Learning to use google cloud
Introduction
Is a suite of cloud computing services and products offered. It provides a wide range of infrastructure and platform services that can be used for computing, sotrage, data analytics, and more.
Some of the core services and offerings provided Google cloud include:
- Compute Services: This includes virtual machines (VMs) through Google
Compute Engine, serverless computing with Google Cloud Functions, and managed Kubernetes clusters througCompute Services: This includes virtual machines (VMs) through Google Compute Engine, serverless computing with Google Cloud Functions, and managed Kubernetes clusters throug
2. Storage Services:
Google Cloud offers various storage (Google Cloud Storage, Cloud Bigtable, Cloud Spanner, and Cloud SQL) which are suitable for different types of data
3. Data Analytics and Machine Learning:
Google Cloud provides BigQuery for data warehousing, Cloud Dataprep for data preparation, and a range of machine learning services, including AutoML and TensorFlow.
4. Google Cloud provides managed database services
SQL( MySQL, PostgreSQL, and SQL Server), NOSQL (CLOUD FIRESTORE) and Cloud Bigtable.
5. Deployment Tools:
Google Cloud includes a variety of tools for application development and deployment, such as Cloud Build, Cloud Source Repositories, and App Engine.
6. Storage and Data Transfer:
Google Cloud Storage allows for data storage and transfer, and services like Cloud Data Transfer and Transfer Appliance facilitate data migration to the cloud.
EXAMPLE
1. Login in with your gmail account
2. We look for the right service
3. I will use cloud storage
3.1 customizing cloud storage
success creatión
3.1.2 We can create or upload a folder in the bucket
3.1.3 I will create an example folder
3.1.4 I will upload an example file in the created folder
3.1.5 We will load the data into a drive or manually using dataproc
We check if the dataproc Api is public, so as not to have errors
3.1.6 We will customize the dataproc
select the appropriate region and the type of node to work
Let’s find the right iso for the dataproc
we add components
we configure the nodes
We check if we have jupyterlab configured to program in dataproc
programming in jupyterlab
we will use the notebook
code to implement:
Results:
Conclusions:
Implementing a dataproc using Jupyterlab under cloud storage allows us to integrate our data and provide great benefits to store our data anywhere.