Last updated on Nov 20, 2023
AWS Batch is referred to as the service that helps in running batch workloads and holds the capabilities of batch management on the AWS cloud. Most professionals like scientists, developers and engineers utilize batch computing for performing the operations periodically. It also helps in allowing them to access all the resources.
AWS is called a fully-managed service that helps in computing and optimizing the resources based on the scale and quantity of the workloads. It is capable of provisioning different types of resources like memory or CPU optimized resources.
There is no need for the installation of any servers or software like batch computing software, clusters, etc. All you need to do is to keep an eye on the result analysis and problem-solving. AWS batch is capable of performing the operations like planning, scheduling, executing the batch workloads by using the resources like Amazon EC2, Fargate spot, etc.
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Through the AWS Batch, the execution of the jobs becomes easier which helps in developing different kinds of applications. It also provides extensive support for computing resource management that helps in scaling the different jobs parallelly and also helps us in analyzing the results without any setup or infrastructure management. It is recommended to use AWS batch if you run or move the batch workloads to AWS.
AWS Batch includes many features that make it more effective and help the organizations to run the operations smoothly.
Below listed are some of the unique features of the AWS batch.
Priority Job Scheduling is considered one of the important features of AWS Batch. With this feature, you will be able to set up the total number of queues within different levels of priority. The batch jobs will be stored in the queues until and unless the computed resources are ready to perform next job execution. The AWS batch includes the AWS Batch scheduler that is responsible for keeping a tap on where, when and how the job execution has to take place which is purely based on the resource level requirements.
AWS Batch makes use of the Fargate Spot or Fargate, during which there is a need to perform some functions like setting up the job queue, CE, job definition, etc. As you now have the complete structure that includes the queue along with the scheduler, compute architecture, there is no need for you to get into a panic situation about the computing infrastructure management.
AWS Batch provides its extensive support for GPU scheduling, a feature that is used for specifying the type and number which is required by the job as a job definition input variable. The GPU scheduling also helps in scaling up the instances appropriately based on the number of GPUs for the jobs and helps in isolating the accelerators as per the job needs. This will allow only the required containers to access.
When an AWS batch is combined with the Fargate resources, it helps in developing a serverless architecture to perform the batch processing for the jobs that you require. Each and every batch will utilize the same amount of memory and CPU when you implement the AWS batch with Fargate. With this, there is no need for you to wait for the EC2 launches and there will not be any wastage of resources and time.
AWS batch is capable of providing a clear view of the operational metrics for the batch jobs within the AWS management console. It displays all the information at the metric level which is related to computing capacity and pending, completed and running jobs. All the logs for the jobs will be available in the AWS management console. You can also make use of the Amazon CloudWatch Logs for maintaining the logs.
AWS Batch is integrated with the commercial and open source languages and workflows such as Nextflow, Luigi, Metaflow, Apache Airflow, Pegasus WMS and AWS step functions. AWS batch allows the use of workflow languages for modeling the batch pipeline.
AWS batch provides its extensive support for multi-node parallel based jobs that help in running single jobs that span many EC2 instances. Through this feature, you can run the workloads easily and efficiently.
AWS Batch includes 4 different components which are explained below.
A job in the AWS Batch is a unit of work we submit. Each job contains a specific name and ID. Further, we can reference these jobs with a Job Name or Job ID, which purely depends upon the other jobs we completed.
A job definition provides the information on how the job can be run, it is a blueprint of the job resources. In the job definition, you can provide the CPU and memory requirements as well. It also includes information like the environment variables, container properties and mount points to maintain persistent storage.
When an AWS batch job submission takes place, it will be submitted to a job queue and will be available there itself until it is scheduled. There is a possibility to link one or more computing environments within the job queue. You can also assign the values based on the priority levels.
A computing environment refers to the set of unmanaged or managed computed resources that are used for running the jobs. You are allowed to create or set up the com[ute environments that make use of a particular model or an instance. The computing environment includes all the information like desired, maximum and minimum number of CPUs.
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Below is the list of alternatives for the AWS batch:
AWS Lambda is referred to as a computing service that does not require any managing services or provisioning for running the code. It performs the code execution only when it is required and is capable of scaling automatically. You will need to pay only for the compute time that you consume and does not require any payments while the code is running.
AWS Elastic Beanstalk is referred to as a service that helps in the deployment and scaling of the web applications, services that are developed in different programming languages like Ruby, Go, Python, PHP, Java, Docker, .Net, etc. Beanstalk is capable of handling the deployment once the code is uploaded and also you will gain access to the resources anytime.
Apache Airflow is a service that helps in setting up and operating the pipelines in the cloud easily. It helps in automatic scaling by setting up the minimum and a maximum number of workers. It also includes role-based authorization and authentication with a defined set of access policies.
Kubernetes is referred to as an open-source that helps in easy deployment and management of the applications. It is responsible for managing the different clusters of Amazon EC2 instances and also helps in running the containers. It is possible to run any kind of containerized application in the cloud.
In simple terms, AWS batch is an excellent service that helps in minimizing third party batch processing solutions. As it does not require any software or server, it is easy to manage. Through this blog post, you have got an idea of the AWS batch, its features. I hope the above information is helpful to you. You can also gain in-depth knowledge by getting trained and certified in AWS training.
Ishan is an IT graduate who has always been passionate about writing and storytelling. He is a tech-savvy and literary fanatic since his college days. Proficient in Data Science, Cloud Computing, and DevOps he is looking forward to spreading his words to the maximum audience to make them feel the adrenaline he feels when he pens down about the technological advancements. Apart from being tech-savvy and writing technical blogs, he is an entertainment writer, a blogger, and a traveler.
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