BigQuery vs. Redshift: Which One Should You Choose?

Considering BigQuery vs. Redshift for your data warehousing needs? This guide is for you. Both BigQuery and Redshift stand as leading cloud data warehouse solutions each offering a multitude of features catering to multiple use cases. Google’s BigQuery offers seamless scalability and performance within its cloud platform, while Amazon’s Redshift provides great parallel processing and tuning options. 

Let’s simplify the decision-making process by breaking down the differences between Redshift vs. BigQuery, helping you find the perfect fit for your business. 

What is Google BigQuery? 

Google BigQuery is a part of Google’s overall cloud architecture, the Google Cloud Platform (GCP). It operates as a serverless and fully managed service, eliminating the need for managing infrastructure and allowing businesses to prioritize data analysis and insight generation.

Google BigQuery is built on top of Dremel technology, which is a query service that enables users to run SQL-like queries. At its core, Dremel uses a distributed execution model that distributes the workload across multiple nodes within Google’s infrastructure.

BigQuery is among the initial major data warehouses, featuring exceptional query performance. Using Google’s infrastructure and technologies, such as Capacitor, Juniper, Borg, and Colossus, BigQuery can execute complex analytical queries against massive datasets within seconds. 

What is Amazon Redshift? 

 

Amazon Redshift is the first-ever cloud data warehouse that offers a fully managed, petabyte-scale service. Redshift is designed to manage large datasets and complex analytical queries with high performance.   

Amazon acquired the primary source code for Redshift from ParAccel, which is a company that was developing the ParAccel Analytic Database (a PostgreSQL-based database).

Redshift is built on the PostgreSQL fork but has a lot of unique capabilities. For instance, Redshift has a unique column structure and makes use of distribution styles and keys for data organization.

Since Redshift is designed to handle large amounts of data, companies can scale the data warehouse up or down based on their requirements to easily accommodate data volumes. Moreover, there are no charges incurred when the warehouse is idle, which means you pay only for what you use.

BigQuery vs. Redshift: Architecture 

When comparing Google BigQuery to Amazon Redshift in terms of architecture, there are some key differences to consider. 

Firstly, BigQuery operates on a serverless architecture, while Redshift offers greater overall control. In BigQuery, Google manages all the aspects of the warehouse, including provisioning, scaling, and maintenance. But it abstracts the users from the BigQuery infrastructure. With this approach, users can focus on processing massive datasets without having to worry about infrastructure management. The resources are allocated automatically depending on the number of queries you execute.  

 On the other hand, Amazon Redshift follows a more traditional architecture that is based on a cluster of nodes. This architecture includes a leader node that takes care of client connection and query execution, while multiple compute nodes store and process data. Redshift uses a massive parallel processing (MPP) architecture to parallelize and distribute the queries across compute nodes. Redshift generally allows you to have better control over your resources so you can manage tasks, including scaling, patching, and backup. 

BigQuery vs. Redshift: Scalability 

Scalability is mainly limited by three major factors that is lack of dedicated resources, continuous ingestion, and tightly coupled storage and compute resources.  

BigQuery has a serverless architecture and it automates resource provision and scaling. Therefore, scaling is well-planned and well-structured in the case of BigQuery. It generally works on either on-demand pricing or flat-rate pricing.  In the on-demand pricing model, assigning slots (compute resources) is fully controlled by BigQuery, whereas the flat-rate pricing model reserves slots in advance. The auto-scaling capability generally suits companies with fluctuating data volumes or unpredictable workloads. 

In contrast, Amazon Redshift cannot distribute the load across clusters even with RA3. This limits its scalability. To support the query concurrency, it can scale up to 10 clusters easily; however, Redshift can handle only 50 queued queries across all clusters. Though Redshift is scalable, its manual cluster management approach requires monitoring and configuration adjustments that would potentially introduce complexity. 

In a test run by an independent researcher, it was found that BigQuery was significantly faster than Redshift when dealing with a large dataset, which may suggest better scalability for BigQuery. However, note that several of these tests have been performed, and deciding on a clear winner is easier said than done. 

BigQuery vs. Redshift: Performance 

Comparing the performance of Redshift and BigQuery involves considering factors like concurrency, optimization techniques, query speed, and data processing capabilities. Considering that both BigQuery and Redshift are run by tech giants the differences in their performance are negligible. 

 The columnar storage format and distributed execution model of BigQuery enable parallel processing of queries across multiple servers that results in rapid data retrieval and analysis. Moreover, its automatic query optimization features, including execution plans and dynamic query reordering, enhance query performance and efficiency. This minimizes latency and maximizes throughput. That said, BigQuery is a great solution for real-time analytics and interactive querying cases where speed and responsiveness are of considerable importance. 

BigQuery also has a built-in caching mechanism that automatically caches the results of every query for 24 hours, which can significantly speed up repeated queries. However, for small, ad-hoc queries, BigQuery may be slower than Redshift due to its reliance on distributed computing. 

