Item: AWS Batch
Rating: 5
Author: Madhav Pujara

Overall Satisfaction with AWS Batch

Use Cases and Deployment Scope

We encountered issues orchestrating long-running tasks in Lambda functions, as they have a limit of 15 minutes. We needed a solution that could be triggered from AWS Step Functions. Then we found yes it's a bit tedious to set up, has a steep learning curve but once that's passed you can easily run a 24 or 48 hours or other long-timed task without any issues, which helped our use case where needed to run a task which took 24 hours even with AWS Batch's Compute

Pros and Cons

Pros

Easy to orchestrate and trigger jobs
No time limit issues like lambda
Multiple Jobs can be run in same single compute and job queue
JOb queue can queue up task for parralled or serialization

Cons

Steep Learning Curve
Setting up jobs could be easy
Giving roles access to compute environment is hard and confusing

Return on Investment

Well compared to the cost of having multiple lambda's or some other custom solution working, it has has a postiive impact
Helped in orchestrating the jobs, has saved time of developers

Usability

Key advantages include cost-effectiveness through dynamic resource provisioning and the use of spot instances. It auto-scales to meet workload demands, allowing easy job submission via the AWS Management Console or SDKs. It integrates seamlessly with other services like S3 and CloudWatch. It features automatic retries for failed jobs. It allows for a custom computing environment tailored to specific needs

Key Insights

Do you think AWS Batch delivers good value for the price?

Yes

Are you happy with AWS Batch's feature set?

Did AWS Batch live up to sales and marketing promises?

I wasn't involved with the selection/purchase process

Did implementation of AWS Batch go as expected?

Yes

Would you buy AWS Batch again?

Yes

Other Software Used

Amazon CloudWatch

Likelihood to Recommend

In our case, we had a SQL shell script that ingested data into RDS using the MySQL command-line interface (CLI). However, the file was 4 gigabytes in size, so reading each line, transforming the data, and writing it to different tables for various files took a considerable amount of time.

Less Appropriate: if your job doesn't run on demand or can be completed in less than 15 mins , batch doesn't set your use case as the steep learning complexity for such a small time consuming workload is better spent on lambdas or EC2 configuration as well , and if you don't know but batch is a lot more expensive than lambda's or basic EC2

AWS Batch Feature Ratings

Comments

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Why AWS Batch is Blessing for some and curse for others