Getting Started with Google Cloud Platform (GCP)

Author: neptune | 23rd-Apr-2022 | views: 965

Introduction to Google Cloud

What is cloud computing?

Cloud computing has five fundamental attributes, according to the definition of cloud computing proposed by the United States National Institute of Standards and Technology.

Five Fundamental attributes:-

  • On-demand self-service

  • Broad Network Access

  • Resource Pooling

  • Rapid Elasticity

  • Measured Service

First, customers get computing resources on-demand and self-service. 

Second, they can access these resources over the network. 

Third, the provider of those resources has a big pool of them and allocates them to customers out of the pool.

Fourth, the resources are elastic. Customers who need more resources can get more rapidly. When they need less, they can scale back.

And last, the customers pay only for what they use or reserve, as they go. If they stop using resources, they stop paying.

GCP computing architectures are:-

  • Compute Engine

  • Kubernetes Engine

  • App Engine

  • Cloud Functions

  • Managed services

GCP is organized into Regions and Zones

Regions are independent geographic areas that consist of zones. Locations within regions tend to have round-trip network latencies of under 5 milliseconds on the 95th percentile.

The following services have one or more multi-regional deployments in addition to any regional deployments:

  • Google App Engine and its features 

  • Google Cloud Datastore 

  • Google Cloud Storage 

  • Google BigQuery

Google offers a customer-friendly pricing

When it comes to cost, GCP offers many options to its customers. It offers a per-second billing.

  • Billing in sub-hour increments

  • Discounts for sustained use

  • Discounts for committed use

  • Discounts for preemptible use

  • Custom VM instance types

GCP products and services can be broadly categorized into 6 categories. 

  1. Compute 

  2. Storage

  3. Big Data 

  4. Machine Learning

  5. Networking

  6. Operations/Tools

In this course, we will explore the first four compute services - Compute, Storage, Big Data, and Machine Learning.

Compute services offered by GCP.

  • Compute Engine

  • Kubernetes Engine

  • App Engine

  • Cloud Functions

These services all have pretty cool names, but you get confused with regards to their functions and what makes them unique from each other.

Later we will explore more about these services.

Storage services offered by GCP.

  • Bigtable

  • Cloud Storage

  • Cloud SQL

  • Cloud Spanner

  • Cloud Datastore

GCP offers developers and IT organizations durable and highly available object storage. You can assess with no minimum fee. You only pay for what you use.

Well, you might be familiar with the file storage you manage data as a hierarchy of folders. It’s not the same as block storage, it is completely different from a simple file storage system.

What is Object Storage?

Object storage means you tell your storage, “Keep this arbitrary sequence of bytes here,” and the storage lets you address it with a unique key. In GCP and other Object storage, these unique keys are in the form of URLs, which means object storage interacts well with web technologies.

The storage objects offered by Google Cloud Storage are “immutable” which means you can’t edit them, but you can create a new version.

Data services offered by GCP.

  1. Big Data

  2. Machine Learning

1. Big Data services offered by GCP.

  • BigQuery

  • Pub/Sub

  • Dataflow

  • Dataproc

  • Datalab

Big data systems store and process massive amounts of data. It provides unlimited data storage options for big data processing and analysis.

BigQuery is widely used for Big Data processing. It has an SQL-compatible engine that can perform analysis on very large data volumes in seconds.

2. Machine Learning services offered by GCP.

  • Natural Language API

  • Vision API

  • Machine Learning

  • Speech API

  • Translate API

Google has pledged to make this Artificial Intelligence domain knowledge accessible. AI Platform is designed to make it easy for data scientists and engineers to streamline ML workflows and access AI developed by Google.

For example, advanced models built using Tensorflow, and SKLearn.

Hope you get something from this course.

Thanks for Reading !!!

anonymous | March 1, 2022, 1:28 a.m.

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