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Introduction
While working on the cloud, we have to transform, analyze, and visualize the data, so for that, what to do?
For that, we have a datalab cloud. So in this article, we will learn to set up a datalab with cloud monitoring.
Let's dive into the topic to learn more about it.
Cloud Datalab
The interactive data analysis and machine learning environment known as Cloud Datalab was created for the Google Cloud Platform. It enables interactive data exploration, analysis, transformation, and visualization, as well as the creation of machine learning models using the data.
Setting up Datalab to use with Cloud Monitoring.
In this, you'll learn how to configure Datalab for use with your projects for cloud monitoring. You can run ad hoc analyses and visualizations with Datalab's dynamic notebooks that go beyond what Monitoring currently offers.
Setting up Datalab
From a terminal window on your local system, carry out the following actions:
1.Obtain the latest gcloud command:
$ gcloud components update
2. The gcloud datalab component should be installed:
$ gcloud components install datalab
3. Run the following command after changing [PROJECT ID] with your Google Cloud project ID to set up gcloud to connect to your Google Cloud project ID:
$ gcloud config set project [PROJECT_ID]
Run the upcoming command to confirm the configuration:
$ gcloud config get-value project
4. Run the following command to create a Datalab instance, substituting [DATALAB-INSTANCE-NAME] with the name of your instance. All names must start with a lowercase letter, have no more than 62 lowercase letters, numerals, or hyphens, and not include a hyphen at the conclusion.
$ datalab create [DATALAB-INSTANCE-NAME]
5. While the datalab command is in use, the connection to your Datalab instance is still active. The connection is cut off if the terminal command window is closed or stopped. Run the next command to restart the connection:
$ datalab connect [DATALAB-INSTANCE-NAME]
The Datalab Interface
The URL for the newly opened browser window when you run the datalab connect command is http://127.0.0.1:8081/.
You can explore several Datalab getting started notebooks, including notebooks for cloud monitoring, in the Datalab docs/ subdirectory.
Monitoring tutorials
There are various interactive Monitoring lessons installed with Datalab:
Getting Started:
Shows how to integrate the Python Google Cloud's operations suite API into Datalab and specify your default Google Cloud project ID. The API is called using sample code, and monitoring data from your project is obtained.
Group metrics
Shows how to analyze a project's group structure and use groups to aggregate and filter metric data. You must set up a Cloud Monitoring group in order to use group metrics. To learn more, go to Using resource groups.
Time-Shifted data
Demonstrates some intriguing techniques to modify time series data. The tutorial is set up to potentially use previously-extracted data from a demonstration project because your project might not have enough VM instances to be a decent example.
Running the tutorials
Follow these steps to run the tutorials:
In the Datalab interface, select Docs, Tutorials, and Stackdriver Monitoring.
Choosing the tutorial. Select the tutorial by clicking its name.
Set the project ID to Your Google Cloud project ID should be substituted for my-project-id in the column that contains the set datalab project id('my-project-id').
Start the tutorial program. Select Run > Run all cells from the menu bar at the top of the screen. This executes all of the tutorial's code again using your current project ID.
Cleaning up
Please follow these instructions to prevent your Google Cloud account from being charged for the resources used on this page.
Close any open notebooks by clicking the Running Sessions button in the top-right corner of the Datalab interface. If necessary, you can restart them later. Close any related browser windows or tabs.
Close the Datalab tab in your browser by pressing CTRL-C in the Datalab window.
You are responsible for paying charges from the moment the Datalab VM instance is created until it is deleted. After the Datalab VM has been deleted, the Persistent Disk continues to exist until you destroy it.
The datalab create command generates extra resources that can be used by other Datalab instances you create.
Delete the firewall rule that permits SSH access to your Datalab instances, datalab-network-allow-ssh:
Delete the default connection made by Datalab instances to the datalab-network Virtual Private Cloud (VPC) network:
$ gcloud compute networks delete datalab-network
Delete the notebook storage repository you created, datalab-notebooks Cloud Source Repository.
$ gcloud source repos delete datalab-notebooks
5. Delete any projects or VM instances you don't wish to maintain from your creations.
Frequently asked questions
What is called a cloud?
The term "the cloud" describes the software and databases that run on servers that may be accessed via the Internet.
What is the Cloud Controls Matrix?
The Cloud Controls Matrix (CCM) from the Cloud Security Alliance is a tool created expressly to help potential cloud customers and vendors analyze a cloud provider's overall security risk.
What is AWS?
AWS (Amazon Web Service) is an online platform that offers scalable and affordable cloud computing solutions.
What is troubleshooting?
A systematic method of problem-solving known as troubleshooting is frequently used to identify and resolve problems with sophisticated machinery, electronics, computers, and software systems.
For what Compute Engines are used?
The Google infrastructure can be used to build and run virtual machines using Compute Engine, a customized compute service.
Conclusion
This blog has extensively discussed the Concept of Cloud datalab. We hope this blog has helped you in enhancing your knowledge about setting up the cloud datalab. If you want to learn more, check out the excellent content on the Coding Ninjas Website:
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