Data Engineering Tools Comparison: Snowflake vs Redshift vs BigQuery
Choosing the right data warehousing platform is one of data engineers’ most critical decisions. It’s not just about where data lives; it’s about how efficiently and securely it can be accessed, analyzed, and scaled as your business grows. In today’s landscape, Snowflake, Amazon Redshift, and Google BigQuery are the leading choices for data engineers looking to turn vast datasets into valuable insights. But which one is the best fit for your unique needs?
At Data Engineer Academy, we understand the daily challenges data engineers face. Our mission is to equip you with knowledge that helps you make informed, impactful decisions for your organization. In this article, we’ll dive deep into the strengths and weaknesses of these platforms, focusing on what matters: performance, scalability, pricing, and ease of use. Our goal is to give you a straightforward, data-focused comparison, so you can confidently choose the solution that best aligns with your data strategy.
Whether you’re looking to optimize for cost, leverage machine learning, or simply streamline your analytics, understanding the nuances of each platform is essential. Let’s explore the capabilities of Snowflake, Amazon Redshift, and Google BigQuery, so you can decide which one is right for your team and your projects.
Overview of Snowflake, Amazon Redshift, and Google BigQuery
To understand which data warehousing platform best suits your needs, let’s start with a detailed overview of each: Snowflake, Amazon Redshift, and Google BigQuery. Each platform offers unique features, and understanding their strengths and design philosophies will help clarify which aligns with your organization’s data engineering goals.
What is Snowflake?
Snowflake has swiftly emerged as a leading data warehousing solution, celebrated for its adaptability, seamless scalability, and cutting-edge cloud-native design. Established in 2012, Snowflake was purposefully built to harness the advantages of cloud technology, setting it apart from traditional on-premise systems and legacy solutions.
- Decoupled storage and compute
Snowflake’s innovative architecture allows for separating storage and compute resources, enabling users to scale each component independently. This means you only incur costs for the resources you utilize, and you can enhance computing capabilities as needed without impacting your stored data. - Multi-cloud functionality
Snowflake operates on AWS, Microsoft Azure, and Google Cloud Platform, making it an excellent choice for organizations that utilize multiple cloud environments or seek flexibility in their cloud service providers. This capability helps businesses avoid vendor lock-in and tailor their cloud strategies according to workload demands and budget considerations. - Rapid scaling and concurrency management
Snowflake employs “Virtual Warehouses” for data processing. These autonomous compute clusters allow numerous users to access data simultaneously without hindering each other’s performance. This design makes Snowflake particularly advantageous for large teams or organizations with intricate, high-traffic data operations. - Effortless handling of semi-structured data
Snowflake excels at managing semi-structured data formats such as JSON, Avro, and XML. Users can query this type of data without needing prior transformation, which is invaluable for organizations dealing with unstructured data sources, including IoT and real-time applications. - Security
Snowflake incorporates comprehensive security measures, including end-to-end encryption, data masking, and role-based access control. The platform adheres to various regulatory standards such as GDPR, HIPAA, and SOC 2, making it a reliable option for organizations with rigorous security and compliance needs.
What is Amazon Redshift?
Amazon Redshift is a powerful, fully managed data warehouse solution offered as part of the extensive Amazon Web Services (AWS) ecosystem. Launched in 2013, Redshift is designed to provide high-performance data warehousing on a petabyte scale, making it a strong choice for organizations already embedded within the AWS environment.
- Cluster-based architecture with node scaling
Redshift’s architecture is based on clusters, each containing a leader node and multiple compute nodes. This allows users to scale up by adding nodes, expanding both storage capacity and compute power. Redshift’s architecture is well-suited to traditional data engineering workflows, where queries are highly structured and predictable.
