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

Following is the visual walkthrough of the BigQuery User Interface:

#1 In BigQuery we create one or more projects:

#2 Each project is made up of one or more data sets: 

#3 Each data set is made up of one or more tables: 

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

#5 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. 

Here is how the schema editor looks like:

#6 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

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

#8 Use the query editor to enter SQL queries:

#9 We query data from a data table by typing SQL in the ‘Query editor’ and then clicking on the ‘Run’ button.

Needless to say, you need to know SQL in order to use BigQuery.

#10 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. 

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

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

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

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

#15 If you want to enter a new SQL query in the ‘query editor’ then click on the ‘+ COMPOSE NEW QUERY’ button.

This action will delete the existing SQL in the ‘query editor’:

#16 If you want to hide the query editor (so that you can create more room for query results) then click on the ‘HIDE EDITOR’ button:

#17 If you want to increase the size of your query editor (maybe your query is getting quite big) then click on the ‘FULL SCREEN’ button:

#18 You can also manually increase or decrease the size of your query editor by clicking on the handle in the middle and then dragging it upwards or downwards:

#19 If you want to search a particular data set or data table within a project then first click on the project name/ID and then type the name of your data set or table in the search box:

#20 If you can’t find what you are looking for then click on the link ‘Search all projects’:

#21 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’:

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. 

To learn about Google Big Query for Google Analytics, check out this article: Google Analytics Bigquery Tutorial