5 Best Cloud Data Warehouses (Based on G2 Ratings)

In today’s cloud-rich landscape, businesses are turning to data warehouses to store, manage, and analyze their data. As of 2024, over 65k companies use cloud data warehouses to enhance their data management and analytics capabilities.

What used to be a database-oriented world is now moving to warehouses and data lakes due to their extensive features, such as scalability, cost-effectiveness, and enhanced performance. More and more people are moving from on-premise to cloud platforms to increase productivity and reduce the cost of on-premise resources.

By the end of this blog, you’ll have a clear understanding of what data warehouses are and discover the best cloud data warehouses for your business needs.

What is a Cloud Data Warehouse?

A cloud data warehouse is a central storage system designed to store large amounts of data from various sources hosted on the cloud without the need for physical infrastructure. It is highly scalable and cost-effective, making handling complex data queries and large datasets easier.

Cloud data warehouses store current and historical data. They allow organizations to perform complex queries and analyses to help them make data-driven decisions.

Advantages of Using Cloud Data Warehouse

• Cloud data warehouses are hosted on the cloud and can be accessed anywhere, anytime.
• Most cloud warehouses provide flexible and pay-as-you-go pricing models.

Why Shift from On-Premise to Cloud Data Warehousing?

On-premise warehouses require high investment and infrastructure, making scalability difficult and time-consuming. On the other hand, cloud warehouses are hosted on the cloud and require no upfront investment. They provide scalability and flexibility, as well as regular updates and maintenance.

Cloud data warehouses allow users to access their data anywhere with an internet connection. Also, most cloud data warehouses offer pay-as-you-go pricing, meaning you only pay for the resources you use. 

5 Best Cloud Data Warehouses

1. Google BigQuery

G2 Rating: 4.5 out of 5 (991)

Google’s BigQuery is a fully managed data warehouse service designed to manage all data types across clouds. Its serverless design lets you use SQL to analyze your data. BigQuery allows users to run queries on petabytes of data in minutes.

Key features 

• It offers automated data loading and transformation with DTS (Data Transfer Service).
• BigQuery is a column-based tool that is suitable for aggregations across datasets.
• Fully scalable and reliable, it helps users quickly analyze and easily predict trends.
• Its BI engine is an in-memory analysis tool that speeds up SQL queries from various data sources and allows you to incorporate machine learning functionalities into your analysis.

Pricing

BigQuery offers two types of pricing components: Compute pricing and Storage pricing. The first is the cost of processing queries and scripts, and the latter is the cost of storing data that is loaded into BigQuery. That further is divided into two compute pricing models: one is on-demand, and the other is capacity pricing. For the on-demand model, you are charged $6.25 per TiB for running queries, and the capacity model has a pay-as-you-go pricing model and is available in 3 editions: Standard, Enterprise, and Enterprise Plus. Learn more about BigQuery Pricing.

Ideal Use case

It is the perfect choice for storing significant volumes of data with occasional queries due to its cost-effective pricing.

2. Amazon Redshift

G2 Rating: 4.3 out of 5 (392)

Amazon Redshift is AWS’s answer to data warehousing. It is a fully managed cloud data warehouse service that can scale up to petabytes of data. It offers a variety of SQL-based tools and BI applications with integration capability for a wide range of third-party applications. 

Key features 

• It allows data to be organized into either rows or columns. Row-oriented systems can swiftly process a large number of small operations, whereas column-oriented systems can quickly access large amounts of data.
• Redshift can query all structured, unstructured, and semi-structured data.
• One can also create and train ML models with SQL on the data in Amazon Redshift.
• Integration with other AWS services makes it an ideal choice for organizations already using AWS.

Pricing

Amazon Redshift offers on-demand pricing from $0.25 to $13.4/ hour. It also provides Reserved instance pricing, which can help you save 75% over the on-demand(3-year term).

Amazon Redshift also offers a trial period. Users can sign up on its pricing page. 

Ideal Use case

Amazon Redshift is best for storing large datasets and big data analytics. It is also ideal for organizations that are already investing in other AWS services.

3. Snowflake

G2 Rating: 4.5 out of 5 (544)

Like other tools on the list, Snowflake is a fully managed cloud data warehouse. It is easy to use and has advanced transformation capabilities, allowing businesses to store and analyze high volumes of data. Snowflake also offers automatic scaling and parallel processing, empowering businesses with scalable, real-time data analysis.

Key features

• It requires minimal effort to set up and start running.
• Storage and computing can be separated and can be priced accordingly.
• Snowflake offers useful querying features like undrop, fast clone, etc.
• It also offers a time travel feature, which allows you to track data changes in tables and schemas and restore objects of any version within a given period.

