AWS Lambda Python Integration: 2023’s Complete Guide to Get Started

Last Modified: February 22nd, 2023

AWS Lambda Python - Featured Image | Hevo Data

Most companies are modernizing and are preferring Serverless applications for a number of reasons. Serverless applications are flexible and easy to develop due to the simplicity of their design. AWS Lambda is a Function-as-a-Service (FaaS) offering from Amazon Web Services (AWS) that provides on-demand execution of code without the need for an always-available server. This article discusses using Python with AWS Lambda for going Serverless.

AWS Lambda Python integration makes it easy for Developers to work with Serverless functions. A FaaS system like AWS Lambda manages, handles, and maintains the infrastructure details for the web applications. This means that users need not worry about the infrastructure side of things. This blog will help you set up AWS Lambda Python integration to use for your next project. But beforehand let’s discuss this Serverless service in brief.

Table of Contents

What is AWS Lambda?

AWS Lambda Python - Lambda Logo | Hevo Data
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AWS Lambda is a Serverless computing service offered by Amazon that allows users to run their applications or execute the codes without the management of servers. Developed in 2014, AWS Lambda reduces the efforts, resources, and expenses associated with buying, maintaining, and replacing the infrastructure and hardware. This, in turn, improves the efficiency of Cloud-based operations.

The lambda function allows users to run application codes or any backend service virtually with zero administration. As Lambda executes codes based on the requirement, it can scale automatically from a few requests per day to 1000s of requests per second. It further enables users to trigger Lambda functions over 200 AWS services and Software-as-a-Service (Saas) applications. On top of that, AWS Lambda offers users the flexibility to pay only for the computation they use.

Key Features of AWS Lambda

  • Reduced Expenses: As discussed already, users need not worry about the infrastructure side of things and they need to pay only for the resources they use and any network traffic. The pay-as-you-go model saves users from additional costs with respect to unused minutes, storage, etc.
  • Concurrency and Scaling Controls: Concurrency and Scaling Controls such as concurrency limits and provisioned concurrency give users granular control over the scaling and responsiveness of production applications.
  • Automatic Scalability: AWS Lambda offers high scalability as its Serverless architecture allows it to accommodate unexpected increases. 
  • Integration with Products: AWS Lambda integrates with various AWS services such as DynamoDB, API Gateway, S3, and many more to develop functional applications.
  • Code Signing: Code Signing in Lambda provides users with trust and integrity controls. This is essential in order to make sure that only unaltered code published by approved Developers is deployed in your Lambda functions.

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What is Python?

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Python is a high-level, general-purpose programming language designed for Web Development, Software Development, Machine Learning, and more. It emphasizes code readability and its language constructs and the Object-oriented approach helps programmers in writing logical codes for small and large-scale projects with ease.

It is one of the world’s fastest-growing programming languages used by Software Developers, Data Analysts, Scientists, Students, and Accountants. Python finds its use in various industries and companies from Artificial Intelligence to Data Science.

Key Features of Python

  • Easy to Code: Python is popular for its simplicity. It is very easy to learn when compared to other languages like C, C++, Java, etc. Even people with no technical background can learn the basics of python in a few hours or days.
  • Object-Oriented Language: Python supports Object-Oriented Programming and hence the implementation of classes, objects, inheritance, encapsulation, etc.
  • Interpreted Language: Python is an interpreted language as it comes with the IDLE (Interactive Development Environment) interpreter. It further follows the REPL (Read-Evaluate-Print-Loop) structure. It executes the code line by line and displays the output simultaneously.
  • Open-Source: The biggest advantage of Python is that it’s open-source and its large community is always contributing to enhancing it further. It is available for all to use for free.
  • Extensible Feature: Python is an extensible language allowing users to write and compile Python code in C or C++ language as well.

Why Lambda With Python?

Python is one of the most beloved programming languages because of its simplicity and other extensible features. On top of that, Python finds its applications in almost every industry, from Web Development to Data Science. Hence, it is quite natural that most Software Developers would like to stick around Python when working with their Serverless functions.

Now that you’re familiar with Lambda and Python, let’s dive straight into the AWS Lambda Python integration.

How to Build AWS Lambda Python Function?

If you want your app to be immensely popular, you must get it online first. Assuming that you want people all over the world to use your app, you need to think in terms of scalability, resiliency, and many other factors. Well, in simple terms, you just need to go Serverless. A Serverless architecture also saves you from paying extra for underutilized resources. With Lambda, you pay only for the resources you use and the related execution costs.

It’s now time to start writing a Lambda function. Let’s get started with AWS Lambda Python deployment.

Create a Function

For the purpose of this demonstration, the Serverless framework is being used to create a Lambda function.

  • You must have Node.js installed on your system to implement the Serverless framework. 
  • To install Serverless, run this command from the command line.
npm i -g serverless
  • Initialize your project by running this command.
  • Provide the required details and proceed with the setup.
  • You’ll now have 2 files in a folder that are of importance:
    • serverless.yml

The file has your code and the serverless.yml file is where the Lambda function is configured.

