Setting Up Google Cloud, Cloud SQL and App Engine to Build a Flask API

Flask makes it possible for developers to build an API for whatever use case they might have. In this tutorial, we’ll learn how to set up Google Cloud, Cloud SQL, and App Engine to build a Flask API. (Cloud SQL is a fully managed platform-as-a-service (PaaS) database engine, and App Engine is a fully managed PaaS for hosting applications.)

A few Python frameworks can be used to create APIs, two of which are Flask and Django. Frameworks comes with functionality that makes it easy for developers to implement the features that users need to interact with their applications. The complexity of a web application could be a deciding factor when you’re choosing which framework to work with.

Django

Django is a robust framework that has a predefined structure with built-in functionality. The downside of its robustness, however, is that it could make the framework too complex for certain projects. It’s best suited to complex web applications that need to leverage the advanced functionality of Django.

Flask

Flask, on the other hand, is a lightweight framework for building APIs. Getting started with it is easy, and packages are available to make it robust as you go. This article will focus on defining the view functions and controller and on connecting to a database on Google Cloud and deploying to Google Cloud.

For the purpose of learning, we’ll build a Flask API with a few endpoints to manage a collection of our favorite songs. The endpoints will be for GET and POST requests: fetching and creating resources. Alongside that, we will be using the suite of services on the Google Cloud platform. We’ll set up Google’s Cloud SQL for our database and launch our app by deploying to App Engine. This tutorial is aimed at beginners who are taking their first stab at using Google Cloud for their app.

Setting Up A Flask Project

This tutorial assumes you have Python 3.x installed. If you don’t, head over to the official website to download and install it.

To check whether Python is installed, launch your command-line interface (CLI) and run the command below:

python -V

Our first step is to create the directory where our project will live. We will call it flask-app:

mkdir flask-app && cd flask-app

The first thing to do when starting a Python project is to create a virtual environment. Virtual environments isolate your working Python development. This means that this project can have its own dependencies, different from other project on your machines. venv is a module that ships with Python 3.

#flask #cloud #sql #api #developer

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Introduction to Structured Query Language SQL pdf

SQL stands for Structured Query Language. SQL is a scripting language expected to store, control, and inquiry information put away in social databases. The main manifestation of SQL showed up in 1974, when a gathering in IBM built up the principal model of a social database. The primary business social database was discharged by Relational Software later turning out to be Oracle.

Models for SQL exist. In any case, the SQL that can be utilized on every last one of the major RDBMS today is in various flavors. This is because of two reasons:

1. The SQL order standard is genuinely intricate, and it isn’t handy to actualize the whole standard.

2. Every database seller needs an approach to separate its item from others.

Right now, contrasts are noted where fitting.

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What Are Google Compute Engine ? - Explained

What Are Google Compute Engine ? - Explained

The Google computer engine exchanges a large number of scalable virtual machines to serve as clusters used for that purpose. GCE can be managed through a RESTful API, command line interface, or web console. The computing engine is serviced for a minimum of 10-minutes per use. There is no up or front fee or time commitment. GCE competes with Amazon’s Elastic Compute Cloud (EC2) and Microsoft Azure.

https://www.mrdeluofficial.com/2020/08/what-are-google-compute-engine-explained.html

#google compute engine #google compute engine tutorial #google app engine #google cloud console #google cloud storage #google compute engine documentation

Setting Up Google Cloud, Cloud SQL and App Engine to Build a Flask API

Flask makes it possible for developers to build an API for whatever use case they might have. In this tutorial, we’ll learn how to set up Google Cloud, Cloud SQL, and App Engine to build a Flask API. (Cloud SQL is a fully managed platform-as-a-service (PaaS) database engine, and App Engine is a fully managed PaaS for hosting applications.)

A few Python frameworks can be used to create APIs, two of which are Flask and Django. Frameworks comes with functionality that makes it easy for developers to implement the features that users need to interact with their applications. The complexity of a web application could be a deciding factor when you’re choosing which framework to work with.

Django

Django is a robust framework that has a predefined structure with built-in functionality. The downside of its robustness, however, is that it could make the framework too complex for certain projects. It’s best suited to complex web applications that need to leverage the advanced functionality of Django.

Flask

Flask, on the other hand, is a lightweight framework for building APIs. Getting started with it is easy, and packages are available to make it robust as you go. This article will focus on defining the view functions and controller and on connecting to a database on Google Cloud and deploying to Google Cloud.

For the purpose of learning, we’ll build a Flask API with a few endpoints to manage a collection of our favorite songs. The endpoints will be for GET and POST requests: fetching and creating resources. Alongside that, we will be using the suite of services on the Google Cloud platform. We’ll set up Google’s Cloud SQL for our database and launch our app by deploying to App Engine. This tutorial is aimed at beginners who are taking their first stab at using Google Cloud for their app.

