Now that you set up your bulk export and that you understand how to query your Google Search Console (GSC) data in BigQuery, let’s talk about an important topic: how much is it going to cost you and how can you limit those expanses.
Let’s go!
How BigQuery charges you
BigQuery is a relatively inexpensive and efficient platform which you can use for free for a while. However, depending on the amount of data you query and store, it might cost you.
Keep in mind that we are strictly talking about BigQuery here, not about the other APIs or services offered in Google Cloud.
Data stored in your project
BigQuery offers a free tier of 1 TB of data analysis and 10 GB of storage each month. If you go beyond these limits, $20 per TB per month for active storage will be charged to you.
For huge datasets, this might be a concern, but as you can see, this is still pretty cheap.
Computing prices
Where things can be more pricey is on the computing side. You see, each time you run a query in a project, it uses processing power which is quantified in storage amount.
For on-demand pricing, you get 1 TB per month for free, which is a very good amount. If you go over that limit, it will be $5.00 per TB of data processed. Again, it all depends on what you do with your data, but, with big data sets, cost can quickly creep up.
A few tips on how to limit cost
Here are a few simple tips to limit the costs of BigQuery:
Query only the data you truly need
When we use BigQuery, with a big amount of data available, it is often tempting to build queries for any question we have.
But, as you can see above, it might not be for free. So, try, when it makes sense, to use the GSC UI or the API for needs which don’t necessarily require a huge level of precision or going back beyond the last 16 months
Avoid querying all your data
With SQL, it is a good practice to avoid querying all your dataset. Using a SELECT * (select all) means that you ask BigQuery to scan all your rows and columns, which is not cost sensitive. Specify only the columns you need.
Filter data
Use the WHERE clause to limit the data you are using.
Use the estimated query costs
Even more simple, it is a good practice to always check what the estimated query cost is going to be before running it. It might help you avoid super costly queries (which might be optimized further).
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