Google BigQuery

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Google BigQuery

1. Before you start
To use Google BigQuery with Exploratory Desktop, you need to create a project on Google Cloud Platform and a dataset on Google BigQuery.
Create a project for Google BigQuery
Open Google Cloud Platform Console​
Click "Create Project" menu at the right hand side top
Select a Project name and click "Create" button
Make sure that you enable BigQuery API for you Project by clicking "Enable and manager APIs " menu under Use Google APIs section
Create a dataset on Google BigQuery
Open Google BigQuery Web ui​
Click down arrow icon next to your project name and select "Create new dataset" menu.
Enter Dataset id and Click "OK" button
2. Select Google BigQuery Data Menu
Select 'Import Database Data' from Add Data Frames dropdown
Click 'Google BigQuery'
3. Authentication with Google OAuth
Select an account you want to use for your Google BigQuery and click 'Allow' button to allow Exploratory to extract your Google BigQuery data based on the parameters you are going to set up in the next step.
4. Write SQL Query
4.1 Preview Data
Type Data Frame Name
Select Google BigQuery Project from the dropdown menu
Enter query to SQL Query editor
Click Run button to preview data.
5. Standard SQL
You can now use Standard SQL by clicking the "Standard SQL Mode" checkbox on Google BigQuery Configuration Dialog.
Click the Edit button to open the Google BigQuery Configuration Dialog. 
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On the Google BigQuery Configuration Dialog, you can set the Standard SQL Mode.
BigQuery standard SQL is compliant with the SQL 2011 standard and has extensions that support querying nested and repeated data.
Standard SQL has several advantages over legacy SQL, including:
Composability using WITH clauses and SQL functions
Subqueries in the SELECT list and WHERE clause
Correlated subqueries
ARRAY and STRUCT data types
COUNT(DISTINCT <expr>) is exact and scalable, providing the accuracy of EXACT_COUNT_DISTINCT without its limitations
Automatic predicate push-down through JOINs
Complex JOIN predicates, including arbitrary expressions
5.1 Composability using WITH clauses and SQL functions
Now you can use WITH clause which enables extraction or reuse of named subqueries. For example:
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WITH SUBQ AS (
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  SELECT score FROM UNNEST([50, 60, 40, 50]) AS score
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)
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SELECT score / (SELECT SUM(score) FROM SUBQ) AS weighted_score
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FROM SUBQ;
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For Migration from legacy SQL, Please refer Migrating to Standard SQL​
6. Page Size
When importing data from Google BigQuery, if you specify a lot of columns in your SQL query, the query may return missing results. If this is the case, you might want to reduce the page size.
Also, when your query result contains many list columns, you also want to reduce page size so that you can import the query result.
Please note that decreasing the page size slows the importing data process.
You can override the Page Size at the Import Dialog too.
7. Using Parameters in SQL
First, click Parameter link on the SQL Data Import Dialog.
Second, define a parameter and click Save button.
Finally, you can use @{} to surround a variable name inside the query like below.
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select *
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from airline_2016_01
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where carrier = @{carrier}
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If you type @ then it suggests parameters like below.
Here's a blog post for more detail.
8. Import
Click 'Import' button
If the data in the preview table look ok, then click 'Import' button to import the data into Exploratory.
9. Switch Billing Project
When you want to switch your Billing Project, you can click select a Billing Project on Google BigQuery Configuration dialog.