BigQuery Consulting

BigQuery has a freemium model of pricing. All users get 10 gigabytes of storage and one terabyte of queries per month free of charge. Keep in mind that to use your data, you have to store it. Therefore, there are two types of costs associated with BigQuery. The first is storage – or how much data you will have to run queries against.

The second is analysis pricing, or paying for the computational resources to run a query against your data.

Pricing can be on a “pay as you go” model or a reserve model. Pay as you go is more expensive, but you only pay for what is used.

A reserve model is like signing a contract. You guarantee a price that you will pay per month, with a dollar amount minimum. You’ll get better pricing, but you’re on the hook for a minimum fee.

For current rates, please see BigQuery pricing or contact Three Ventures for support.

Yes. We frequently work with clients who use Google Marketing Platform 360 products with BigQuery.

With the native integration to BigQuery from some of the 360 products makes this an obvious option.

With the migration to Google Analytics 4 (GA4), there’s also a native integration for the free version. Many clients are using this as a solution to the data thresholding and cardinality issues currently experienced with GA4.

Our normal hourly rates apply for BigQuery consulting. As a very senior, and very technical consulting firm, we do not publish our rates.

However, we are not the cheapest option. Remember, you get what you pay for.

To get our current rates, please call or contact us.

BigQuery is commonly compared to Amazon (AWS) Redshift. While both are cloud data warehouse platforms, there are some fundamental differences.

Reasons to use BigQuery include cost, technical specificity, company culture or IT preferences. The biggest technical specification with BigQuery is the much larger column width for individual tables. This setup is ideal for quickly working with disparate data sources without much preparation or optimization effort.

Company culture also plays a significant role. If a company primarily uses Google products, it’s easy to step into using a Google data warehouse. If a company is more integrated with AWS, then they will typically choose to stay with an AWS first approach.

IT preferences can also play a role in the decision making process, specifically around internal skills and proficiencies. The required technical skill sets might not be available in-house, so a trusted partner is key.

On the cost side, BigQuery is by far the more affordable option with the most features. Given their freemium model, this is not surprising. Most organizations, however, don’t have the technical, analytical and data science skill sets to fully unlock its potential.