6 Best Cloud Data Warehouse Solutions for 2023 and How to Choose the Right One

By Jack Zagorski | 11 min read

It may seem overly simplistic to say, but any business that takes its data seriously should be investing in a data warehouse. The reasons for this are three. 

Why You Need a Data Warehouse

First, it’s the only practical way to have access to all your data from as far back in time as you need. Business intelligence (BI) tools, or the tools that we use to display data for actionable insights, do not store data. While many of the cloud-based services we use for things like marketing, customer support, and accounting do store data, their storage space is always limited, and so is the time for which it is stored. Take Google Analytics 4, for example—it only retains user-level data for a maximum of 14 months. Data warehouses offer virtually unlimited storage.

Second, cloud-based services often do not allow users to filter out what data is sent directly to destinations and are only capable of an “all or nothing” data dump. When paired directly with BI tools, the heavy load of data transferred in a dump can cause the tools to time out, making it difficult or even impossible to comb data for insights. When sending data from a warehouse to a BI tool, users can pick and choose exactly what data they send.

Third, data warehouses give engineers the tools they need to transform or “groom” their data. This is arguably the main benefit of having a data warehouse. Data that is transformed is higher in quality, contains fewer mistakes, can be queried faster, and can be manipulated more easily for advanced business intelligence. For more on common transformation scenarios, see our blog on how to build a data infrastructure for an ecommerce business.


Choosing the Right Data Warehouse

You need a data warehouse. A cloud-based one. According to a report by Gartner entitled The Impact of Data Warehouse Integration, 75% of databases will be in the cloud in 2023. And with cloud technology getting better by the day, this trend will only continue, making the choice between cloud and on-prem an obvious one. But what factors to weigh when choosing a cloud-based warehouse depends on the size of your organization, as well as how its data needs to be managed.

Smaller organizations that aren’t managing large data sets, and which have little or no human capital in the data analytics field, like some ecommerce businesses, are going to want to select a data warehouse for its ease of use and cost-effectiveness rather than for its performance.

Many larger organizations, or organizations that have very specific data needs, will have to face a tradeoff. This tradeoff is accurately described by the CAP theorem, which states that any distributed data store can only guarantee two of the following three things:

  1. Consistency
  2. Availability
  3. Partition tolerance, i.e. protection against failure

Every organization needs the third item, so in practice, the tradeoff is between consistency and availability

Let’s say you’re a bank. You need to have lots of historical data and row-level precision for all of it. If the data takes a little longer to query, that’s OK because real-time, minute-by-minute analytics are not so important. You would choose a data warehouse that prioritizes data consistency over the availability of writing and processing operations. 

Let’s say your company monitors air quality. You have hundreds or even thousands of sensors deployed in a given area or complex of buildings to detect real-time changes. If some of the sensors produce inconsistent data, it’s no big deal because, collectively, they will deliver the insights you need. You would choose a data warehouse that prioritizes the availability of writing and processing operations over data consistency.

Another factor that may narrow down your choice of solution quite conveniently is compatibility. If you are using a host of Google apps, Google BigQuery would be an obvious choice. If you are using a host of Microsoft apps, Azure SQL Database or Azure Synapse Analytics would be an obvious choice. Etcetera.

Now, let’s take a look at some solutions.


Best Cloud Data Warehouse Solutions for Small Businesses and Enterprises

#1 - Google BigQuery

A fully managed data warehouse that’s an obvious choice for companies that use a range of Google apps.


Pros: BigQuery is cost-effective and, like so many other Google tools, offers powerful analytics capabilities. Its storage is column-based, making it suitable for complex aggregations across massive datasets. It is fully scalable, reliable, and has a number of business intelligence features that help users glean insights and predict trends. 

Cons: You will need to be well-versed in SQL language to be able to effectively leverage its data analytics capabilities, as with most warehouses. While generally cost-effective, the price is affected by code quality (i.e., you pay for processing time in addition to storage), so queries need to be optimized to avoid high costs when pulling data.

Overall: Thanks to its separation of computing and storage layers, BigQuery can handle heavy computing operations, making it a suitable option for companies that prioritize availability over consistency.

#2 - Amazon Redshift

Having launched in 2012, Redshift is the oldest cloud data warehouse on the market. 


Pros: Amazon’s data warehousing solution is highly scalable and can store virtually limitless amounts of data (up to a petabyte or more). It offers a plethora of SQL-based tools, BI applications, and cluster management options. There is also a robust ecosystem of third-party solutions that can be integrated with it, making it essentially endlessly expandable. 

