AWS Lambda Professional, Scientific, and Technical Services Reviews & Insights

So, in a recent task, we developed an application where user-uploaded images were getting stored in S3, triggering AWS Lambda function to resize them for uniform display. AWS Lambda also handled CRUD operations, which allows us to focus solely on business logic without managing servers. This serverless approach has streamlined development and we continue to use it for various tasks.

AWS Lambda is well suited for event driven tasks that require automatic scaling and minimal infrastructure management such as Image processing (as in our case) and Backend API's. However, AWS Lambda is less appropriate for long running processes and tasks that require consistently low latency as cold starts can introduce latency.

AWS Lambda is perfect for small serverless workloads that enable cheap integrations with native AWS services. It is primarily used as a trigger from things like DynamoDB or SNS, which allows for very lightweight automations to occur. Not having to allocate or pay for a dedicated server to handle these micro workloads is ideal. The integration with many services gives AWS Lambda a lot of potential.

AWS Lambda is well suited for small/micro workloads that do not need to be running 24/7. It is a very cost efficient way to handle quick triggers or automations that integrate with AWS services. Things like triggering an email from a DynamoDB change or processing an incoming file into S3.
It is not suited for very complex and/or very long running jobs that need to be ready and active at all times.

I used AWS lambda in one of project which was developed by company I am currently in. My current company is AWS partner network accredited. While developing project, our company tries to make offer for AWS lambda as it is cost efficient compared to EC2 instance or any other compute service. Business problems are related to what clients want in a smaller budget. AWS lambda is considered as server-less compute service and helps to deliver solution much faster than any other computer service.

Compressing image while uploading it to S3 bucket can be easily done using lambda. While if your workload require lot of execution time or there is large amount of data to be processed, also workload where tweaking environment to suit workload, lambda can be less appropriate. Instead other computer service like EC2 and elastic beanstalk can be well suited for such task.

-AWS Lambda is being used for cron job with AWS Event bridge.
-AWS Lambda can be also be used as a serverless function with api gateway.

-AWS Lambda can be used for cron job with AWS EventBridge.
-When we use AWS Lambda use with API Gateway to only transport data to another service without any business logic.

We are using AWS Lambda as [much as] we can and when is feasible. When I say feasible, I mean we should observe AWS Lambda limits and costs because even [with] AWS Lambda being an amazing product, you need to be careful with costs since every call to a function will charge you.

I believe the main concern is about costs. If your function calls generates profit, no problem with the amount, as more [is] better. But if this is not the case and your user case trends to grow without associate profit, maybe by provisioning dedicated resources (EC2 instances for example) to run your functions will hurt less in your wallet.

AWS Lambda serves various purpose accross teams
1. We mainly use AWS Lambda when we have very short time to productionise code and have little time to worry about infrastructure.
2. AWS Lambda takes care of scaling and dynamic increase in inflow of traffic.

Well suited:
1. when we need to worry about time to market and we don’t have infra defined.

Not suited:
1. Not suited for Business Client transactions as its server are located out of Switzerland and hence country laws are different

We use AWS Lambda to help us automate the process of start/stop EC2 instances in AWS. We use it in infrasctructure but it affects the whole organization and most of our internal servers and customer servers are in AWS. With this automation, we cut our cost by 35% which is a very significant amount for a small company.

The main area in my point of view is the automation reduction of costs. You can program and use Lambda to execute several tasks based in several types of events like logs, schedules, and output from other AWS tools. With the AWS API, you can do almost anything you want and your function will use only the needed resource (memory, cpu) so it is optimized.

Monitoring Usecase:
In the organization, we have got a need to increase the monitoring and availability of the systems, because of which we have created several scripts that run on a server for every 15 mins to extract data from one system and store it in a database. It was working fine when we have a few scripts and less number of CRON jobs. We have a complex environment, we interact with multiple systems most of the time and there are several logs that are captured in different systems, so our number of scripts increased, eventually jobs increased to run. We started to see a bottleneck on the server, so we started to think Cloud alternative and analyzed about the usage of AWS Lambda functions.

AWS Lambda is well suited for batch scripts, and API development.

AWS Lambda is being used by my organization to manage machine to machine communication via serverless code. It addresses the problem of sending data between services on Amazon Web Services and it does the job quite well. The lambda handler allows me to set invocations at certain durations and also allows me to pass data back and forth in an automated manner.

AWS Lambda is well suited in cases where the entire stack is on AWS. It doesn't make sense to use lambda when you have services with other cloud providers.

A scalable tool used to configure applications which can be used from outside the organization's environments. The servers are managed on their own by AWS, which is one less task to be taken care of. Can be easily integrated with other back-end APIs like Node.js or Python. Highly scalable, load balancers are configured automatically.

Can be used for creating:

• Alexa skills.
• Serverless architectures.
• Ability to create RESTful APIs.