What is a Cloud Data Warehouse? (+ 6 Top Choices for 2024)

Data Engineering
Feb 8, 2024
What is a Cloud Data Warehouse? (+ 6 Top Choices for 2024)

Businesses that process large volumes of data need large volumes of data storage. In the past, if a business had countless data sources and terabytes of data, they would store it physically on their own on-premises servers. But as apps became increasingly based in the cloud, the storage had to move too - and cloud data warehouses are the result.

Today, we’re going to talk about cloud data warehouses - what they are, why they matter for data analytics and which are the best ones to choose in 2024 and beyond.

What is a cloud data warehouse?

A cloud-based data warehouse is a centralized data repository that is hosted on the internet and can be accessed online by anyone with an internet connection. It hosts structured and unstructured data and it is built to help businesses aggregate, query, analyze and prepare data for further use.

Cloud data warehouses are commonly used in businesses that have large volumes of (typically structured) data that they will further use for data analysis, visualization, reporting and other needs.

A data lake is a common term that is used interchangeably, but the main difference is that data lakes typically handle unstructured data.

PS. for more info on data warehouses, check out this post.

The benefits of using cloud data warehouses

Storing data in the cloud has a range of use cases and benefits, reflecting not only in improved operations and faster insights, but also in happier customers that stay with you for longer periods of time.


By far, the biggest advantage of a cloud data warehouse compared to one that is located on premises is scalability. With cloud data warehouses, you are not limited by physical space and you can increase (or decrease) your storage capacity according to your needs. 

Need to 10x your data source’s load capacity overnight to support suddenly popular BI dashboards? You can do it with one click or even automatically scale up without any clicks at all.

The speed of insights

Imagine you need to pull data from a data warehouse located on the other end of the United States. It won’t take decades, but it won’t be lightning speed either - the data needs to be pulled and loaded. If you have large volumes of data and many data sources, this can slow down your operations. Cloud data warehouses allow real-time insights.

A huge part of this is something called Massively Parallel Processing or MPP. This process means that there are multiple servers running at the same time, which distributes processing and improves query performance.

There is also the fact that data warehouses are columnar: data is stored in columns rather than rows. This structure allows you to run aggregate queries much faster due to optimized data retrieval.


With a data warehouse that is located on-premises, your business takes on the bulk of the costs. Maintaining the hardware, solving problems, hiring and training people to manage the data warehouse - it all costs a good chunk of money. In most cases, cloud data warehouses are more cost-effective since all of the costs are managed by the data warehouse provider.


Have a remote team or a company that works across different locations? A cloud data warehouse can be accessed from anywhere that has an internet connection. The cloud infrastructure fosters team collaboration, whereas a traditional data warehouse makes everyone tied to a single location.


With cloud data warehousing, you can rest assured that your data is safe. Modern cloud services use access control, encryption, compliance certifications and more to ensure that you and only you have access to your data.

Cloud data warehouses come with a host of benefits, such as automated backups and disaster recovery. This means that you never have to worry about backups again and in case something does happen, your data can quickly be restored.

You also get regular security updates from your vendor, as well as patch management. In case of any vulnerabilities in the data warehouse, the provider takes care of things, and not your data scientist or analyst.

Last but not least, you get Identity and Access Management (IAM). This allows you to fine-tune who gets access to data through features such as multi-factor authentication. 

Automatic updates and maintenance

Traditional data warehouse solutions put the burden of updating and maintaining the infrastructure on you, or things like resource provisioning. Modern cloud data warehouses include updates and maintenance in the monthly payments, which means you never have to worry about anything other than using your data.

Integrations with other tools

Compared to on-premises data warehouses, the ones that work in the cloud connect to other tools more easily. Some examples include data visualization tools such as Luzmo, storage services and machine learning tools. Most of the tools we’ll cover in a minute have built-in integrations with these tools, right out of the box.

The best cloud data warehouses in 2024

Based on your needs, budget and what you intend to do with your data, you’re going to choose a different cloud-based warehouse for your needs. Here are some of our top choices of cloud providers for real-time data analytics, visualizations and more.

Amazon Redshift

amazon redshift as a cloud data warehouse

Best for: businesses with large data volumes

If you want a scalable data warehouse that is fast and integrates well with the AWS ecosystem, Amazon Redshift is the first choice to go for. It supports standard SQL queries, so your team won’t struggle with the basic commands. It’s fully managed and AWS takes care of maintenance, disaster recovery and more.

Perhaps one of the biggest selling points is that the Amazon Redshift and AWS community is vast, so if you get stuck with something, such as a problem with datasets, you’ll have plenty of people to ask for help.


snowflake as a cloud data warehouse

Best for: cloud-agnostic businesses

Snowflake is the only cloud data warehouse that does not run data in its own cloud. Thanks to global data replication, you can move your data around to just about any cloud in any part of the world.

