Understanding the BigQuery User Interface

Google BigQuery is one of the products of Google Cloud Platform.

BigQuery is a data storage and management system which is used to bring data from several data sources for the purpose of reporting and analysis.

In order to access BigQuery, navigate to https://console.cloud.google.com/bigquery

 
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Following is the visual walkthrough of the BigQuery User Interface:

#1 In BigQuery we create/add one or more projects:

#2 When you click on a project ID, a new tab is opened:

#3 Each project is made up of one or more data sets which you can see by clicking on the drop-down menu:

#4 When you click on a data set name, a new tab is opened:

#5 Each data set is made up of one or more tables which you can see by clicking on the drop-down menu:

It’s the data tables (and not the projects or data sets) that contains the actual data.

#6 When you click on a data table name, a new tab is opened:

#7 Each data table has got ‘Schema’, ‘Details’ and ‘Preview’ tabs:

#8 Schema is the structure of your data table. 

It shows you how the table has been set up. What type of values it accepts. You can also read a short description of each table field:

If you want to edit the schema then click on the ‘Edit schema’ button. 

#9 Here is what the schema editor looks like:

#10 Through the ‘Details’ tab you can get the following information about your data table:

  • Table ID
  • Table size
  • Number of rows in the table
  • Date and time when the table was created
  • Table expiration date
  • Last modified date and time
  • Data location

#11 Through the preview tab you can preview your table without running a single query:

#12 Click on the ‘QUERY TABLE’ button to create and edit a new SQL query:

You should now see a new SQL editor (pre-populated with SQL) opened in a new tab (named EDITOR-2) :

However, this SQL is not complete as it is missing the name of the columns that should be retrieved. 

And that’s why you see the syntax error:

Let’s retrieve all the columns of the table by using the SELECT * statement.

The correct SQL may look like the one below:

#13 In order to make your queries more readable and easy to understand, format them by clicking on the ‘Format Query’ option under the ‘MORE’ drop-down menu:

#14 Here is how the formatted query would look like:

#15 When you type a query in the ‘query editor’, BigQuery tells you in advance how much data your query will process:

In BigQuery, you are charged on the basis of the amount of data your query processes.

So if your query is going to process gigabytes or terabytes of data then it would quickly increase your query cost. So look at this notification every time before running a query.

#16 Click on the ‘Run’ button to execute your query:

#17 When you run a query, you would see the query results directly below the ‘query editor’:

#18 There is a navigation bar at the bottom right-hand side of your query results data table to see more records/rows:

#19 To save your query click on the ‘Save Query’ option from the ‘SAVE’ drop-down menu:

Name your query and then click on the ‘Save’ button:

Your current tab should now rename to ‘My First Query’:

#20 If you want to switch to the current data table then click on the tab corresponding to the data table:

#21 If you want to switch to the current data set then click on the tab corresponding to the data set:

#22 If you want to switch to the current BigQuery project then click on the tab corresponding to the project:

#23 If you want to compose a new query then click on the ‘Editor’ tab:

Here you can enter a new query and then execute it by clicking on the ‘Run’ button.

#24 If you want to switch back to the SQL query you saved earlier then click on the tab corresponding to the saved query:

#25 Click on the ‘SAVE RESULTS’ button to save your query result as a CSV file, JSON file, BigQuery table or Google Sheets document:

#26 If you want to visualize your query results in Google Data Studio then click on the ‘EXPLORE DATA’ button:

#27 If you want to compose a new SQL query then click on the ‘+ COMPOSE NEW QUERY’ button:

You should now see a new SQL editor opened in a new tab (named EDITOR-2):

If you want to close this new tab then click on the > button next to it:

Now click on the cross button:

#28 You can manually increase or decrease the size of your query editor by clicking & dragging the bottom/side windows:

#29 If you want to search a particular data set or data table within a project then follow the steps below:

Step-1: Click on the project name/ID which contains the data set / data table you want to explore:

Step-2: Type the name of your data set or table in the search box:

Step-3: Press the enter key. 

You should now be able to see your desired data table / data set (provided it already exist):

#30 If you can’t find what you are looking for then click on the link ‘Broaden search to all projects’:

#31 BigQuery provides a lot of public data sets which you can use for practice purposes. 

To find such data sets click on the ‘+ADD DATA’ drop-down menu and then click on ‘Explore Public Datasets’:

#32 To see the list of available keyboard shortcuts click on the ‘SHORTCUT’ button:

#33 If you want to switch back to the old user interface of BigQuery then click on the ‘HIDE PREVIEW FEATURES’ button:

#34 Here is what the old user interface of BigQuery looks like:

#35 If you want to switch back to the new user interface of BigQuery then click on the ‘SHOW PREVIEW FEATURES’ BUTTON:

#36 Click on the hamburger button at the top left-hand side of your screen to navigate to the home page of your Google cloud console account, to access billing, support and other Google Cloud products:

#37 Hover your mouse over the magnifying glass on the top left-hand side of your screen to access the BigQuery menu:

#38 Most of the time, you will find yourself working in the ‘SQL workspace’ section of BigQuery:

There are some other bits and bobs which you can easily discover yourself while using BigQuery. 

But that’s all there is really regarding using the BigQuery user interface. 

Other articles on Google Analytics BigQuery

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  2. Cost of using BigQuery for Google Analytics
  3. Guide to BigQuery Cost optimization
  4. What is Google BigQuery Sandbox and how to use it
  5. Sending data from Google Analytics to BigQuery without 360
  6.  How to connect GA4 (Google Analytics 4) with BigQuery
  7. events_& events_intraday_ tables in BigQuery for GA4 (Google Analytics 4)
  8. Using Google Cloud pricing calculator for BigQuery
  9. How to access BigQuery Public Data Sets
  10. How to use Google Analytics sample dataset for BigQuery
  11. Connect and transfer data from Google Sheets to BigQuery
  12. How to query Google Analytics data in BigQuery
  13. How to send data from Google Ads to BigQuery
  14. What is BigQuery Data Transfer Service & how it works.
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  16. How to send data from Google Search Console to BigQuery
  17. How to pull custom data from Google Analytics to BigQuery
  18. Best Supermetrics Alternative – Dataddo
  19. Google Analytics BigQuery Tutorial
  20. How to backfill Google Analytics data in BigQuery

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