![]() ![]() This means that a table that is simply being QUERIED from (or having data manually exported/copied from) DOES NOT indicate the table has had its data updated, and thus it will retain Long Term Storage status (if already applied). The critical consideration here is the keyword of updated. Once the table is updated, however, the table is automatically reverted back to normal Storage Data pricing and the 90-day timer is reset. Once the 90-day mark has been met, the Storage Data price drops by 50%, from $0.02 per GB, per month to $0.01 per GB, per month.įunctionally, a table marked as Long Term Storage is no different than normal. Long Term Storage is simply a flag that is automatically applied to any table that has not been updated within the previous 90 consecutive days. Long Term Storage DataĪfter determining the total Storage Data size above, it’s also worth considering how much of that data will qualify as Long Term Storage. If we had a dataset that was a total of 500 TB of data, we can simply multiple that by 1000 (to convert to GB), then multiple the result by the $0.02 per GB fee.Įven storing a whopping 500 TB of data is (at most) a cost of roughly $10,000 per month in BigQuery. Yet even a massive table that size is only about a third of a dollar per month in storage fees. ![]() For example, the public-data:samples.gsod table is around 16.1 GB in total with 114 million rows. It is simple to view the Table Size for the various tables in a BigQuery dataset to give a rough estimation of the Storage Data you’re using. Storage Data is by far the simplest component of BigQuery pricing to calculate, as BigQuery currently charges a flat rate of $0.02 per GB, per month for all stored data. While the official Pricing page includes a lot more details and useful information that’s worth looking over, in this guide we’ll briefly explore each of the three pricing elements in order to estimate our monthly costs. Since Google BigQuery pricing is all based on usage, there are primarily only (3) core aspects of your BigQuery data storage you need to consider when estimating the costs: Storage Data, Long Term Storage Data, and Query Data Usage. Google Cloud Platform Pricing Calculator. ![]()
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