Performance capabilities of data warehouses and how Cube can help

1

Cube Cloud

There are two ways you can go about running Cube in production. First, you can use the hosted Cloud deployment of Cube. Or, run a production-ready setup yourself.

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2

Google BigQuery

Let's start with BigQuery, a serverless big data warehouse available as a part of the Google Cloud Platform. It's highly scalable, meaning that it can process tiny datasets as well as petabytes of data in seconds, using more cloud capacity as needed. You're able to manage its performance by choosing the pricing model (on-demand by default, flat-rate available as an option) which affects how BigQuery allocates slots, its virtual compute units with CPUs, memory, and temporary storage used to execute queries. BigQuery automatically calculates how many slots are required by each query, depending on query size and complexity. It's worth noting that every query competes for the slots with other queries within a GCP project, and also with other projects if you stick with the default on-demand pricing model as many do.

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3

Amazon Redshift

Snowflake Data Cloud, Google BigQuery, and Amazon Redshift are all good examples of such data warehouses and the most used and popular choice for storing huge amounts of data. If your company has a data warehouse in use, chances are it's one of these behemoths.

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4

Amazon CloudFront

Now let's take the speed of light into account! Seriously, let's imagine that a user from California sends a request to your app deployed in a popular region, e.g., us-east-1 on AWS. With the roundtrip of twice the distance of 3,000 miles and the speed of light of 186,000 mi/s, the minimum delay between sending the request and getting the response would be 30 ms. Wait, and what about our overseas users? You can actually use massively distributed CDNs like CloudFront or Netlify Edge to bring your app closer to users, but how practical is it for the data warehouses that your app interacts with?

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