AWS Lambda Reviews

Our primary use case for Lambda is for serverless computing in our project. We have an environment in AWS with Lambda, EC2, S3, SQS, RDS, and Redshift. Our Lambda function is triggered whenever a new object is put into S3. It will validate, extract data from S3 then input metadata into MySQL and put the main data into Redshift as the data warehouse. An SQS message will be created so our Application in EC2 is alerted that there is new data to have processed.

With AWS Lambda, we found it easy to integrate with other AWS services such as S3, SQS, RDS, etc. This allows us to scale our services in an almost unlimited fashion, as data input into S3 sometimes peak up to a thousand objects and need to be processed as soon as possible.

This solution is easy to integrate with other AWS services, making our job easier and more effective. Lambda is lightweight and we can quickly develop our functions. The ability to scale up and down very quickly helps because we can maintain our system performance and business at a low cost.

There are several things that can be improved, including:

• It has limited access to disk space, as it does not have full access to underline VM
• Lamba functions have cold-starts that can cause some delay
• It is hard to troubleshoot and monitor Lambda functions

Our Lambda functions are very stable, despite the fact that we have scaled up and down frequently. This means our functions run in the different underlying hosts, but their behavior is still the same.

This solution can scale up to thousands of instances in a very short time without any delay. It is very fast.

We have just started with Lambda and it is great.

Setting up AWS Lambda is simple and AWS provides great documentation that includes installation details, as well as guidelines and best practices. The coding of Lambda functions is simple and straightforward with our developer.

We implemented using our in-house team.

With Lambda, we can reduce the time to deliver our project to production and can provide our clients with a scalable, reliable, and high-performance system.

We considered deploying our functions in EC2 with Auto Scaling but decided on Lambda as we need a lightweight function that is able to scale up and down quickly. Also, the overall cost is much lower.

Implementing this solution is easy, with just a few clicks your function will be ready. I suggest reviewing your requirements and choosing a suitable Lamba function size. Also, have a good strategy on how to trigger your functions.

I back up my MongoDB, that database has user data. The performance of AWS Lambda has been fine.

It has made things a lot easier in terms of restoring if we need to and just not having to worry about the small things.

The console is very good, the user experience works very well.

I would like to see some better integration with other providers, like Cohesity, Druva, and others. I also think the Lambda interface could be better.

I don't have a ton of transactions, so it's very stable from my perspective. But I can imagine that it would not necessarily be as great for an enterprise product.

The scalability is hard for me to see from where I sit because I have a very small company, but I can imagine that it's going to be an issue.

Tech support is very minimalist. It's more do-it-yourself, which works for me, but I don't think it will work forever.

The setup was pretty complex because there were many steps. For me, it was complex because I was somewhat new at it. It could be easier for someone who has done it a bunch of times. I just found that it was a very dense user experience. There's a lot going on during setup.

The vendors on the shortlist now would be Cohesity, Commvault, Druva. But in the past, I went with this because we were using an AWS cluster anyway. It was easy-ish.

My most important criteria when selecting a vendor are

• user experience
• support.
  

I would rate AWS Lambda at a six out of 10 because it's not quite clear that it scales, but it does have a good, easy path from when you're using a cluster.

1. Use lambda function to trigger the jenkins jobs to make whole release pipeline.

2. Send the slack notification once the release with automated test is done.

My automation test story is we moved from CircleCI to Jenkins, then to AWS. The benefit I can see is the maintenance and budget. Ops made this decision but it did not affect automation tests and the release pipeline too much, but I have seen the release pipeline has been developing much faster.

We use AWS Function and Jenkins API to trigger the Jenkins job from the AWS CodePipeline.

Since our automated deploy jobs are on AWS, and automated tests are on Jenkins.

Then we can create the release pipeline including AWS and Jenkins and put them together.

1. Cannot checkout code from GitHub but Jenkins can.

2. No shell script supported.

3. CodePipeline cannot send notification to Slack, but we can use Slack API and Lambda Function.

Pretty good.

I saw Ops setup the automated release jobs faster in AWS than in Jenkins and CircleCI.

For automation tests, it does not affect too much. 

Our Ops are all AWS experts.

For automation developers, we do not care too much about which CI system we use. It does not affect us too much. Automation tests can be ran in most of mainstream CI systems.