  • Serverless will now create some boilerplate code with a single function with the following signature.
def hello(event, context):
    # return an object with a statusCode and body

As seen there are 2 parameters: an event and a context parameter. The event parameter contains the body that was passed to the Lambda function along with other necessary details like HTTP headers. The context parameter on the other hand contains some interesting information about the invocation and environment.

  • Now modify this code based on your requirements. For instance, let’s validate an Email address.
import json
import re
def validate(event, context):
    event_body = json.loads(event['body'])
    email_regex = re.compile('^(([^<>()[].,;:s@"]+(.[^<>()[].,;:s@"]+)*)|(".+"))@(([[0-9]{1,3}.[0-9]{1,3}.[0-9]{1,3}.[0-9]{1,3}])|(([a-zA-Z-0-9]+.)+[a-zA-Z]{2,}))$')
    matches = email_regex.match(event_body['email']) != None
    response = {
        'statusCode': 200,
        'body': json.dumps({ 'result': matches })
    return response

The body is parsed from the event parameter and then it is checked if it’s an Email address.

Configure the Lambda Function

As the code and function are modified, the serverless.yml needs to be configured as well.

Let’s have a look at the serverless.yml file.

service: email-validator
  name: aws
  runtime: python3.8
    handler: handler.hello

Here, you need to give a name to the service, specify which runtime to use, give a name to the functions, and specify which Python function to execute.

Let’s modify the function part from the above code.

    handler: handler.validate

You can now test the function.

Take Lambda Function for a Spin (Test Locally)

The Serverless framework makes it easy to test a Lambda function locally. You just need to run this command to invoke the function.

serverless invoke local --data '{"body": "{"email": ""}"}' --function main

The result is displayed as.

    "statusCode": 200,
    "body": "{"result": true}"

To be sure, you can run this command with an invalid Email address. For an invalid Email address, the result will be displayed as false.

With this, you’re now all set to deploy the function to AWS, the last step of AWS Lambda Python integration.

Deploying to AWS

It’s now time for the AWS Lambda Python deployment. To do so, you will need to give Serverless access to your AWS account.

  • In the AWS Console, go to “Identity and Access Management (IAM)” and click on the “Users” section.
  • To create a new user, click on the “Add user” button located at the top of the page.
AWS Lambda Python: Add User | Hevo Data
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  • Give the user a name and Programmatic access.
AWS Lambda Python: Programmatic access | Hevo Data
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  • Proceed and click on the “Attach existing policies directly” button. Provide access to the following AWS services:
    • Lambda
    • IAM
    • S3 (for storing Lambda code)
    • CloudFormation

Your permissions should look like this.

AWS Lambda Python: Permissions | Hevo Data
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  • Continue and click on “Confirm” to create your user. You can take a note of the ID and secret as they will be used later as well.
AWS Lambda Python: Access key ID | Hevo Data
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  • Now, you need to configure Serverless for it to use these credentials.
serverless config credentials --provider aws --key AKIA52XT4ILHYF5UR54E --secret xxxxxxxxx
  • With that done, just run the following command to deploy your Serverless code to AWS.
serverless deploy

The AWS Lambda Python function is now up in the Cloud and you can test it from inside the AWS Console.

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Lambda Function Handler in Python

The Lambda Function Handler is basically a method in your function code that processes events. Lambda runs the handler method whenever the function is invoked. The handler becomes available to handle another event after it exits or returns a response.

Following is the general syntax for creating a function handler in Python.

def handler_name(event, context): 
    return some_value

The Lambda runtime passes 2 arguments to the function handler whenever the function handler is invoked:

  • The first argument is the event object. As discussed already, an event contains data (or body) passed to a Lambda function to process. The event is passed to your function code only after the Lambda runtime converts it to an object. It is usually of the Python dict type but it can also be of a list, int, float, or str type.
  • The second argument is the context object. Lambda passes a context object to your function at runtime. This object contains information about the invocation, function, and runtime environment.

Error Handling in Python Lambda Function

Run the following code to see how to handle errors in Python.

def error_handler(event, context):
   raise Exception('Error Occured!')
AWS Lambda Python: Error Handling in Python | Hevo Data
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This brings us to the end of AWS Lamda Python integration, the article covered all the important aspects with respect to AWS Lambda Python.


With every organization moving most of its business into the cloud, it is essential to implement Serverless applications for various reasons. Going Serverless is the need of the hour keeping in mind the scalability, security, and resiliency. And AWS Lambda Python integration is here to do just that. In fact, an AWS Lambda Python integration streamlines the entire process and makes it easy for Developers to work with their Serverless functions.

This article introduced you to AWS Lambda and Python and helped you in the deployment of the AWS Lambda Python function. However, it’s easy to become lost in a blend of data from multiple sources. Imagine trying to make heads or tails of such data. This is where Hevo comes in.

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Share your experience of AWS Lambda Python deployment in the comments section below.

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