Setting Up A Flask Project

This tutorial assumes you have Python 3.x installed. If you don’t, head over to the official website to download and install it.

To check whether Python is installed, launch your command-line interface (CLI) and run the command below:

python -V

Our first step is to create the directory where our project will live. We will call it flask-app:

mkdir flask-app && cd flask-app

The first thing to do when starting a Python project is to create a virtual environment. Virtual environments isolate your working Python development. This means that this project can have its own dependencies, different from other project on your machines. venv is a module that ships with Python 3.

#flask #cloud #sql #api #developer

Multi-cloud Spending: 8 Tips To Lower Cost

A multi-cloud approach is nothing but leveraging two or more cloud platforms for meeting the various business requirements of an enterprise. The multi-cloud IT environment incorporates different clouds from multiple vendors and negates the dependence on a single public cloud service provider. Thus enterprises can choose specific services from multiple public clouds and reap the benefits of each.

Given its affordability and agility, most enterprises opt for a multi-cloud approach in cloud computing now. A 2018 survey on the public cloud services market points out that 81% of the respondents use services from two or more providers. Subsequently, the cloud computing services market has reported incredible growth in recent times. The worldwide public cloud services market is all set to reach $500 billion in the next four years, according to IDC.

By choosing multi-cloud solutions strategically, enterprises can optimize the benefits of cloud computing and aim for some key competitive advantages. They can avoid the lengthy and cumbersome processes involved in buying, installing and testing high-priced systems. The IaaS and PaaS solutions have become a windfall for the enterprise’s budget as it does not incur huge up-front capital expenditure.

However, cost optimization is still a challenge while facilitating a multi-cloud environment and a large number of enterprises end up overpaying with or without realizing it. The below-mentioned tips would help you ensure the money is spent wisely on cloud computing services.

• Deactivate underused or unattached resources

Most organizations tend to get wrong with simple things which turn out to be the root cause for needless spending and resource wastage. The first step to cost optimization in your cloud strategy is to identify underutilized resources that you have been paying for.

Enterprises often continue to pay for resources that have been purchased earlier but are no longer useful. Identifying such unused and unattached resources and deactivating it on a regular basis brings you one step closer to cost optimization. If needed, you can deploy automated cloud management tools that are largely helpful in providing the analytics needed to optimize the cloud spending and cut costs on an ongoing basis.

• Figure out idle instances

Another key cost optimization strategy is to identify the idle computing instances and consolidate them into fewer instances. An idle computing instance may require a CPU utilization level of 1-5%, but you may be billed by the service provider for 100% for the same instance.

Every enterprise will have such non-production instances that constitute unnecessary storage space and lead to overpaying. Re-evaluating your resource allocations regularly and removing unnecessary storage may help you save money significantly. Resource allocation is not only a matter of CPU and memory but also it is linked to the storage, network, and various other factors.

• Deploy monitoring mechanisms

The key to efficient cost reduction in cloud computing technology lies in proactive monitoring. A comprehensive view of the cloud usage helps enterprises to monitor and minimize unnecessary spending. You can make use of various mechanisms for monitoring computing demand.

For instance, you can use a heatmap to understand the highs and lows in computing visually. This heat map indicates the start and stop times which in turn lead to reduced costs. You can also deploy automated tools that help organizations to schedule instances to start and stop. By following a heatmap, you can understand whether it is safe to shut down servers on holidays or weekends.

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Overview of Google Cloud Essentials Quest

If you looking to learn about Google Cloud in depth or in general with or without any prior knowledge in cloud computing, then you should definitely check this quest out, Link.

Google Could Essentials is an introductory level Quest which is useful to learn about the basic fundamentals of Google Cloud. From writing Cloud Shell commands and deploying my first virtual machine, to running applications on Kubernetes Engine or with load balancing, Google Cloud Essentials is a prime introduction to the platform’s basic features.

Let’s see what was the Quest Outline:

1. A Tour of Qwiklabs and Google Cloud
2. Creating a Virtual Machine
3. Getting Started with Cloud Shell & gcloud
4. Kubernetes Engine: Qwik Start
5. Set Up Network and HTTP Load Balancers

A Tour of Qwiklabs and Google Cloud was the first hands-on lab which basically gives an overview about Google Cloud. There were few questions to answers that will check your understanding about the topic and the rest was about accessing Google cloud console, projects in cloud console, roles and permissions, Cloud Shell and so on.

**Creating a Virtual Machine **was the second lab to create virtual machine and also connect NGINX web server to it. Compute Engine lets one create virtual machine whose resources live in certain regions or zones. NGINX web server is used as load balancer. The job of a load balancer is to distribute workloads across multiple computing resources. Creating these two along with a question would mark the end of the second lab.

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