and analyze large datasets. Redshift also offers strong compliance features, particularly within the AWS ecosystem, and is suitable for businesses requiring tight control over data governance when handling transactional data.

What are the cost implications when scaling BigQuery or Redshift for large datasets?

BigQuery’s on-demand pricing makes it efficient for fluctuating workloads, ideal for businesses that need to rapidly analyze terabytes of data and handle streaming inserts. In contrast, Redshift’s reserved pricing model can be more cost-effective for businesses that deal with steady data operations, especially when processing transactions and handling niche business workloads.

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