- Integration with the AWS ecosystem
As a part of AWS, Redshift seamlessly integrates with other AWS services like S3, Kinesis, and Lambda. This tight integration is advantageous for teams using AWS extensively, as it allows for streamlined workflows, particularly in data ingestion, ETL (Extract, Transform, Load) processes, and application integration. - Massively parallel processing
Redshift employs a massively parallel processing (MPP) system, which enables it to distribute data across multiple nodes, optimizing query performance. This design makes Redshift a compelling choice for data engineers who need to process large, complex queries quickly, especially within enterprise settings. - Columnar storage and data compression
By storing data in columns instead of rows, Redshift optimizes storage and enables faster retrieval of large datasets. Data is also compressed during storage, which reduces disk I/O and improves query speed. - Compliance
Redshift supports advanced security features, including network isolation, encryption at rest and in transit, and integration with AWS Identity and Access Management (IAM). Redshift’s compliance with standards like GDPR, HIPAA, and SOC 1, 2, and 3 make it a strong choice for enterprise-grade applications.
Amazon Redshift’s combination of high performance, seamless AWS integration, and scalable infrastructure make it ideal for organizations heavily invested in AWS and seeking an efficient, enterprise-level data warehousing solution.
What is Google BigQuery?
Google BigQuery is a fully managed, serverless data warehouse developed as part of the Google Cloud Platform (GCP). Launched in 2010, BigQuery has earned a reputation as a high-performance platform designed for real-time, ad-hoc analytics on massive datasets. Its unique serverless model means users don’t need to manage infrastructure, which is a significant advantage for teams focused on fast, scalable analytics without operational overhead.
- Serverless architecture and automatic scaling
BigQuery’s serverless model is distinct from Snowflake’s and Redshift’s approaches. With BigQuery, there’s no need to manage or optimize infrastructure. This allows users to focus on querying and analyzing data, as BigQuery automatically scales resources based on query demands. - Pay-as-you-go pricing model
BigQuery’s pricing is based on the volume of data processed per query, which can be cost-effective for organizations with unpredictable or intermittent query workloads. For organizations needing cost transparency and efficiency, this pay-per-query model is highly advantageous. - BigQuery ML for Machine Learning
BigQuery offers integrated machine-learning capabilities, allowing data teams to build and deploy machine-learning models directly within BigQuery using SQL syntax. This feature, known as BigQuery ML, is a game-changer for teams needing machine learning capabilities on large datasets without moving data to another platform. - Support for real-time analytics and streaming data
BigQuery’s unique data ingestion capabilities allow it to handle real-time analytics, making it well-suited for applications like IoT, monitoring, and live dashboards. This is particularly valuable for businesses that require real-time data processing and analytics.
Google BigQuery’s serverless model, real-time processing capabilities, and machine learning integration make it ideal for organizations that prioritize agility, analytics speed, and flexibility without needing to manage infrastructure.
Snowflake vs. Redshift vs. BigQuery: Key Comparison Criteria
Snowflake, Amazon Redshift, and Google BigQuery each offer unique advantages, influenced by their architectures, scalability features, and integration capabilities. By grasping the subtle differences in how these platforms manage essential elements—like query performance, handling multiple workloads, data ingestion processes, and cost management—data engineers can gain the insights necessary to create effective and sustainable data solutions. This analysis underscores the distinct ways each platform caters to various organizational requirements, laying the groundwork for making informed and significant decisions in your data engineering endeavors.
1. Data warehousing architecture comparison: Snowflake, Redshift, and BigQuery
Snowflake utilizes a unique architecture that separates compute and storage resources, known as a multi-cluster, shared-data architecture. This setup enables flexible scalability for both computing and storage, allowing users to independently adjust resources based on demand. Snowflake also operates on a multi-cloud platform, meaning it’s available on AWS, Azure, and GCP. This flexibility is ideal for organizations with diverse cloud strategies or requirements for cloud-agnostic solutions.
Amazon Redshift follows a more traditional cluster-based architecture. In Redshift, compute resources are organized into clusters that include one leader node and multiple compute nodes, which users can scale vertically or horizontally by adding or resizing nodes. While this architecture offers high performance for structured data and predictable workloads, it does require careful planning to optimize for scalability, as adding or removing nodes can affect performance. Redshift’s architecture is tightly integrated with AWS, which is ideal for teams fully embedded within the AWS ecosystem.
