AWS Batch Reviews

In many ways, it's like using an HPC environment but a lot more flexible. In theory, you could have many different kinds of computing systems and computers, ranging from those geared toward computational speed to larger memory machines or GPU machines. 

The idea is to break your computational jobs into smaller jobs that can be run on multiple machines. Any of these virtual machines will orchestrate all of the computation, send out jobs to different machines, wait for them to be done, and then run the next process in the sequence. It's simply a way to run multiple processes on multiple machines in the cloud.

Scalability is the most valuable feature for me. I could run anywhere from 32 cores to over 2,000 cores. So it scales very well, and it's really good for situations where jobs are very heterogeneous, meaning that you have a long-running job that sometimes needs a lot of small compute-intensive machines, for example. 

But then, in the second stage, you may need a few very high-memory machines. It's really good for those kinds of situations, for HPCs, where you can really customize and tailor the compute, memory, and GPU requirements for the job. 

You can run as many jobs as you want, provided you pay the cost. So, it's about the scalability to run really large jobs in a really short amount of time with a very minimal setup. 

One person can set up a compute cluster on AWS Batch. I don't need all the hardware resources, people to maintain those resources, or software installations.

Moreover, there is one other feature in confirmation or call confirmation where you can have templates of what you want to do and just modify those to customize it to your needs. 

And these templates basically make it a lot easier for you to get started. So, if you've been doing this for a while, you probably already have a template in your toolbox, and you can use one of those to get started, but you would just customize it. So, these templates help a lot.

I use AWS Batch for planning, scheduling, and computation on containerized workloads. In my previous role, I heavily used AWS Batch for running numerous simulations. It includes running different types of computations, such as on-demand and scheduled computations, and creating clusters to optimize memory, CPU, or GPU usage. This flexibility allows processing batch jobs effectively, such as data processing and computation.

AWS Batch is highly flexible. It allows users to plan, schedule, and compute on containerized workloads. In previous roles, I utilized it for diverse simulations, including on-demand and scheduled computations. It facilitates creating clusters tailored to specific needs, such as memory-centric or CPU-centric workloads, and supports scaling operations massively, like running one hundred thousand Docker containers simultaneously.

My company is a battery storage solution provider. We work on a grid level, where we can store energy to power up entire cities. Our architecture handles high-frequency data, and we use AWS Batch for data extractions. Every two seconds, we monitor and collect node-level and pack-level data, such as system-level voltage, state of charge, state of health, cell voltages, temperature, power thresholds, and warning points. AWS Batch enables us to scale efficiently, whether we're extracting data from one client or over two hundred clients. It's a reliable, cost-effective solution for our needs.

The main feature I like about AWS Batch is its scalability. Whether ten extraction jobs or ten thousand jobs are running, it works seamlessly and scales seamlessly. The Fargate option is cost-effective and efficient, removing dependency on EC2 instances. AWS Batch also integrates with the entire AWS ecosystem, including S3, Lambda, and AWS Lambda Step Functions, making it robust. I can use different services with AWS Batch, trigger it through other services, and orchestrate AWS Batch jobs. AWS Batch allows time-extensive workloads to run for days without interruption, unlike AWS Lambda's fifteen-minute hard deadline. It's reliable and cost-effective, and it has been a good solution since 2021.

At our startup, we use AWS Batch to allow users to run Python code securely in isolated environments. I configured everything related to AWS as a DevOps in our startup. Users can share their code, and others can execute it utilizing AWS Batch for safe containers.

The three options for creating AWS Batch compute environments, Fargate, EC2, and EKS (Elastic Kubernetes Service), are very valuable to me. The system is maintainable and allows for running concurrent jobs by dynamically increasing CPU and RAM. AWS Batch includes a queue system and supports scheduling tasks, enabling jobs to start when resources are available.

I have an analysis pipeline, and I want to deploy that pipeline. It's a containerized pipeline that uses Docker, and I want to deploy that pipeline in parallel on all my samples. I use AWS Batch to launch those jobs in parallel. Each sample gets processed as a batch job submitted on AWS Batch.

I appreciate that AWS Batch works with EC2, allowing me to launch jobs and automatically spin up the EC2 instance to run them. When the jobs are completed, the EC2 instance shuts down, making it cost-effective. In my current role, we don't have an on-premises cluster, so AWS Batch allows me to run things in parallel rather than using a shell script and running them on a large, long-running instance.

In my project, I worked on an enhanced backup of some assets in AWS QuickSight. AWS QuickSight has a rudimentary 30-day buffer for restoring deleted assets but lacks other versioning options. To address this, I back up data to JSON files and run an AWS Batch process hourly to detect any changes during business hours. Additionally, a longer process runs every few months to check the data's integrity and restore it to its original state, which takes a couple of hours due to the size of the QuickSight installation.

AWS Batch is a cost-effective way to perform batch processing, primarily using spot instances and containers. We package our application as a Docker container, place it in ECS, and run it through AWS Batch. Although batch processing often lacks the appeal of real-time processing, it is highly useful for jobs that can execute within a designated time frame. AWS Batch is economical and reliable for these scenarios.

We use AWS Batch to manage containerized workloads and dynamic scaling. The solution automatically scales computer resources based on the number of jobs and resource requirements. The solution also integrates with other AWS services.

AWS Batch manages the execution of computing workload, including job scheduling, provisioning, and scaling. AWS Batch is specified as a docker container that allows us to run our applications with all their dependencies.

I use AWS Batch for my data activities, including building the data science pipeline, processing, storing data, and performing analytics.

AWS Batch is invaluable for parallelizing processes and samples, which is essential for our large data sets, such as terabytes of genome data. It also integrates well with other services like Step Functions. The job scheduling capabilities allow getting the status of jobs, whether running, processing, or stopped, and it can be integrated with monitoring and alert systems.