Pricing

Snowflake offers four pricing options to meet different business needs:

• Standard: This is the introductory edition priced at $2.00/per credit
• Enterprise: This is for companies with extensive data and more enterprise control, priced at $3.00/per credit.
• Business Critical: This offering is for industries dealing with sensitive data, priced at $4.00/per credit.
• Virtual Private Snowflake: This customizable offering includes all the features of a business-critical solution but in a private environment.

Ideal Use case

Snowflake is ideal for businesses that need flexibility to run across multiple cloud platforms.

4. Azure Synapse Analytics

G2 Rating: 4.4 out of 5 (35)

Azure Synapse Analytics is an analytics service designed for enterprises that combines the best of SQL technologies. It offers a choice between serverless on-demand and provisioned resources for querying data. It is a cloud-native processing engine built on the foundation of SQL server.

Key features

• Scales storage and computing separately.
• It offers built-in advanced security features like connection security, authentication, and encryption.
• It also guarantees 99.99% availability and is highly scalable.
• Synapse Analytics can be integrated with Azure Machine Learning to build ML models within the data warehouse.

Pricing

It offers highly customizable pricing with pre-purchase plans. There are six tiers to choose from, beginning from $4,700 and going up to $2,59,200 per year.

Ideal Use case

Azure Synapse analytics is ideal for Enterprise data warehousing, where companies can integrate data from many data sources across the company.

5. IBM Db2 Warehouse

G2 Rating: 4.1 out of 5 (649)

IBM Db2 Warehouse is a cloud-based data warehouse built on IBM’s Db2 technology. It is designed for complex queries and high-level analysis and is widely used due to its strong integration with IBM’s ecosystem and built-in data science tools.Db2 Warehouse supports both structured and unstructured data.

Key features

• It includes an in-memory columnar database, which provides high-performance capabilities.
• IBM Db2 offers various cloud deployment options, such as in IBM Cloud, AWS, or an on-premises version of Db2 Warehouse.
• This data warehouse supports various data types and formats, including JSON, XML, etc.

Pricing

IBM Db2 Pricing is based on deployment options, including cloud-based and on-premises solutions. It offers a free trial for IBM DB2 Warehouse and flexible pricing models based on usage. The pricing page has more details. 

Ideal Use case

IBM Db2 is ideal for Businesses handling big data analytics that can benefit from its in-memory database engine and cloud deployment options.

Factors to Consider Before Choosing a Cloud Data Warehouse

1. Integration Capabilities: The first factor to consider is the data warehouse you choose must integrate seamlessly with your existing tools and data sources.

2. Pricing Model: Analyzing the pricing models and determining the total cost of ownership is essential to ensure you are paying the right amount.

3. Security and Compliance: One of the most critical factors to consider is security and compliance with industry standards to protect your data.

4. Ease of Use: Consider the user interface and overall ease of use for technical and non-technical users. 

5. Scalability: It is crucial to evaluate how well the warehouse handles large volumes of data and whether it can scale to meet your organization’s growing needs.

Learn More About:

Optimizing Data Warehouse Cost

Comparison Table of the Best Cloud Data Warehouses

Criteria	Google BigQuery	Amazon Redshift	Snowflake	Azure Synapse Analytics	IBM Db2 Warehouse
Scalability	Highly Scalable	Highly Scalable	Highly Scalable	Highly Scalable	Scalable
Cost	Pay-as-you-go pricing	On-demand, reserved instance pricing	On-demand and pre-purchase pricing	On-demand and reserved instance pricing	Flexible pricing
Security	Strong encryptions and Google authentication	High-level security with major certifications like HIPAA and SOC 2.	Role-based access, end-to-end encryption	Built-in security	Enterprise-grade security
Ease of use	User-friendly, easy setup	Technical know-how required	Minimal drag-and-drop tools	Steeper learning curve	Easy for IBM users
Support	Extensive docs, community	Large user community and high support	Community as well as enterprise support	Varies support plans	Strong support from IBM
flexibility	Scales easily with Google Cloud	AWS centric	Multi-cloud	Flexible resource management	Elastic scaling

Conclusion

Selecting the right cloud data warehouse is essential for optimizing your organization’s data management and analytics. This blog highlights significant factors that a person looking for the best cloud data warehouses should consider: Google BigQuery, Amazon Redshift, Snowflake, Azure Synapse Analytics, or IBM Db2 warehouse—each offers unique strengths suited to different business needs. Optimize storage costs and query efficiency with Apache Iceberg. Discover details at Data Warehouse Cost Savings.

For a detailed, step-by-step approach to building a data warehouse, check out our guide on Building a Data Warehouse for Modern Businesses.

To further enhance your data integration and management capabilities, consider using a data integration tool like Hevo Data. It seamlessly connects with these cloud data warehouses to enable an efficient data pipeline. Hevo allows users to get maximum output with minimum clicks. Sign up for Hevo’s 14-day free trial and experience seamless data migration.

FAQs for Cloud Data Warehouse