#1 The amount of data you stored in BigQuery (i.e. the storage cost)
#2 The amount of data you processed by each query you run (i.e. the query cost).
#3 The cost of connecting your Google Analytics account to BigQuery
The first 10 GB of active storage is free each month. After that, you would be charged $0.020 per GB of active storage.
The first 1 terabyte of data processed is free each month. After that, you would be charged $5 per terabyte (TB) of data processed.
But before you even start processing a large amount of data, you should ask yourself why you are even collecting that much data.
Is it even necessary? Do you even have permission?
Because collecting unnecessary data about your website users and customers are against GDPR.
GDPR compliance requires practising data minimization.
Data minimization is the practice of collecting, storing and using only that personal data, which you absolutely need, for the purpose you have specified in your privacy policy.
Now when I hear someone bragging about processing millions or billions of rows of data, my first thought (unless proven wrong) is that their company is not GDPR compliant.
They are a sitting duck and just waiting to be sued by some fanatic privacy lawyer.
If you do not question your data practices, if you process the data as you please, you could find yourself explaining to a supervisory authority why you should not be fined $12.5 million or 2% of your annual global turnover (whichever is higher).
Google BigQuery can get expensive pretty fast if you are dealing with terabytes or petabytes of data every day and you do not construct your queries properly or pull too much data too frequently.
Cost of using BigQuery for Google Analytics 360 users
So you can connect your Google Analytics account to the BigQuery account for free.
However, you could still be charged based on your data storage and processing.
If you are using Google Analytics 360, you get $500 of free credit each month, giving you up to 25 terabytes of data storage and 100 terabytes of data to query each month.
You will be charged only when you exceed this free usage limit (which is very unlikely).
Most GA360 users generally don’t pay anything extra for using BigQuery.
Cost of using BigQuery for Google Analytics 3 (or earlier version) users
Google Analytics 3 (or earlier versions) does not come with a free connection to BigQuery.
In order to connect your Google Analytics account with BigQuery, you would need to use a third-party solution which will cost you a little bit of money.
However, it is unlikely to cost you even 1/100 of $150k / year, which all GA360 users pay to access GA data in BigQuery.
You could also be charged extra based on your data storage and processing.
However, your credit card will not be charged if you remain within 10 GB of data storage and one terabyte of queries per month.
Once you exceed this free usage limit, your credit card will be charged.
Long story short,
BigQuery is not completely free to use with Google Analytics unless you are using Google Analytics 4 (GA4) and you stay within the free usage limit every month.
Cost of using BigQuery for Google Analytics 4 users
Google Analytics 4 comes with a free connection to the BigQuery.
So you won’t need a third-party solution to connect your Google Analytics account with BigQuery.
However, you could still be charged based on your data storage and processing.
As long as you remain within 10 GB of data storage and one terabyte of queries per month limit, your credit card will not be charged.
Once you exceed this free usage limit, your credit card will be charged.
Note: If you start querying gigabytes or terabytes of data daily in BigQuery, you need to be mentally and financially prepared to pay a considerable amount of storage and/or processing fees each month.
Your query cost is affected by the number of columns your query returns:
Following is an example of a query which would return one column named ‘id’:
Following is an example of a query which would return two columns named ‘id’ and ‘creation_date’:
Note how adding a second column increases the query size from 236 to 473 MIB (megabytes).
Now, what would happen if we wrote a query that returns all the table columns?
So if we try to return all the columns of this data table, 29.4 GB of the data would be processed.
So only query the columns you really need.
Your query cost is also affected by the size of each column.
The query below returns one column named ‘id’:
The query below returns one column named ‘body’:
Note how the size of the query increased from 236 MB to 26.3 GB.
So you must be very careful about the size of the column you want to retrieve.
#5 Avoid using SELECT *
SELECT * means returns all the columns of the data table.
Now, if your data table contains a lot of columns and some of the columns are very big in size (maybe in GB or TB), using SELECT * could considerably increase your query cost.
So the best practice is to avoid using SELECT *
#6 Applying a LIMIT clause to a SELECT * query does not affect the query cost
This is because the LIMIT clause controls the number of rows/records your query returns.
But as you know by now, the number of rows/records your query returns doesn’t affect your query cost.
With the LIMIT clause:
Without the LIMIT clause:
#7 Set up Budget alerts
Set up cloud billing budgets and budget alerts which trigger email notifications to billing admins and/or project managers when your costs (actual costs or forecasted costs) exceed a percentage of your budget (based on the threshold rules you set).
These email alerts inform you of your usage costs trending over time.
Note: Setting up a budget does not automatically cap Google Cloud usage or spending.
BigQuery Mate is a Google Chrome extension through which you can estimate query costs within the query validator. However, this extension works only on the old user interface of BigQuery.
Unlike the Google Cloud pricing calculator, this extension works even when you query less than 1 terabyte of data.
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