Cloud Data Warehousing

Cloud Data Warehouse

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In the twenty-first century, data is the new gold. Almost all (successful) companies rely on data to grow. Understanding and adapting to clients needs are the most direct ways to increase sales. The bigger the company, however, the more data that needs to be processed in order to have that understanding. That’s where data warehouse systems come into play. We will discuss what exactly cloud data warehouses is.

A typical company runs many different applications and systems. One application might be used for ordering supplies from a third party, one might be used for running the shop, and another might be used for connecting to a shipping company. These are just a few examples. In order to answer critical business questions, data from all these different systems needs to be easily accessible, preferably in one place. This can be useful to create reports and insights based on which decisions can be made that will be best for the business. Data warehouse systems are doing just that: gathering data from different sources across the whole company, processing all of it, and making it available for data analytics.

Traditionally, these systems required a lot of servers. You needed to purchase them, install the data centers, and hire a specialized engineer. But since a lot of companies are moving their infrastructure into the cloud nowadays, there is also a new norm of using cloud data warehouse systems instead of on-premises ones.

There are many advantages to cloud data warehouse systems. There’s no need for purchasing servers up front, no risk of under- or over-provisioning, much more flexibility, and very easy scaling. On top of that, they usually bring better performance. Also, since the data is in the cloud, business teams usually don’t need to wait for updated information as they often do with on-premises systems. From a technical perspective, they don’t differ that much from their on-premises counterparts.

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Flavors

If you want to go for cloud data warehouse systems, you first need to make some decisions. There are a few different architectures and pricing models for cloud data warehouse systems you can choose from.

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Architecture vs Pricing

Most of the cloud data warehouses come in either paid-per-server clustered versions or pay-per-use fully managed solutions. In the first option, you’ll responsible for managing the cluster. You’ll also be responsible for scaling the cluster up and down when needed. Basically, you will see your data warehouse as a cluster. This architecture is usually associated with a pay-per-server model. Therefore, knowing how big your cluster is and having control over its capacity make it easy to predict costs.

Another option is to use fully managed solutions. In this case, the cluster is “hidden” from you. You won’t need to take care of it because all the maintenance tasks and scaling will be taken care of for you by the provider. This architecture will, in most cases, provide a pay-per-use model. Since there are no servers/clusters assigned to you, you’ll pay based on usage or query. The advantage of this approach is that you don’t have to manage anything, so you only have to focus on your data.

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Examples of Data Warehouses

1. Azure Synapse Analytics

In November 2019, Microsoft announced its new platform called Azure Synapse Analytics, which replaced its previous offering, Azure SQL Data Warehouse. While SQL Data Warehouse was offered only in a pay-per-cluster architecture, the new Azure Synapse Analytics gives you a choice and offers both pay-per-cluster and pay-per-usage models. The key selling points of Azure Synapse Analytics are the abilities to combine data warehouses with big data analytics into one platform and to run on a petabyte-scale.

2. Amazon Redshift

Amazon’s Redshift platform is probably the most well-known cloud data warehouse solution on the market. It offers easy integration with data lake solutions and all other Amazon services. Therefore, it’s a natural option for companies that run the rest of their infrastructure on Amazon. Redshift promises to easily query petabytes of data from various data sources and databases.

3. Google BigQuery

Google also has its own DW solution. As one of the services available in Google Cloud Platform, Google BigQuery offers seamless integration with all the other services offered by Google. It’s a fully managed solution with a pay-per-usage pricing model. Google BigQuery offers a few unique functions, like multi-cloud analysis, building machine learning models using SQL, and an in-memory analysis service

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