Google BigQuery, in contrast, takes a serverless and fully managed approach, meaning users don’t have to manage infrastructure at all. BigQuery automatically provisions compute resources as needed, scaling them based on query demand. Storage is separated from compute, much like Snowflake, but BigQuery’s serverless nature removes the need for users to manage clusters or nodes, making it incredibly easy to scale for large datasets without planning or configuring resources.
2. Performance comparison for Snowflake, Redshift, and BigQuery
Snowflake is known for its multi-cluster computing capability, which allows high performance for concurrent queries. Users can configure multiple “virtual warehouses” (compute clusters) to handle queries simultaneously without affecting one another, which is ideal for organizations with heavy concurrent data loads or multiple data teams. This feature makes Snowflake both highly performant and scalable for a wide range of workloads, from standard analytics to complex data science applications.
Amazon Redshift leverages massively parallel processing (MPP) for high performance. Redshift’s columnar storage and optimized compression techniques are effective at handling large datasets and complex queries. However, Redshift’s performance can be impacted by the need to manually scale clusters, especially for teams handling fluctuating workloads or sudden spikes in demand. While Redshift offers features like Redshift Spectrum for querying data directly in S3, scaling clusters effectively still requires configuration and planning.
Google BigQuery excels in scenarios requiring ad-hoc analytics and real-time processing due to its on-demand query execution. BigQuery’s architecture is optimized for low-latency, large-scale analytics and can handle high concurrency without configuration, thanks to its serverless and auto-scaling nature. BigQuery’s performance remains consistent even under heavy loads, making it suitable for applications such as real-time dashboards and large-scale data analysis.
3. Data integration and loading in Snowflake, Redshift, and BigQuery
Snowflake supports a wide range of data formats, including structured, semi-structured (e.g., JSON, Parquet, Avro), and unstructured data. It integrates easily with ETL tools like Fivetran, Informatica, and Matillion, making it ideal for organizations that need versatile data ingestion. Snowflake’s Snowpipe feature allows for continuous data loading, which can be beneficial for teams needing near-real-time ingestion capabilities.
Amazon Redshift supports structured and semi-structured data and integrates well with AWS-native tools like AWS Glue for ETL processes, as well as third-party ETL solutions. Redshift’s COPY command is efficient for bulk loading large datasets from S3, DynamoDB, or other external databases. For teams using Redshift Spectrum, it’s possible to query directly from S3 without moving data into Redshift, providing added flexibility for data integration.
Google BigQuery provides excellent support for real-time streaming data through BigQuery Data Transfer Service and integration with Google’s Pub/Sub. It supports a wide array of data formats, including JSON and Avro, and can load data directly from Google Cloud Storage, Amazon S3, or external APIs. BigQuery’s streaming ingestion capabilities make it especially valuable for businesses processing continuous data from IoT devices or real-time applications.
4. Pricing models: Snowflake vs. Redshift vs. BigQuery Cost Comparison
Snowflake uses a pay-as-you-go pricing model where storage and compute costs are billed separately. Users are charged based on the time compute clusters are active, meaning costs are proportional to usage. Snowflake’s separate pricing for storage and computing provides flexibility but requires monitoring of compute hours for cost control. Snowflake also offers credit-based pricing for predictable budgets.
Amazon Redshift offers both on-demand pricing and reserved instance pricing. With reserved instances, organizations can save significantly on costs by committing to one or three-year terms. Redshift’s pricing flexibility is beneficial for organizations that can predict and commit to their usage, though on-demand pricing is available for shorter-term needs. Redshift Spectrum adds additional costs per terabyte when querying directly from S3.
Google BigQuery follows a unique pay-per-query pricing model. Users are billed based on the amount of data processed by each query, which can be economical for organizations with intermittent data workloads. However, high-volume or poorly optimized queries can lead to unexpected costs, so budget-conscious teams need to monitor query volume closely. BigQuery also offers flat-rate pricing for users needing more predictable costs, making it versatile for various budget requirements.
