The cloud data warehouse market is expected to nearly triple by 2026. 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.
Read our recommended considerations below, or skip down to the list of warehouses.
What to Consider When Choosing a Data Warehouse
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:
- 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 one obvious choice. If you are using a host of Microsoft apps, Azure SQL Database or Azure Synapse Analytics would be obvious choices. Etcetera.
Let’s take a look at some of the top cloud data warehouses with these considerations in mind.
Best Cloud Data Warehouse Solutions for Small Businesses and Enterprises
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#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: While generally cost-effective, the price is affected by code quality (i.e., you pay for processing time in addition to storage), so queries really 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 than 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 - ClickHouse
An open-source column-oriented database management system, ClickHouse has gained rapid popularity for its ability to process big volumes of data with lightning-fast query execution.
Pros: ClickHouse stands out for its incredible performance in handling large-scale data analytics. It's designed to perform real-time query processing on datasets ranging from a gigabyte to petabytes in size. Its columnar storage format allows for efficient data compression and fast reads, making it ideal for analytical queries over large datasets. The system is highly scalable, both vertically and horizontally, and can efficiently handle concurrent queries.
Cons: While ClickHouse excels in speed and performance, it will require some technical expertise to set up and optimize for specific use cases, which will be a challenge for organizations without dedicated data teams. Also, its primary focus on analytics means it may not be the best choice for transactional processing use cases. Finally, being relatively new to the market, it may lack some advanced features offered by more mature warehousing solutions, like RedShift and BigQuery.
Overall: ClickHouse is an excellent choice for organizations looking for a high-performance, scalable data warehouse solution, especially for real-time analytics and handling massive datasets. Its ability to deliver fast query responses makes it a strong contender for businesses prioritizing analytics speed and efficiency. It's particularly well-suited for use cases where fast data ingestion, large-scale data analysis, and real-time reporting are critical.
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 one will you choose?
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