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Understanding AWS Lambda in 3 minutes


AWS Lambda

AWS Lambda is a compute service with no server also know as Serverless computing. Running Lambda enables you create backend applications using different programming languages as Java, Python, Node.js, .Net, Ruby, Go and more.

The best part about Lambda is that you don't need to worry about servers instance to deploy and run it. No concerns about provisioning capacity responsibilities that usually happens to EC2 instances becoming a cheapest alternative to compose architectures.



How it works


Lambdas also are used to compose architectures being responsible for specific workloads. For example, using Lambda you can start listening to files from a S3 Bucket processing them to normalize it or you can use EventBridge (Cloudwatch events) creating schedules through a cron expression to trigger the Lambda to run workloads and after that shutdown the process.


As shown in the image below, we have a some examples of AWS Lambda integrations, so you can use them to invoke Lambdas for a variety of scenarios.



Limitations


Lambdas can run for up to 15 minutes, so if you want to try it out, be careful handling workloads that take longer than 15 minutes.



Integrations


As mentioned earlier, AWS Lambda allows for various service integrations to be used as a trigger. If you want to listen to objects created on S3 Bucket, you can use S3 as a trigger. If you need to process notifications from SNS, you also can set Amazon Simple Notification Service (SNS) as the trigger and it will receive all the notification to process.

Note that we have different scenarios that Lambda can solve solutions efficiently. Here you can see a complete list about the integrated services.

Prices


AWS has certain policies regarding the use for each service. Lambdas are basically billed by number of requests and code execution time. For more details, see here.


Use Cases

Here we'll have some example where the use of Lambda can be an interesting option.


  • Data processing: Imagine you must normalize unstructured files into semi-structured to be read by some process. In this case it is possible to listen to a S3 Bucket looking for new objects to be transformed.

  • Security: A Lambda that updates an application's users token

  • Data Transform: You can use Kinesis/Firehose as a trigger, so that Lambda can listen to each event, transform it, and send it back to Kinesis to delivery the Data to S3.


Benefits


  • Price: Pay just for requests and code runtime

  • Serverless: No need server application

  • Integrated: Lambda provides integration with AWS Services

  • Programming Language: It's possible to use main programming languages

  • Scaling and Concurrency: Allows you to control the concurrency and scaling the number of executions until account limit


 

Books to study and read


If you want to learn more about and reach a high level of knowledge, I strongly recommend reading the following book(s):


AWS Cookbook is a practical guide containing 70 familiar recipes about AWS resources and how to solve different challenges. It's a well-written, easy-to-understand book covering key AWS services through practical examples. AWS or Amazon Web Services is the most widely used cloud service in the world today, if you want to understand more about the subject to be well positioned in the market, I strongly recommend the study.














Well that’s it, I hope you enjoyed it!





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