Bigquery and Tableau

Question

I attached Tableau with Bigquery and was working on the Dash boards. Issue hear is Bigquery charges on the data a query picks everytime.

My table is 200GB data. When some one queries the dash board on Tableau, it runs on total query. Using any filters on the dashboard it runs again on the total table.

on 200GB data, if someone does 5 filters on different analysis, bigquery is calculating 200*5 = 1 TB (nearly). For one day on testing the analysis we were charged on a 30TB analysis. But table behind is 200GB only. Is there anyway I can restrict Tableau running on total data on Bigquery everytime there is any changes?

Answer 1

The extract in Tableau is indeed one valid strategy. But only when you are using a custom query. If you directly access the table it won't work as that will download 200Gb to your machine.

Other options to limit the amount of data are:

• Not calling any columns that you don't need. Do this by hiding unused fields in Tableau. It will not include those fields in the query it sends to BigQuery. Otherwise it's a SELECT * and then you pay for the full 200Gb even if you don't use those fields.
• Another option that we use a lot is partitioning our tables. For instance, a partition per day of data if you have a date field. Using TABLE_DATE_RANGE and TABLE_QUERY functions you can then smartly limit the amount of partitions and hence rows that Tableau will query. I usually hide the complexity of these table wildcard functions away in a view. And then I use the view in Tableau. Another option is to use a parameter in Tableau to control the TABLE_DATE_RANGE.

Answer 2

1) Right now I learning BQ + Tableau too. And I found that using "Extract" is must for BQ in Tableau. With this option you can also save time building dashboard. So my current pipeline is "Build query > Add it to Tableau > Make dashboard > Upload Dashboard to Tableau Online > Schedule update for Extract

2) You can send Custom Quota Request to Google and set up limits per project/per user.

3) If each of your query touching 200GB each time, consider to optimize these queries (Don't use SELECT *, use only dates you need, etc)

Answer 3

The best approach I found was to partition the table in BQ based on a date (day) field which has no timestamp. BQ allows you to partition a table by a day level field. The important thing here is that even though the field is day/date with no timestamp it should be a TIMESTAMP datatype in the BQ table. i.e. you will end up with a column in BQ with data looking like this:

2018-01-01 00:00:00.000 UTC

The reasons the field needs to be a TIMESTAMP datatype (even though there is no time in the data) is because when you create a viz in Tableau it will generate SQL to run against BQ and for the partitioned field to be utilised by the Tableau generated SQL it needs to be a TIMESTAMP datatype.

In Tableau, you should always filter on your partitioned field and BQ will only scan the rows within the ranges of the filter.

I tried partitioning on a DATE datatype and looked up the logs in GCP and saw that the entire table was being scanned. Changing to TIMESTAMP fixed this.

Answer 4

The thing about tableau and Big Query is that tableau calculates the filter values using your query ( live query ). What I have seen in my project logging is, it creates filters from your own query.

select 'Custom SQL Query'.filtered_column from ( your_actual_datasource_query ) as 'Custom SQL Query'  group by 'Custom SQL Query'.filtered_column 

Instead, try to create the tableau data source with incremental extracts and also try to have your query date partitioned ( Big Query only supports date partitioning) so that you can limit the data use.