Explain the features of Amazon App Runner

In this recipe, we will learn about Amazon App Runner. We will also learn about the features of Amazon App Runner.

Recipe Objective - Explain the features of Amazon App Runner?

The Amazon App Runner is a widely used service and is defined as a fully managed service that allows developers to easily create containerized web apps and APIs at scale while requiring no prior infrastructure knowledge. Starting with the user's source code or a container image is a good place to start. Amazon App Runner automatically creates and deploys the web application, encrypts traffic for load balancing, is scalable to meet users' traffic demands, and makes it simple for their services to interface with other AWS services and applications running in a private Amazon VPC. Instead of worrying about servers or scaling, users can concentrate on your apps using App Runner. AWS App Runner is also defined as a cloud-based managed container solution. Web apps and APIs are the most common use cases. AWS, like its counterparts DigitalOcean App Platform, Heroku, and Google Cloud Run, doesn't want users to worry about scaling or infrastructure when they use their service. Amazon App Runner executes user's containers behind the scenes using Amazon ECS Cluster and Fargate. Also, Amazon App Runner has two ways of operation. AWS downloads code from GitHub and builds the application on every modification in build mode. It deploys Docker-compatible images from public or private AWS ECR registries in container mode.

Top Reasons to Learn AWS Basics from Scratch for Beginners

Benefits of Amazon App Runner

  • The Amazon App Runner enables the designing and executing of secure web-scale apps in just a few clicks and users don't need any prior container or infrastructure knowledge. No prior understanding of server configuration, networking, load balancing, or deployment pipelines is required and thus it is easy to use. Amazon App Runner enables running user's apps at a web-scale with high availability simple and cost-effective. To avoid cold starts and maintain persistent low latency, App Runner effortlessly scales up resources in response to user's traffic and automatically scales down to their chosen number of provided container instances thus it scales with the traffic. AWS manages App Runner's resources and infrastructure components, ensuring that they follow the security and operational best practices. This allows users to keep focused on their application while meeting their infrastructure and regulatory obligations and thus saves time. With App Runner's Amazon VPC integration, users can quickly connect to the AWS database, cache, and message queue services to support their App Runner apps. There are no public subnets required, which helps users safeguard their VPC's resources and thus ensures a compliant environment.

System Requirements

  • Any Operating System(Mac, Windows, Linux)

This recipe explains Amazon App Runner and the Features of Amazon App Runner.

Features of Amazon App Runner

    • It offers automatic deployments

When users link App Runner to their code repository or container image registry, App Runner can build and deploy their application automatically whenever their source code or container image is updated.

    • It offers to balance of Loads

The Amazon App Runner automatically load and balances traffic to provide high levels of reliability and further availability for your applications.

    • It provides auto-scaling

The Amazon App Runner automatically adjusts the number of containers up or down to fit the demands of your application, which is enabled by default.

    • It provides metrics and logs

The Amazon App Runner provides extensive development, deployment, and runtime logs, making it simple to monitor and improve their containerized apps. With built-in Amazon CloudWatch integration, users can receive a comprehensive set of computing metrics.

    • It provides management of certifications

The Amazon App Runner comes with fully managed TLS that requires no configuration. Before the certificates expire, App Runner renews them automatically.

    • It provides management of costs

Using the terminal, CLI, or API, users can easily pause and restart your App Runner apps. Users will only be charged if the service is active.

What Users are saying..

profile image

Ray han

Tech Leader | Stanford / Yale University
linkedin profile url

I think that they are fantastic. I attended Yale and Stanford and have worked at Honeywell,Oracle, and Arthur Andersen(Accenture) in the US. I have taken Big Data and Hadoop,NoSQL, Spark, Hadoop... Read More

Relevant Projects

Snowflake Real Time Data Warehouse Project for Beginners-1
In this Snowflake Data Warehousing Project, you will learn to implement the Snowflake architecture and build a data warehouse in the cloud to deliver business value.

Yelp Data Processing using Spark and Hive Part 2
In this spark project, we will continue building the data warehouse from the previous project Yelp Data Processing Using Spark And Hive Part 1 and will do further data processing to develop diverse data products.

AWS CDK and IoT Core for Migrating IoT-Based Data to AWS
Learn how to use AWS CDK and various AWS services to replicate an On-Premise Data Center infrastructure by ingesting real-time IoT-based.

Project-Driven Approach to PySpark Partitioning Best Practices
In this Big Data Project, you will learn to implement PySpark Partitioning Best Practices.

AWS Snowflake Data Pipeline Example using Kinesis and Airflow
Learn to build a Snowflake Data Pipeline starting from the EC2 logs to storage in Snowflake and S3 post-transformation and processing through Airflow DAGs

Create A Data Pipeline based on Messaging Using PySpark Hive
In this PySpark project, you will simulate a complex real-world data pipeline based on messaging. This project is deployed using the following tech stack - NiFi, PySpark, Hive, HDFS, Kafka, Airflow, Tableau and AWS QuickSight.

Build a big data pipeline with AWS Quicksight, Druid, and Hive
Use the dataset on aviation for analytics to simulate a complex real-world big data pipeline based on messaging with AWS Quicksight, Druid, NiFi, Kafka, and Hive.

Migration of MySQL Databases to Cloud AWS using AWS DMS
IoT-based Data Migration Project using AWS DMS and Aurora Postgres aims to migrate real-time IoT-based data from an MySQL database to the AWS cloud.

GCP Project to Learn using BigQuery for Exploring Data
Learn using GCP BigQuery for exploring and preparing data for analysis and transformation of your datasets.

Build an AWS ETL Data Pipeline in Python on YouTube Data
AWS Project - Learn how to build ETL Data Pipeline in Python on YouTube Data using Athena, Glue and Lambda