AWS Lambda: What It Is and 5 Practical Applications

The cloud is the new normal for computing. It has become the foundation for software development and delivery in general, and a variety of computing-related services are now offered as cloud services. These cloud services come in many different flavors, each targeting a specific use case or technology.

What is AWS Lambda?

Lambda is a computing service offered by Amazon Web Services (AWS). Developers can use it to build and run serverless applications. A lambda function is a piece of code that executes in response to an event. This event can be as simple as an HTTP request, or as complex as receiving data in a SaaS application.

Lambda functions are always invoked in the cloud and can be written in any language that can run on AWS, such as Java, Node.js, Python, or C#. Recent versions even support container execution. Lambda is event-driven and stateless, meaning that every time a function is triggered, it starts with a clean slate. In this sense, it's similar to a cron job, but it has some advantages.

Why use AWS Lambda?

The main reason to use Lambda is that it frees you from having to manage server infrastructure. As we've already mentioned, Lambda functions are always invoked in the cloud. This means all you have to worry about is writing the code for those functions.

There's no need to provision a server, install an operating system, or perform any of the other tasks associated with server setup and maintenance. Lambda is also highly scalable. All you have to do is configure your function to use a sufficient amount of memory, and Lambda will automatically scale it to meet demand.

This means your application will remain online even during periods of high traffic. So, one of the major advantages of using Lambda is its high availability, low latency, and ease of integration.

You simply pay for what you use, so if a feature is not used, it doesn't incur any costs.

5 practical applications for AWS Lambda

AWS Lambda functions are event-driven, meaning they are triggered by specific events, such as an HTTP request or the arrival of data in a SaaS application. This makes them well-suited for a wide range of use cases, including:

• Streaming data processing: Lambda can be used to process data ingested in real time. This can be useful in a variety of use cases, such as auditing, monitoring, and event logging.
• Batch processing: Lambda can also be used to process large amounts of data. This can be useful in a variety of use cases, such as data cleansing, ETL, and data analysis.
• Building microservices: Lambda functions can be combined to form microservices. This makes it easier to build large applications, as microservices can be developed, tested, and deployed independently.
• Integration: Lambda can be used to create a wide variety of integrations, such as custom APIs, webhooks, and triggered emails. This makes it easy to integrate various third-party services with your own application.
• Disaster Recovery: Lambda can be used to implement a disaster recovery plan for critical applications. This can be useful when an application moves to a new data center, or when an application transitions to a new service or technology.

AWS Lambda Limitations

Despite all the advantages and functionalities mentioned above, AWS Lambda has some limitations.

For starters, Lambda functions can't be invoked directly—they can only be triggered by specific events. This means they can't be used to implement real-time applications. Fortunately, many real-time applications can be built using event-driven microservices, making it easy to integrate Lambda functions with them.

Lambda functions have a maximum execution time of five minutes. This means they are not suitable for long-running tasks. Fortunately, long-running tasks can be broken down into smaller chunks using a cron job, or by using triggers that run the Lambda function multiple times.

Lambda functions also have a maximum execution request of 1 GB. This means they cannot be used to process large amounts of data. This can be mitigated by breaking long-running tasks into smaller chunks, or by using triggers that execute the Lambda function multiple times.

There's also an issue with Lambda: it takes a long time to create a temporary container. This takes between 100 milliseconds and 2 minutes and is known as a cold start. However, this is solvable. To ensure the container isn't destroyed, simply ping your Lambda every 5 minutes. Only the first request will take a while to process; all subsequent requests will be processed much faster and without delay.

Conclusion

There is a huge payoff in using a serverless architecture like AWS Lambda to deploy some functionality in a production environment without investing in infrastructure and paying only when you use it.

Serverless platforms save time and money, while increasing security and reliability.

Before adopting a serverless architecture, you should carefully evaluate whether or not it is the right approach for the problem at hand.