Cons: Unlike some of the other solutions listed here, Redshift’s computing and storage layers are not yet completely separate, so doing a lot of write operations could affect the performance of analytics queries. It is also not a fully managed service, making it unsuitable for companies without in-house staff that can keep up with ongoing maintenance.

Overall: Redshift offers excellent row-level consistency, making it a good choice for organizations that operate, for example, in the banking sector. But for organizations that need to perform a lot of writing and processing operations simultaneously, it may not be the best choice.

#3 - Snowflake

Snowflake is a high-end, yet easy-to-use data warehouse known for its advanced transformation capabilities, top query speed, heightened security features, and automatic scaling based on warehouse demands.


Pros: Snowflake is very flexible and can be used by data analysts and scientists of all levels of experience, as neither Python nor R is required to pull data from it. Its separation of computing and storage layers allows massive parallel processing for timely and scalable insights. It also offers secure and compressed storage for data that is semi-structured.

Cons: Though it’s capable of running highly elaborate queries that wouldn’t be possible with other tools, it doesn’t perform so strongly when it comes to dashboard creation; custom functions and routines must be coded. 

Overall: In terms of the trade-off between consistency and availability, Snowflake is somewhere between BigQuery and Redshift, making it most popular among mid-sized companies, which don’t need to perform high-volume writing and processing operations or require absolute consistency across high volumes of data.

#4 - Azure SQL Database

A fully managed database-as-a-service provided as part of Microsoft Azure, the company’s cloud computing platform. It’s a natural consideration for organizations that use Microsoft’s suite of business tools.


Pros: Azure SQL DB is an established option for cloud database hosting that offers an excellent user journey from creation of SQL servers to configuration of SQL databases. Its interface is user-friendly and its functionality makes playing with data easy. It is known for its scaling capabilities, which reduce cost and optimize performance when application usage is low.

Cons: SQL DB is not designed for major data loads. Instead, it is designed for online transaction processing (OLTP) workloads, meaning it can handle high volumes of small read-and-write operations. Also, at lower price points, performance can be less-that-satisfactory.

Overall: This is not a “big guns” solution for enterprises that need heavy analytics firepower. But it is a good option for businesses with simpler query patterns and smaller data loads. In terms of the consistency vs. availability tradeoff, it’s closer to Redshift (i.e. it favors consistency), only its database size is more limited.

#5 - Azure Synapse

Another part of the Microsoft Azure platform, Azure Synapse is an analytics service that combines data integration, enterprise data warehousing, and big data analytics. It is similar to but different from Azure SQL DB.


Pros: Azure Synapse Analytics is also highly-scalable and well-suited to data warehouse scenarios with large data tables because of its distributed computing. It uses massive parallel processing (MPP), which lets users run complex, high-volume data queries across multiple nodes at lightning speed. Puts a premium on data security and privacy.

Cons: Though it is a great choice for businesses already using Microsoft tools, it is harder to integrate with non-Microsoft tools than other data warehouses. Also, since the service is constantly being updated, it can get buggy at times.

Overall: In contrast to SQL BD, Azure Synapse is designed for online analytical processing (OLAP), making it suitable for processing huge datasets in real time. It prioritizes analytical availability over data consistency. If your warehouse contains 1TB of data or more, consider opting for Azure Synapse over Azure SQL DB. 

#6 - Firebolt

A newcomer to the field, Firebolt claims to be a next-generation data warehouse, whose performance is up to 182 times faster than that of other data warehouses with SQL-based systems.


Pros: Part of what makes Firebolt’s performance so fast is new techniques in data compression and parsing. When querying, it accesses much smaller ranges of data through the use of indexes, instead of entire partitions and segments like other data warehouses, thus freeing up network bandwidth. It can query data at an impressive speed across multi-petabyte datasets. There is no limit to scalability.

Cons: Because Firebolt is so new, it cannot yet integrate natively with a large ecosystem of business intelligence tools or platforms. But this problem can be solved by using an ETL tool to funnel data to and from the warehouse. 

Overall: Since Firebolt’s storage and computing layers are separated, users can pay for any number of nodes or node sizes. This makes it cost-effective for organizations large and small. Clearly, it’s targeted to businesses that need blazing fast analytics. Keep in mind that, to use it properly, you’ll need experienced in-house data analysts.


No Right or Wrong Answer

All of the above solutions are serious contenders in the field of data warehousing, yet no solution is 100% complete. Each one puts a premium on either consistency or analytics availability. Regardless of which is more important for your business, one thing is certain: a cloud data warehouse is a key element of any modern data strategy. Which warehouse will you choose?


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