This means you can separate your storage and compute and run multiple virtual warehouses at the same time, isolating different queries. This translates to high data concurrency.

This multi-cloud support, coupled with strong security measures, makes Snowflake the ideal choice for enterprise businesses.

Clickhouse Cloud

clickhouse cloud as a cloud data warehouse

Best for: businesses that need an open-source, serverless cloud data warehouse that is extremely fast

If having an open-source data warehouse architecture is important to you, Clickhouse Cloud should be your go-to choice. Coupled with its high-performance query processing, especially with large workloads, it makes a compelling case for businesses of all sizes.

Luzmo customers love Clickhouse because of how fast the querying is in an analytics use case.

Open-source means that there are no licensing fees for Clickhouse. However, their cloud offering has a license fee. There is a huge degree of flexibility - you can add new nodes and clusters, thanks to the horizontal scalability of this warehouse. It works well with structured and semi-structured data.

Google BigQuery

google bigquery as a cloud data warehouse

Best for: businesses on a budget that don’t run queries that often

One of the most popular cloud data warehouses for a reason, BigQuery allows pay-as-you-go pricing, which means you only pay for the queries you run. This makes it a great solution for smaller businesses that want the convenience of the cloud but without the associated costs.

It’s fully managed and serverless, allows for real-time performance and plays well with the rest of the Google cloud ecosystem. If your data science team already uses SQL, this is one of the most logical choices you can make.

Microsoft Azure SQL Data Warehouse

microsoft azure as a cloud data warehouse

Best for: mid-size businesses on the Microsoft stack

If Power BI is your business intelligence tool of choice, then you want to go with Microsoft Azure for cloud warehousing needs. Leaning into Power BI also means that Azure supports both cloud and on-premise warehousing use cases.

Business users that need machine learning integrations will appreciate the connectivity with Azure Databricks and Azure Machine Learning. For your big data needs, there is the integration with Azure Synapse Analytics.

Oracle Autonomous Data Warehouse

oracle autonomous cloud data warehouse

Best for: businesses that use other Oracle applications and prioritize data security

Ease of use is the name of the game here, and you won’t need advanced knowledge of enterprise data management to use Oracle. Autonomous data management means that Oracle handles the everyday, mundane tasks, so you can focus on data analysis and BI reporting.

It’s enterprise-friendly, allowing easy migration from on-premise to the cloud, as well as consistently high performance under varying workloads.

How does a cloud data warehouse work?

Data doesn’t just magically appear in a cloud data warehouse - it’s a complex process that happens in the background. From raw data to decision-making, it’s a long journey. We’ll try to explain it in a few short steps.

The data ingestion

The data is ingested from a variety of data sources, such as APIs, databases, various tools, apps, and more. The data then goes through an ETL (extract, transform, load) process so that it can be hosted in a structured way. To prepare the data, it often goes through processes like normalization, enrichment, and data cleansing.

The data storage

Data in a cloud data warehouse is stored in a structured format, making it easier for other tools and processes to “read” it later on. At this point, the data can also be partitioned for easier access in the future. It is also convenient if your device's disk is full because such data will not take up space directly on the hard disk.

Metadata management

Besides data itself, cloud data warehouses host data about data, or metadata. This is information about how the data is structured, and what relationships and characteristics it has. 

Compute resources

When there are multiple clusters or nodes, a cloud data warehouse uses distributed computing to parallelize the execution of queries. This allows for handling large volumes of data processes easily later on.

Query execution

The user gets to interact with the databases in the cloud data warehouses by using SQL or similar programming languages. They use SQL queries to submit the data for analysis and execution.

Analysis and reporting

At this stage, the data is ready for business insights. Clean, structured and formatted data can be used for visualization. For example, you can connect your cloud data warehouse with a tool such as Luzmo to visualize it in the forms of graphs, charts, scatter plots, histograms and more, helping you highlight your key business metrics, create client-facing dashboards to embed in a web app and more.

You can also use the data for ad-hoc analysis, by querying it interactively, exploring the different dimensions and digging deep into queries based on your findings.

Wrapping up

For a modern business that deals with large data volumes and relies on that data to make important business decisions, using cloud data warehouses is a no-brainer. The flexibility, speed of insights, data integration with BI tools, all make it a logical choice for data-driven businesses looking into the future.

And once you’ve chosen your ideal cloud data warehouse, you can do much more with your data - visualize it, for example. With Luzmo, you can create beautiful, functional embedded analytics dashboards for your SaaS app. No matter the cloud data warehouse you choose, we’ll help you create interactive dashboards your customers will love.

Get a free demo to learn more today!

Mile Zivkovic

Mile Zivkovic

Senior Content Writer

Mile Zivkovic is a content marketer specializing in SaaS. Since 2016, he’s worked on content strategy, creation and promotion for software vendors in verticals such as BI, project management, time tracking, HR and many others.

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