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With the boom in the technological era, data is increasing in volume, and managing them demands both resources and time. Amazon Web Services has come up with AWS Batch which will ease the process of implementing the batch processes.

AWS Batch allows you to build efficient, long-running compute jobs by focusing on the business logic and analytics required, while AWS manages the scheduling and provisioning of the job. SNDK Crop has devised solutions to meet your requirements for setting up your batch processes.

AWS Batch process

Why should we use AWS Batch?

There are ample amount of reasons why you should shift to AWS Batch for carrying out the batch processes. Some of them are listed below-

  • More Focus

    Solutions on AWS Batch allows you to completely focus on the logic and analytics of the job while AWS Batch will take care of the scheduling of the work.

  • Workload

    Generally, AWS Batch is used for big analytics jobs that require long-running hours.

  • Migrate to Cloud

    When you want to migrate your work to the cloud but the job dependencies are complex, AWS Batch can come handy in these situations.

AWS Batch Management

How does AWS Batch Works?

Running a single job may be less complicated. But running many processes with complex dependencies requires abundant resources. AWS Batch does a phenomenal job of simplifying the tasks.

AWS Batch comprises of five main components as per SNDK Crop.

AWS Job Definition

Let’s dive deeper into the structure of the AWS Batch.


The unit of work that is to be executed by AWS Batch is a job. We can submit a large number of simple or complex jobs. They can be executed as containerized applications running on the computing environment of Amazon ECS container instances in an ECS cluster.


The job description defines the structure and other parts of the job. It can comprise of the docker image of your job, the commands you wish to run on initialization of the job, the memory specifications, any specific environment you wish to run the command, and a lot more properties.

It consists of these main parts-
  1. Job Name

    The name specified to your job.

  2. Job Type

    It is also a required property of String data type.

There are three types of jobs-
  • Individual Jobs: No different than its name, individual jobs are a single job that is pushed to the Job Queue.
  • Array Jobs: An array of jobs that run together in parallel.
  • Multi-node parallel Jobs: Through this, we can apply multiple clusters of jobs.
  1. Parameters

    Parameters are values that will replace placeholders in the job definition. It isn’t a required property and is of String to String map data type.

  2. Container properties

    It is a list of properties that are passed to the Docker daemon on a container instance when the job is placed. It includes command, environment, image, jobRoleArn, memory, and a few other options. For multinode parallel jobs, these properties are defined in the Node Properties of individual nodes.

  3. Retry Strategy

    It specifies the number of times a job should run in case of failure. By default, it is set to one try. In case of an error, there won’t be a retry. We can set it to any value so that the job is run again in case of an error.

  4. Timeout

    In case the job runs for more than a specified amount of time, it is terminated. This can be set in the timeout property.


Jobs are submitted to the queue where they are scheduled to run in the Compute Environment. A job can be assigned to be of high or low priority and accordingly, they are executed. It is decided upon by the scheduler. Job Queue is defined by the following parameters-

  • Name– A String value that specifies the name of the queue.
  • State– It determines if the queue is accepting jobs or not. This can be done by the “ENABLED” or “DISABLED” keyword.
  • Priority– An integer value establishing the priority order. A higher value means the queue will be executed first. You can have multiple job queues in your AWS Batch.


It evaluates how, when, and where the job Queue is to be executed. It is done following the job queue priority as the jobs are submitted in them.


Compute environments contain the Amazon ECS container instances that provide the platform to run containerized batch jobs. The job queues are mapped into one or more Compute Environment or it can be done the other way round. It is classified as the Managed and Unmanaged Compute Environment.

AWS Batch Job Definition

Where is AWS Batch used?

AWS Batch is used by various industries and even your project can be a part of this list using SNDK Crop Solutions.

  1. Financial Industry

AWS Batch is used in the finance industry in the areas of high-performance computing, risk management and to avoid human errors. It can also be used for Fraud surveillance.

  1. Life Science

In this field, AWS Batch it the best fit for DNA Sequencing and Drug Screening. These processes demand a high computing performance environment.

  1. Digital Media

Digital media requires management of enormous data, high-quality graphics as well as infrastructure for cyber-security. AWS Batch comes handy to process data in a high computing performance environment.

Explore More About AWS Cloud Services

Read: Amazon Redshift: Infrastructure, Benefits and Use Cases


After going through the structure of the AWS Batch, it turns out to be the easiest and user-friendly service to run your Batch Jobs. It not only provides you with the resources but also at a very cost-efficient price range. AWS services like EC2, AWS Glue, Fargate were not efficient enough to carry out batch workloads and a lot of custom tooling was required to manage the AWS services.

So, all your problems with scheduling and managing of jobs that required high-end resources and a generous amount of time can be solved by SNDK Crop Solutions using AWS Batch. It is a powerful service to help you not limit your skills and creativity due to a lack of resources or management.


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