AWS Lambda Killer Use Cases
Since its launch in 2014, Lambda’s popularity has soared. It has become one of the fastest-growing and most widely adopted products for AWS. Furthermore, there is a surge of AWS Lambda use cases across organizations today.
An event-driven serverless computing platform, AWS Lambda could be used in many instances. It’s easy to get started with and could run with less operating costs. Since its launch, the serverless model has taken off and every major cloud provider has introduced its FaaS service, including Azure functions. The AWS lambda vs azure functions focuses on the unique features of the platform.
How does Lambda work? Let’s explore.
9 AWS Lambda Killer User Scenarios
Serverless Websites Operation
This is one of the killer use instances to take advantage of the Lambda pricing model. Host the web frontend on S3 and accelerate the delivery of content with Cloudfront caching. Requests to Lambda functions could be sent by the web fronted through the API Gateway HTTPS endpoints. The platform could handle the app logic and persist data to a completely managed database service.
Lambda functions and databases could be handled within a VPC to separate them from other networks. You only pay after the traffic has been incurred in Lambda. The only fixed expense is the running of the database service.
Fast document conversion
When you provide documents to users, such as manuals, specifications, and others, they may not always want them in a single standard format. Although a lot of users are happy with an HTML page, others may opt for a PDF download or may need the document in a format that’s more specialized.
Of course, copies of every document could be stored in all formats. However, storing static documents could take up so much space and this is not practical if the content changes often. Generating documents on the fly is so much easier, which is exactly the task that an AWS Lambda app could handle fast and easily.
Swift Log analysis
To check log files from CloudWatch or CloudTrail is a breeze with Lambda. Furthermore, the platform could search in the logs to look for certain events or log entries as they happen and send notifications via SNS. Also, you could implement custom notification hooks easily to Zendesk, Slack, or other systems by calling the API endpoint within the platform.
Working with external services
If an app or website needs to ask for services from an external provider, there is no reason in general why the code for the main app or site should handle the details of the response and the request. Waiting for a response from an external source, as a matter of fact, is one of the major reasons for slowdowns in web services.
When handing requests for things like inventory checks and credit authorization to an app that runs on AWS Lambda, the main program could continue with other transaction elements while waiting for a response. Meaning that in most instances, a slow response from the provider would be hidden from customers as they would see the transaction proceeding, with the data required arriving and processing before it closes.
Rendering of Predictive Page
AWS Lambda use cases also include more than cleaning up data. If you are using predictive page rendering to prepare web pages for display, Lambda could play a major supporting role. For instance, you can use a Lambda-based app to retrieve documents as well as multimedia files.
If there are multimedia files served by an external source, like YouTube, for instance, a Lambda app could check their availability, and try to use alternate sources if not available.
Uploaded S3 Objects Processing
You can immediately start processing files by Lambda by using S3 objects event notifications the moment they land in S3 buckets. AWS Lambda image thumbnail generation is a great example of this use scenario. The solution would be cost-effective and there is no need to worry about scaling up since Lambda could handle any load.
Daily Tasks and Automated Backups
Lambda events schedules are great for housekeeping within AWS accounts. Building backups, generating reports, checking for idle resources, and other tasks that occur often could be implemented right away using the boto3 Python libraries as well as hosted in AWS Lambda.
Real-Time Bulk Data Processing
It’s common for a website or an app to handle a certain amount of real-time data. Data could stream in from different communication devices, from user input devices to peripherals interacting with the physical world. In most instances, data is likely to come in short bursts, or even a few bytes at a time and in formats that could be parsed easily.
Nevertheless, there are instances when an app may have to handle big volumes of streaming input data. Moving that data to temporary storage for processing later may not be a good solution. For instance, you may have to choose specific values in a fast data stream from a remote telemetry device as it comes in.
When sending that stream to an AWS Lambda app designed to pull and process the information required fast, all the necessary real-time tasks could be handled without slowing down the main app.
For any consumer-oriented website, rapid response time is one of the top priorities. Visible response delay or slow response could mean significant traffic loss. If a website is too busy handling background tasks to display the next page fast or show search results, users could go to another website that delivers the same kind of services or information.
Backend tasks should not be a source of delay when it comes to responding to frontend requests. If there is a need to parse user input to store to a database, or if there are other input-processing tasks not necessary for next page rendering, the data could be sent to an AWS Lambda process. It could not only clean up data but could also send it to an app or database that uses it.
Lambda is 100 percent no operations and could run on app code with the AWS infrastructure. While most PaaS offerings are designed to run 24/7, Lambda is entirely event-driven and runs only when invoked.
This is perfect for app services that have quiet periods followed by traffic peaks. Lambda could instantly scale up to a big number of parallel executions in terms of scalability.