Google had rolled out a new user interface for BigQuery in July of 2018, that made it easier to discover insights and share them with others.
Today, the company is introducing another round of updates to the UI.
Google terms them as “major” updates to the UI that were baked in the last five months.
Part of these improvements, some functionalities are ported from the classic UI, while other features are all new.
For those new, “BigQuery exposes a graphical web UI in the Cloud Console that allows to create and manage BigQuery resources and to run SQL queries.”
Google designed several new functionalities that make collaboration a breeze. These additions include:
ability to share queries by simply clicking on the “Link Sharing” button located above the editor and then “turn on” link sharing for others see the query.
Once shared, any updates made to the query will be shared as well, so it’s no more require to “paste new versions into an email.”
Here is an image showing the BigQuery sharing feature:
Users can now add metadata to their BigQuery resources, like add or edit descriptions to datasets or tables.
Metadata can be edited by clicking on the “pencil” icons on the “Details” pages for a dataset or table.
Google also now allows adding custom labels to be used as keywords to search datasets and tables.
Custom labels can consist any keys and values. In the image below you can see this feature:
Editing of individual column descriptions through the interface is now available too.
Just navigate to the Schema view for a table, and click “Edit Schema” icon. This will allow editing descriptions of existing fields or add new.
Public Datasets in BigQuery
Analysts are now allowed to include datasets from Google Cloud Public Dataset Program (GCPD) in their BigQuery queries, so they can discover own insights or join them [GCPD) with own data.
GCPD provides access to data from over 100 valuable sources at BigQuery’s standard analysis pricing.
Navigate to Resources section and select “Add Data” option, then choose “Explore public datasets” to browse the marketplace for the dataset. Then, select “View Dataset” to see and query it in BigQuery.
Here is an image representation of the above steps:
New Sorting and Filtering Queries
Google also has added highly-requested features, “sorting” and “filtering” personal and project query history.
You’ve told us that it can be hard to find a specific query of interest in a lengthy query history. As such, sorting and filtering your personal and project query history have been highly-requested features.
Users are now allowed to sort their queries on the date, duration, duration/MB, input bytes, slot time, or slot time/MB.
While, filter support queries text, bytes processed, job ID, job status, user email, and the start and end time.
For creating more complex searches users can combine filtering conditions logically.
Here is an image showing sorting and filtering queries:
Beyond these updates, a number of improvements to performance, security, and reliability are also now available, too including:
- The results view and table previews for a table with columns now load 5-10 times faster when first viewed.
- Secure tables can now be created using the UI with user-owned “managed encryption keys.”
- A variety of small visual improvements including better text-wrapping and getting-started messages for anyone who hasn’t run queries or added datasets yet.