
Before you begin
- Labs create a Google Cloud project and resources for a fixed time
- Labs have a time limit and no pause feature. If you end the lab, you'll have to restart from the beginning.
- On the top left of your screen, click Start lab to begin
Create a Dataset
/ 20
Create a sink
/ 20
Run example queries
/ 30
Viewing the logs in BigQuery
/ 30
Cloud Logging serves as a central repository for logs from various Google Cloud services, including BigQuery, and is ideal for short to mid-term log storage. Many industries require logs to be retained for extended periods. To keep logs for extended historical analysis or complex auditing, you can set up a sink to export specific logs to BigQuery.
In this lab you view the BigQuery logs inside Cloud Logging, set up a sink to export them into BigQuery, and then use SQL to analyze the logs.
Read these instructions. Labs are timed and you cannot pause them. The timer, which starts when you click Start Lab, shows how long Google Cloud resources are made available to you.
This hands-on lab lets you do the lab activities in a real cloud environment, not in a simulation or demo environment. It does so by giving you new, temporary credentials you use to sign in and access Google Cloud for the duration of the lab.
To complete this lab, you need:
Click the Start Lab button. If you need to pay for the lab, a dialog opens for you to select your payment method. On the left is the Lab Details pane with the following:
Click Open Google Cloud console (or right-click and select Open Link in Incognito Window if you are running the Chrome browser).
The lab spins up resources, and then opens another tab that shows the Sign in page.
Tip: Arrange the tabs in separate windows, side-by-side.
If necessary, copy the Username below and paste it into the Sign in dialog.
You can also find the Username in the Lab Details pane.
Click Next.
Copy the Password below and paste it into the Welcome dialog.
You can also find the Password in the Lab Details pane.
Click Next.
Click through the subsequent pages:
After a few moments, the Google Cloud console opens in this tab.
Cloud Shell is a virtual machine that is loaded with development tools. It offers a persistent 5GB home directory and runs on the Google Cloud. Cloud Shell provides command-line access to your Google Cloud resources.
Click Activate Cloud Shell at the top of the Google Cloud console.
Click through the following windows:
When you are connected, you are already authenticated, and the project is set to your Project_ID,
gcloud
is the command-line tool for Google Cloud. It comes pre-installed on Cloud Shell and supports tab-completion.
Output:
Output:
gcloud
, in Google Cloud, refer to the gcloud CLI overview guide.
The Welcome to BigQuery in the Cloud Console message box opens. This message box provides a link to the quickstart guide and the release notes.
The BigQuery console opens.
Under the Explorer section, click on the three dots next to the project that starts with qwiklabs-gcp-
.
Click Create dataset.
Set Dataset ID to bq_logs.
Click Create Dataset.
Click Check my progress to verify the objective.
First, run a simple query which generates a log. Later you will use this log to set up the log export from to BigQuery.
In All resources, select BigQuery, then click Apply.
Now, click Run query button in the top right.
A few log entries from the query should appear.
Look for the entry that contains the word "jobcompleted".
jobservice.jobcompleted
in the logs.This sets up the search with the correct terms. You may need to toggle the Show Query button to see it.
Now that you have the logs you need, time to set up a sink.
Any subsequent log entries from BigQuery are now exported to a table in the bq_logs dataset.
Click Check my progress to verify the objective.
To populate your new table with some logs, run some example queries.
You should see the results of each query returned.
Click Check my progress to verify the objective.
Navigate back to BigQuery (Navigation menu > BigQuery).
Expand your resource starting with the name qwiklabs-gcp- and expand your dataset bq_logs.
The name may vary, but you should see a "cloudaudit_googleapis_com_data_access" table.
If you clicked Preview and wondered why it doesn't show the logs for the recently run queries, its because the logs are streamed into the table, which means that the new data can be queried but won't show up in Preview for a little while.
To make the table more usable, create a VIEW, which pulls out subset of fields and also performs some calculations to derive a metric for query time.
Click Check my progress to verify the objective.
You successfully exported BigQuery logs from Cloud Logging to a BigQuery table, then analyzed them with SQL.
...helps you make the most of Google Cloud technologies. Our classes include technical skills and best practices to help you get up to speed quickly and continue your learning journey. We offer fundamental to advanced level training, with on-demand, live, and virtual options to suit your busy schedule. Certifications help you validate and prove your skill and expertise in Google Cloud technologies.
Manual Last Updated: January 23, 2025
Lab Last Tested: January 23, 2025
Copyright 2025 Google LLC All rights reserved. Google and the Google logo are trademarks of Google LLC. All other company and product names may be trademarks of the respective companies with which they are associated.
Ce contenu n'est pas disponible pour le moment
Nous vous préviendrons par e-mail lorsqu'il sera disponible
Parfait !
Nous vous contacterons par e-mail s'il devient disponible
One lab at a time
Confirm to end all existing labs and start this one