Choosing A Cloud Data Warehouse In 2021

It’s 2021 and by now any data-driven organization should have a centralized, well-configured cloud data warehouse. Why? Well, you will need a central repository for all your data so that teams across your organization can access it in order to run queries and do some pretty cool analyses.

In terms of speed, dependability, security, and ease of use, cloud-based warehouses and repositories surpass their on-premise counterparts. They make it simple for the entire business to access data in real-time and allow users to modernize their procedures as new technology is introduced.

Selecting the best cloud data warehouse is actually a big deal. After all, moving from one warehouse to another does take up a huge amount of time and effort. So, in this article, I’m going to take you through how to choose the right cloud data warehouse that fits your needs.

Important Points to Consider when Choosing a Cloud Data Warehouse

Let’s start with some important points to consider when choosing the right Cloud Data Warehouse.

The Business Needs for a Cloud Data Warehouse

While cloud data warehouses are designed to be generalizable across different business divisions, you should think carefully about how you intend to utilize yours. The criteria for assessing platforms can differ depending on the use case and business needs.

For example, an organization that plans to use JSON in their data warehouse may pick Snowflake over Redshift because Snowflake enables storing and querying JSON natively. Organizations a lack of technical resources may wish to avoid Redshift because it requires a lot of continuous monitoring.

The Security of a Cloud Data Warehouse

You should know exactly what your security goals are, what security protections each provider offers, and what procedures they employ to protect your apps and data. Also, make certain you fully comprehend the exact areas in which each party is liable. These providers should have detailed policies and manuals that you can request directly from them.

Consider what security features are available free of charge and out of the box for each vendor you’re reviewing, what additional paid services are available from the providers themselves, and where you might need to supplement with technology from third-party partners.
AWS and Google Cloud, for example, make this procedure very simple by presenting their security features, paid goods, and partner integrations on their respective security sections of their websites.

Because security is a key priority in the cloud (and everywhere else), it’s vital to ask specific and extensive questions about your specific use cases, industry, legal needs, and any other issues you may have.

The Scalability of a Cloud Data Warehouse

If your organization has plans to expand (especially in terms of data storage and resources) then you should consider choosing a warehouse that can expand with your company. This is one of the most significant advantages of today’s cloud data warehouses, but keep in mind that each warehouse scales differently (with different costs associated with it).

Make sure you consider how easy it is to scale the warehouse, how much it costs to scale, and what technical resources you’ll need to expand along the way when picking a supplier. In an ideal world, you’d find a system that scales automatically to handle any amount of concurrency or query volume.

Examine the Technical Inner Workings for Cloud Data Warehouses

In terms of data requirements and assumptions, remember that cloud data warehouses differ. Snowflake, for example, supports semi-structured data in Object and Array forms, whereas Redshift does not. As a result, Redshift can make more confident assumptions about the data’s structure (such as patterns in the data type, which influence compression scheme selection) and better optimize its storage speed; however, data retrieval speed may be slowed due to Redshift’s lack of support for denormalized data structures.

Which technique is ideal for an organization should be determined by the level of flexibility required. For example, if an organization must store data with a non-predefined structure, Snowflake’s structure may be advantageous.

Speed of the Data Warehouse

Speed can be thought of in two ways: access speed and processing speed. Which warehouse can assist you in achieving the quickest query times? How quickly can you enter and exit the solution with your data? And, in relation to that, how will you maintain your warehouse to sustain that speed?

Different solutions provide different options for achieving the desired speed. Amazon Redshift uses parallel computing and stores data in columnar format. To produce a result in seconds, Google BigQuery uses as many resources as are required. Because Snowflake does not share compute clusters with other virtual warehouses, a query in one virtual warehouse has no effect on the others. The performance of Azure gen 2 is comparable to that of the other options.

S3 scales via parallel requests as a cloud storage solution, so you can keep adding prefixes to get the performance you require.

You need ETL solution to keep your warehouse functioning smoothly by ensuring that your data is cleaned, de-duped, and appropriately streamed in. This reduces the amount of time you have to spend managing the infrastructure, allowing you to spend more time working with the data rather than merely maintaining it.

Be familiar with costing and resource bundling.

The biggest cloud data warehouses bundle resources in various ways and calculate expenses in differently. Redshift, for example, combines storage and compute resources. This simplifies pricing, but it also makes users accept predefined memory, storage, and I/O numbers for instance types.

BigQuery’s costing is more detailed, with charges for storage, streaming inserts, and most importantly bytes read. The provider also does not charge for hardware resources. Billing is thus less predictable, as they are mostly determined by the number of bytes read in queries, making it difficult to correctly forecast consumption.

The lower-level technical aspects of computation cost, such as logical CPU cores and I/O, are bundled into a “Data Warehouse Unit” in Azure’s data warehouse (DWU). Storage and DWUs become factors in cost considerations. Users can put their DWU usage on hold, and only storage charges will be applied.

Snowflake abstracts physical resources into credits, which grow in proportion to the number of virtual warehouses and the amount of resources in each. A virtual warehouse is a collection of machines that can run queries, load data, and conduct other data processing tasks. Storage is charged on a per-terabyte-per-month basis.

The majority of cloud data warehousing providers offer flat-rate pricing. Redshift, for example, features a Reserved Instances pricing model that offers reductions if an enterprise commits to and pays for resource utilization for a year or more. Businesses with large deployments can use Reserved Instances to control costs as consumption grows.

Most cloud data warehouse services also provide flat-rate pricing. For instance, Redshift has a pricing model called Reserved Instances that provides discounts if an organization commits to and pays for resource usage for a year or more. Reserved Instances allow businesses with large deployments to manage their costs as usage becomes more predictable.

While it’s difficult to predict expenses precisely before deploying a data warehouse, an examination of anticipated workflows can help you come near. Ask questions such as:

• On a monthly basis, how much data will you need?
• How often will you need the data updated?
• How often will you need to deploy analytics jobs?
• How much data will be needed for the analytics jobs to run?

These questions can assist you in calculating projected utilization, allowing you to compare providers and make an informed decision.

All of the big providers offer free trials. Consider utilizing them to get an approximate idea of how much things will cost at scale.

We’ve put together a useful cloud data warehouse comparison chart to help you make your decision.


While the points discussed in this post may not give you all the information you need, it will assist you in developing a good framework when deciding which cloud platforms will be best for your needs. I do suggest taking this one step further and add further detail by doing a thorough examination of your organization’s requirements to uncover extra aspects that will help you make a more informed choice.

This will be key to determining which platform provider will be the one that can deliver the features and resources that will best support your ongoing business, operational, security, and security goals.

Interested in more? Check out our product, Vantage Point. Vantage Point (VP) is a no-code, click & go business acceleration tool which enables data driven decisions across your business. It drives interactivity across all parts of your organization by communicating value (KPIs), autogenerating tasks with cutting-edge ML/AI technology and enabling users to combine VP’s ML/AI recommendations with their own analysis. You can finally track the exact ROI impact throughout your entire business with Vantage Point.

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Written by

Devasha Naidoo

Senior Technology Architect

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