Google BigQuery New UI Features

Nilesh Khandalkar
4 min readJan 27, 2021

Google Cloud has rolled out some new features for the BigQuery UI. There are layout changes, enhancements to existing features, and all together new features that should help change the way we use BigQuery UI.

First to get to the new UI go to the BigQuery console and select ‘SHOW PREVIEW FEATURES’ in the top navigation bar. Once in the new UI you will see a hide version to take you back so do not worry about being stuck in the new UI forever as you can always go back to the older version.

Older Version:

Older Version

Current Version:

Current Version

New Features:

1.

The BigQuery contextual menu is now split out into its own collapsable menu. Because it is able to collapse it allows for the data set explorer to be cleaner and easier to navigate.

2.

The Job History, Query History, and Saved Queries now have also moved to the bottom of the window in their own collapsable menu. The positioning is useful in the context of the whole UI change.

From the Query History pane, select a query, and click Open query in editor. This is great to see, as in earlier version this would overwrite your SQL when there was just one pane. Now that’s not the case as it open in a new editor window.

From the Saved queries pane, click Open query in editor. Again, great to see this doesn’t overwrite your current SQL window.

With the change to the layout there is no longer a section for viewing dataset and table information. Previously this would be at the bottom of the window when you have clicked on one of those objects. Instead now they open up as their own editor window. This should allow us to see information about those objects easier instead of being confined to the smaller window that it previously had.

3.

Next is the biggest change to quality of life in the tool. MULTIPLE EDITOR WINDOWS! If you have used BigQuery in the past you have likely added a Chrome plugin or had multiple windows open to give you a semblance of this feature. Not anymore, now you can have multiple editor windows open without having to consume additional resources on your local machine to support more tabs or plugins. Jumping around those tabs is so far much easier. To enable all you have to do is hit the ‘COMPOSE NEW QUERY’ button and it will add more editors as additional tabs.

You can expand/collapse controls to hide the navigation menu by clicking at the bottom left <| Hide BigQuery navigation menu.

4.

Now with the multiple editor windows comes another great feature. The ability to have split editor windows. Simply click the drop down in the editor you want to split.

Split Editor windows

This allows to compare SQL queries, results, and execution details of two queries at the same time. So, if you’re trying to see how a change in a query impacts results or performance, we can easily do that comparison.

Since we can split them out, we can have an editor and schema definition side by side. This allows us to have a more complete view of the schema of a table.

5.

Another change to the UI is being able to highlight and execute portions of queries within an editor. This is a subtle change from previous behavior where you had to click the submenu for run, then select run selected. Now we can highlight and run via the ‘RUN’ button or shortcut command.

6.

Finally, a change is with the auto-complete functionality. Previously you would have to start typing and then hit tab to trigger suggestions for completion. Now as you type keywords and table names suggestions begin to populate immediately.

Also now writing SQL a lot quicker, especially if you are not too familiar with the table structures. We just have to start typing the name of the dataset, and in real-time, we see datasets (and databases) that match. Pressing tab completes the dataset, a big relief in writing the table name which includes projectid.datasetname.tablename

Conclusion

New tabs are a much-welcomed addition, and the new IntelliSense that can list functions will save time to remember commonly functions or the long tables names where we have to type projectid.datasetid.tablename.

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Nilesh Khandalkar

Passionate about Data and Cloud, working as Data Engineering Manager at Capgemini UK. GCP Professional Data Engineering Certified Airflow Fundamentals Certified