On the other hand, the Amazon Redshift is manufactured on a massively parallel processing (MPP) architecture that allows it to perform well for data warehousing and analytical workloads. Redshift has more tuning options than many others, but you cannot expect it to deliver much faster compute performance than other cloud data warehouses.  

Redshift also offers workload management features, including query queues and concurrency scaling, to prioritize and manage query execution based on user-defined criteria. However, its manual cluster management approach may introduce overhead in terms of cluster configuration and maintenance, impacting its overall performance.  

Redshift vs. BigQuery: Which One to Choose? 

When choosing between the two, companies should assess their preferences and requirements before picking any of these data warehouses. Here are a few use cases to help you decide.  

When to Use Google BigQuery 

• Large-Scale Data Analytics: BigQuery’s serverless architecture and ability to handle petabytes of data make it an ideal choice for large-scale data analytics.  

• Data Exploration: BigQuery is designed for ad-hoc analysis and data exploration. It allows users to perform SQL-like queries on big datasets.   

• Real-Time Analytics: BigQuery supports real-time analytics through its streaming API, making it perfect for analyzing live data.  

• Integration with Google Ecosystem: If your organization already uses Google Cloud Platform services, using BigQuery can provide seamless integration.  

When to Use Amazon Redshift: 

• Complex Query Execution: Redshift maintains a strong performance when executing complex, compute-heavy queries. Its column-based storage and MPP architecture are designed for this purpose.  

• Data Warehousing Operations: Redshift is ideal for traditional data warehouse operations, where the primary requirement is storing structured and semi-structured data.  

• Predictable Pricing: If predictable pricing is a priority, Redshift may be a better choice as its pricing is per node, which can often be more predictable and affordable. 

• Integration with AWS Ecosystem: If your organization is already invested in the AWS ecosystem, using Redshift can simplify data warehousing operations.  

The Path Forward: Future-Proof Data Warehousing 

For future-proof data warehousing, it’s significant to select a solution that can adapt to evolving data demands and analysis technologies. Here is what you can expect from Redshift and BigQuery in the future. 

BigQuery’s Petabyte Scale: BigQuery has the capability to manage large datasets without any hassle. No matter if it requires dealing with customer transaction data or years of dealing with billions of sensor readings from IoT devices, BigQuery can handle all that efficiently by accommodating your data needs. This scalability is advantageous for enterprises that expect sustained growth in their data volumes over time. 

 Redshift’s Real-time Options: Despite its emphasis on batch processing, Redshift offers real-time analytics functionalities through its integration with Amazon Kinesis Firehouse. With this, near real-time data ingestion into Redshift becomes possible. This turns out beneficial in cases that require immediate insights, like stock price monitoring and fraud detection. While this feature addresses some real-time needs, those looking for instantaneous analytics at scale will find BigQuery a more suitable option due to its inherent design for better performance for low-latency queries and real-time processing. 

Choosing the Right Future-Proof Platform 

Choosing the ideal data warehousing solution for future-proofing your infrastructure depends upon the specific needs and priorities of your organization. Here’s a guide to help you pick the right one: 

1. Looking for AI/ML Integration? Choose BigQuery as it stands out for seamless integration with Google’s AI and machine learning tools like Vertex AI and TensorFlow. This native integration allows for easy analysis and enables the development of ML models directly within the data warehouse environment.  

1. Want to Focus More on Real-time Analytics? BigQuery emerges as a superior choice. It features serverless architecture and automatic scaling, achieving real-time insights with minimal latency. That’s difficult with Redshift as it may require additional configuration and management overhead to handle real-time data effectively.  

1. Have Significant Investments in AWS? Consider Redshift as it offers tight integration with other AWS services. By using Redshift, you can ensure seamless interoperability and maximize the benefits of existing AWS infrastructure. 

1. Looking for a Completely Serverless Architecture? BigQuery is the optimal choice. It runs on a fully serverless architecture that eliminates the need for any kind of server management. That said, scalability and resource allocation become easier. 

1. Considering the Integration of Unstructured Data? Go ahead with Redshift with Spectrum, as it has better capabilities for analyzing some forms of unstructured data. However, if the data primarily consists of unstructured formats like text and images, BigQuery will be a better option as it provides better built-in features for handling such unstructured data. 

1. Working with Massive Datasets? BigQuery will be your perfect companion as it excels at handling massive datasets. It can manage large data volumes and ensures optimal performance and scalability even with your growing data needs.  

Evaluating these considerations and aligning them with your business’s objectives and requirements will help you choose a future-proof data warehousing platform that positions you to continue to leverage the power of data for years to come. 

Astera Provides Native Connectivity to Redshift and BigQuery

This wraps up our “BigQuery vs. Redshift” comparison. Both platforms offer high-performance and scalable cloud data warehousing, each with its own set of features, pricing models, and usability. Being supported by tech giants like Amazon and Google, either option is a solid choice.  

However, selecting the one that fits your data warehousing needs is essential. 

Astera provides native support for both BigQuery and Redshift. Whether you’re migrating an existing data warehouse or creating a new one, our no-code platform, Astera DW Builder, enables you to design, develop, and deploy enterprise-grade data warehouses quickly and efficiently.

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