5. Security and compliance standards for Snowflake, Redshift, and BigQuery
Snowflake emphasizes security with end-to-end encryption (both at rest and in transit), role-based access control, and data masking capabilities. Snowflake is compliant with multiple security standards, including GDPR, HIPAA, and SOC 2, making it suitable for organizations with strict regulatory requirements.
Amazon Redshift also offers robust security, with encryption capabilities for both data at rest and in transit and network isolation using Virtual Private Clouds (VPCs). Redshift integrates with AWS Identity and Access Management (IAM), providing secure access controls and compliance with standards like SOC 1, 2, and 3, HIPAA, and GDPR.
Google BigQuery integrates Google Cloud IAM for comprehensive access control and supports encryption at rest and in transit. BigQuery complies with ISO 27001, HIPAA, and GDPR standards, aligning with organizations requiring high levels of data security and regulatory compliance. BigQuery’s integration with Google’s security infrastructure offers seamless protection for businesses heavily invested in Google’s ecosystem.
Pros and Cons of Snowflake, Amazon Redshift, and Google BigQuery
Having explored the core features and capabilities of Snowflake, Amazon Redshift, and Google BigQuery, it’s clear that each platform is designed to address specific needs within data engineering. However, choosing the right solution goes beyond features alone. It requires a careful look at each platform’s pros and cons to understand how they align with your organization’s unique requirements, from scalability and ease of use to integration and cost management.
Below, we’ve summarized the most notable advantages and potential drawbacks of each platform. This table offers a concise view of where Snowflake, Redshift, and BigQuery excel and where they may fall short, helping you make an informed decision based on your organization’s goals, resources, and technical environment.
| Platform | Pros | Cons |
| Snowflake | – High costs for frequent/heavy queries – Dependency on the Google Cloud ecosystem – Limited infrastructure control for custom configurations | – Complex pricing model requires monitoring- Limited direct third-party integrations- Reliant on underlying cloud provider storage |
| Amazon Redshift | – Strong integration with AWS ecosystem – High performance for structured data – Flexible pricing (on-demand and reserved) – Redshift Spectrum enables S3 data querying | – Manual scaling and management needed- Limited support for semi-structured data- AWS-specific, with cross-cloud data transfer fees |
| Google BigQuery | – Serverless, no infrastructure management – Pay-per-query pricing – Real-time analytics and streaming support – Integrated ML capabilities with BigQuery ML – Seamless integration with GCP services | – High costs for frequent/heavy queries – Dependency on Google Cloud ecosystem – Limited infrastructure control for custom configurations |
This balanced look at the pros and cons of each platform can help you choose the best fit for your data needs, whether it’s flexibility, cost efficiency, or advanced analytics. Understanding these strengths and limitations will enable your data engineering team to select a solution that meets both your current needs and future growth.
Which Data Warehousing Solution is Best?
Choosing between Snowflake, Amazon Redshift, and Google BigQuery isn’t simply about selecting a platform with the most features — it’s about identifying the solution that best aligns with your organization’s specific data engineering needs, infrastructure, and strategic goals. Below, we break down scenarios where each platform shines, helping you decide which one will support your organization’s data journey most effectively.
Each platform has unique strengths that make it best suited to particular use cases:
- Choose Snowflake if you need multi-cloud flexibility, support for both structured and semi-structured data, and a highly concurrent, scalable solution with minimal management requirements.
- Choose Amazon Redshift if you are invested in the AWS ecosystem, require high performance for structured data, and value the cost savings and predictability of reserved pricing for long-term workloads.
- Choose Google BigQuery if real-time analytics, machine learning integration, and a fully managed serverless solution align with your needs, especially if your team operates within Google Cloud.
Selecting the right platform ultimately depends on your organization’s cloud strategy, workload characteristics, and team’s expertise. By understanding these considerations and aligning them with the capabilities of Snowflake, Amazon Redshift, and Google BigQuery, you can make a choice that best supports your data strategy today and into the future.
If you’re interested in diving deeper into data engineering practices and mastering these platforms, Data Engineer Academy offers courses and resources tailored to help you build expert-level skills. Explore the full potential of data engineering with professional guidance from DE Academy!
