10 Practical Examples of AWS Lambda

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AWS Lambda

Catching up with the latest technology trends in software development is very difficult and implementing it is even more difficult. The story is not so different for serverless. Leaving this debate aside, cloud architects always find it challenging to change their old VMs container-based for adopting serverless technology.   

But the technology that snowballs into popularity is often the result of tremendous community feedback and experiments. Understanding of practical use cases is required for bringing clarity, consensus, and commitment towards the adoption of serverless. In this article, we will be discussing a few AWS lambda examples that are a bit complex. 

AWS Lambda Practical Examples

Below are the practical examples of AWS Lambda:

1. AWS lambda example showing media transformation

Cross-device development is a large concern when it comes to application development. Facilitating this comes at the high cost and manual tasks that hinder the efficiency of development teams. Nevertheless, with AWS lambda you can solve and automate this problem by developing a multi-platform media and content delivery pipeline. One of the best examples is Netflix with its 70 billion hours of content in a quarter to nearly 60 million customers uses AWS lambda examples for transforming to facilitate their media files in more than 50 formats.

2. Deriving multiple data formats from a single source

Many times there comes a necessity when a single object is required in multiple formats. AWS Lambda along with S3 and SNS helps in building a general-purpose event-driven system that processes data in parallel. For implementing this a  pub-sub model is used for creating a layer where data could be processed in the required format before sending it to the storage layer.

3. Real-time data processing example using AWS Lambda

Processing data in real-time and responding to it is highly imperative for modern business requirements. For enabling this, analyzing metrics data in real-time is critical. But Amazon Kinesis stream and AWS Lambda types have made it possible. Creating Kinesis stream and configuring it for capturing your data from the website. 

Time message facilitates data volume processing on 1 shard: one function basis to limit the parallelism as soon as you hit a certain data limit per shard. Set number of Lambda function instances skills automatically as the stream is scaled.

4. Custom logic workflows using AWS Lambda

We've seen a lot of sophisticated systems (e-commerce, analytics software, ERP, and so on) that are made up of complex repetitive scenarios that need to be run in reaction to a certain event. Workflows, in other words. Lambdas could not previously be included in such processes. 

A developer's job was to coordinate these routines, connect them, and verify conditions before choosing what to perform. However, with Step Functions, coordinating AWS Lambda is possible. These functions will be brief, simple to test, and will only have one responsibility.

5. Change data capture with AWS Lambda

It is frequently necessary to examine and maintain track of modifications made to the database. Alternatively, you might wish to process data before storing it in a database. This is doable using AWS Lambda and DynamoDB Streams. When used with Common Lambda application types and use cases, Amazon DynamoDB can let you activate a piece of code that responds to events in the DynamoDB Streams. These triggers can assist you in developing an application that reacts to data changes in DynamoDB tables.

Examples of AWS Lambda
Examples of AWS Lambda

6. AWS Lambda example showing custom Alexa skills

Alexa is a voice assistant that has become famous thanks to Amazon's Echo smart speakers and gadgets. We're used to conducting speech searches and communicating with voice assistants integrated into our cell phones. Alexa is an example of an AWS Lambda service. This comes with a collection of predefined functionalities (called skills) for the voice interface. Alexa converts natural language into a programmed action for voice interaction.

7. Automated stored procedures using AWS Lambda

Users may be required to perform computation work to obtain different forms of data. The data that is being entered, modified, or removed into the database provides the basis for this compute activity. However, they frequently prefer that the computer work be done on the database rather than on the computing resources. AWS Lambda use cases may be called a "storage/stored procedure" in MySQL. 

This feature activates the function before or after certain database table actions of interest are done. Because of Lambda's capabilities, traditional stored procedure techniques have been recreated into new methods with higher and larger velocity.

8. The serverless image recognition engine

Assume you have a website where visitors may post photos. You want the photos to run through a series of workflow steps as soon as they're uploaded. A workflow in which a user uploads an image to an S3 bucket, for example, activates a Lambda function 1. The job of many Lambda functions is orchestrated in this step function workflow. The first thing it does is extract the image's metadata, such as image type information. In the meantime, three more Common Lambda application types and use cases are called in parallel.

9. Serverless text-to-speech example

Text-to-speech has become essential for current applications with the introduction of AI-enabled gadgets. The most recent addition to the TTS capability is Medium. Furthermore, voice synthesis is a difficult issue with an unending set of interpretation problems. You may use AWS Lambda and Amazon Polly to create a lifelike voice synthesis application. Amazon Polly synthesizes speech that sounds like a human voice using advanced deep learning technology.

10. Personalized content delivery through AWS Lambda

Currently, the majority of the app supports customized information and news feeds. This is feasible because individualized user experiences are becoming more common, and accessing and monitoring user touchpoints is getting more straightforward. Setting up and administering a complicated architecture, on the other hand, isn't required. With AWS Lambda, you can quickly get started with a customized content platform that allows you to make changes on the fly.


As it must have been evident that AWS Lambda use cases with a bunch of prerequisites. Experimentation and iterations are required to determine what works best in your image. It's important to remember that AWS Lambda types aren’t a brand-new technology, though its potential is rapidly becoming apparent.

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Also Read: AWS Lambda Cold Start Problem

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