What is AWS Lambda?

AWS Lambda is serverless computing that enables developers to execute code in response to occurrences without worrying about managing servers, thus making it an essential component of the current serverless architecture. It works by executing your code in a highly available computing environment that manages all the aspects of your computing resources, such as server management, operating system, computation capacity, scaling, and logging.

This platform adjusts its response based on the number of events, potentially impacting application deployment, resource utilization, and scaling. Lambda is great for short-lived and flexible work, but it is best suited for short interims or tasks with simple resource or runtime needs.

AWS Lambda possesses a range of benefits, but its characteristics may render it a better fit for specific applications. These advantages may concern developers’ code quality, scalability, and efficiency, which could influence developers’ productivity.

• Infrastructure management: Lambda’s serverless nature minimizes the need for direct server management compared to traditional solutions, potentially resulting in a less complex operational environment and a lower requirement for specialized infrastructure expertise.
• Elastyczność: The availability of numerous languages and runtimes on Lambda may influence developer choices and application-building approaches.
• Adaptive infrastructure: Using a server-less computing environment, Lambda architecture has integrated scaling that rotates its applications about workload requirements.
• Pay per request option: One is only billed based on the computation employed, whereby the user is charged depending on the amount of computation utilized.
• Operational simplicity: Lambda’s automated scaling and self-management capabilities help simplify operational tasks by eliminating manual server configuration and maintenance but require initial setup and configuration.
• Custom logic and backend services: Lambda enables the creation or modification of backend services using custom logic while drawing upon the infrastructure, security, and performance capabilities of AWS. 
• Similarity in tooling and processes: Adherence to established practices; Lambda integrates containers, images, and serverless artifacts known by developers.
• Limited execution time: Lambda functions provide a beneficial utility, but their execution time is limited to fifteen minutes, which can be a drawback if an operation takes too much time.

AWS Lambda can be applied to various types of serverless computing processes. Here are some of the most common uses:

• Event-based data processing: Lambda executes the specified actions in case of occurrence of events by the workflow.
• Real-time file processing: Lambda can automatically trigger file processing jobs when files are updated or uploaded.
• Backend services for web and mobile applications: Create APIs that scale effectively without dealing with servers through Lambda.
• Task automation: Emailing, writing logs, and event scheduling, and finally discouraging, routinized functions as a way of avoiding repetitive functions.

Of course, like any service, AWS Lambda also has several constraints worth mentioning when choosing this service for applications. These limitations can influence what kind of applications are suitable for Lambda and may require other approaches for such workloads. You must mind these limitations so your Lambda functions run optimally in the AWS environment.

• Runtime Environment Resources: Lambda functions have inherent restrictions regarding resources available to the function runtime. These constraints include disk space (ephemeral) of 512 MB and a maximum memory allowance of 10240 MB. To ensure efficient operation, Lambda implements resource limitations, which affect the types of functions suitable for its environment.
• Execution Timeout: It is important to note that Lambda functions have an upper limit of 15 minutes of processing time. This constraint becomes more relevant when dealing with processes or tasks that require extended processing. If your application requires such operations, you might have to find other ways of accomplishing this or split the process into smaller, more manageable Lambda functions on a time-based model.
• Deployment Package Size: The size of the deployment package that is supported by default for a Lambda function is 50 MB. The size of deployment packages becomes a critical factor, especially for applications with numerous dependencies or complex code. To address this constraint, you might have to look for creative ways to work around it; one such way is to use container images or subdivide your application into functional sizes that are more easily deployable.

The anticipated growth of AWS Lambda may be driven by a combination of customer needs and evolving technologies.

An important focus lies on Lambda Destinations, enabling non-simultaneous invocations to enhance activity-based programs and facilitate code complexity. 

This suggests additional incorporation of AWS services and SaaS apps to increase based on event characteristics and deliver more instinctive serverless environments.

AWS Lambda’s development indicates its potential to play a prominent role in serverless architecture, although the exact nature of its future evolution is yet to be determined.