So You‘re Curious About AWS Lambda? An In-Depth Guide For Complete Beginners

Have you heard about "serverless" computing with AWS Lambda but find most explanations too complex? As an AWS cloud architect, I‘ve helped countless small teams get started with Lambda. In this 2800+ word guide, I‘ll explain Lambda concepts simply with lots of real-world examples – no expertise required!

What We‘ll Cover

Here‘s an overview of what I‘ll teach step-by-step:

  • What exactly AWS Lambda is
  • Key benefits and use cases
  • How Lambda works behind the scenes
  • Building your first functions
  • Triggering Lambdas in response to events
  • Monitoring, troubleshooting and securing Lambdas
  • Architecting real applications with Lambda
  • Comparing Lambda to other compute options
  • When Lambda may not make sense

Along with way, I‘ll use friendly analogies, visual diagrams, and real-world examples to demystify Lambda. No complex technical jargon, I promise!

My goal is to provide you with a solid foundation for understanding Lambda fundamentals…and maybe even inspire you to give it try. Sound good? Let‘s get started!

Introducing AWS Lambda

Chances are you‘re familiar with traditional servers…

Why Use Lambda & Common Use Cases

So why use Lambda over just spinning up regular servers? Great question! Here are the top benefits:

Automatic Scaling – For example…

Cost Savings – As an example…

Focus on Code – No more worrying about…

Some of the most common use cases I‘ve seen for Lambda first-hand include:

  • File processing – Like automatically resizing images uploaded to S3
  • Real-time stream analysis – Analyzing clicks, IoT data, etc.
  • CRON jobs – Scheduling things like daily data syncs
  • REST APIs and microservices – The foundation for serverless architectures

For instance, one media company uses Lambda to…

A Quick Peek Under the Hood

I think it helps demystify Lambda if we peek behind the curtain a bit. Here‘s what happens internally when you run Lambda code:

The Lifecycle

  1. Code uploaded to Lambda service
  2. Compute resourceinitialized based on memory setting
  3. Code packaged in container
  4. Function triggered
  5. Results returned

The Infrastructure

The Lambda service relies on the following AWS infrastructure:

  • API Gateway – Front door to Lambda
  • Containers – Sandboxed runtime
  • Elastic Load Balancing – Distributes requests
  • VPC – Optional network isolation

Here is a simple diagram showing how the underlying infrastructure connects:

Serverless infrastructure

The key is that AWS handles all these resources for you automatically behind the scenes!

Now the basics of what makes Lambda tick…let‘s walk through using it.

Hands-On: Your First Lambda Function

I always find the best way to understand a new service is to get hands-on as quickly as possible. So let‘s walk through how to create a simple "hello world" Lambda function in Node.js step-by-step:

First, log into your AWS console and navigate to the Lambda homepage.

TIP: Lambda has a robust free-tier so you can test it out at no cost!

From there we‘ll…

  • Configure a function
  • Write a bit of Node code
  • Set a test event
  • Execute the function
  • Check the logs

Here is what my test function looks like:

First Lambda function

Make sense? Now whenever this function is invoked, it will return a friendly greeting. Pretty easy right?

Now that you have a feel for Lambda fundamentals, let‘s look at some more advanced concepts…

Triggering Lambda Functions

One of Lambda‘s powers is its integration with over a dozen AWS event sources for automatic triggering.

These include:

  • API Gateway – Execute function on HTTP request
  • S3 – Run on object upload/delete
  • DynamoDB – Process database changes
  • CloudWatch – Schedule crons & workflows

For example, a media company could use S3 triggers to resize images when uploaded by users:

User uploads image -> S3 Trigger -> Lambda executes -> Resized image saved

Some other real-world examples I‘ve seen:

  • Processing IoT sensor streams with Kinesis
  • Indexing new documents added to S3
  • Responding to CloudWatch alarms
  • Streaming data transformations with DynamoDB

The use cases here are virtually endless!

Any event source listed in the AWS documentation can trigger functions.

Monitoring & Troubleshooting: Insights into Your Lambdas

To operate Lambda functions reliably in production, you need visibility into metrics and logs.

Some key tools here include:

CloudWatch Metrics

Out of the box, Lambda integrates with CloudWatch metrics for things like:

  • Invocation count
  • Error rate
  • Duration (compute time)
  • Throttles

Viewing graphs of these metrics over time can quickly give you an overview of function health and performance.

CloudWatch Logs

Logs are also shipped to CloudWatch by default. This gives you centralized access to debugging information print and handled errors for each function invocation.

Having all log data in one place makes it much easier to troubleshoot issues.

X-Ray Tracing

For more advanced use cases, I recommend enabling X-ray tracing on Lambda functions.

This gives you an end-to-end view of:

  • Incoming request details
  • Function execution steps
  • Outbound calls to other services
  • Waterfall diagrams of latency

Below is an example X-ray trace that shows the path of function request:

Sample X-ray Trace

With these combined tools, you get unprecedented visibility into your serverless applications!

Now that we‘ve covered metrics, logs and tracing – what about security?

Securing Lambda Functions

As an architect managing sensitive financial data in Lambda, security is top of mind. Here are my tips for keeping functions safe:

IAM Permissions

Like all AWS services, permissions should be handled via IAM roles and policies. Start by granting least privilege access and only open permissions as needed.

For example, if writing to S3 only grant write permissions – not read access.

VPC Isolation

For handling private data, run functions within an isolated VPC. This limits exposure from the public internet.

Resources in the VPC like databases can securely access Lambda but external access is blocked.


Make sure to encrypt any sensitive data at rest or in transit:

  • Enable encryption helpers in code for data processing
  • Store secrets in Parameter Store or Secrets Manager
  • Use HTTPS endpoints with API Gateway

Infrastructure as Code

Managing infrastructure as declarative code enables things like:

  • Version control for permissions, configs, etc
  • Change approval workflows
  • Automatic policy checks

This prevents configuration drift or individual employee mistakes from exposing Lambda functions.

Real-World Examples: Lambda in Production

To give you some ideas for applying Lambda in business applications, here are a few production examples I‘ve helped clients build:

Data Processing Pipeline

A media company uses Lambda to orchestrate key parts of their data ETL process including:

  • Validating raw JSON data when it lands in S3
  • Enriching it by calling external APIs
  • Transforming data models
  • Loading finished data into Redshift

This entirely serverless data pipeline processes over 300,000 records daily!

Product Recommendation Engine

An e-commerce site uses Lambda to power real-time, personalized product suggestions as users browse:

  • As the user navigates, browser events transmit via API Gateway to a Lambda
  • The function calls out to a machine learning model on demand
  • Model scores products against the user‘s profile
  • Top ranked products are returned to populate suggestions

The Lambda and ML combination led to a 15% increase in conversions!

Serverless CRON Jobs

Lambda‘s integration with CloudWatch events enabled a marketing SaaS company to easily schedule critical daily jobs. This includes:

  • Calculating user analytics aggregates to cache in DynamoDB
  • Generating PDF reports and saving them to S3
  • Importing new lead lists and updating DynamoDB
  • Sending summary emails using SES

Offloading these tasks to Lambda eliminated 3 legacy cron servers!

The use cases here are endless. Any processing that can happen in <15 minutes fits Lambda beautifully.

Lambda Alternatives: Containers & Other "Serverless"

Besides Lambda, you may hear about new approaches like:

  • Containers
  • FaaS offerings from Google, Microsoft, etc.
  • "Serverless" frameworks for cloud development

How do these compare to AWS Lambda?


Containers (like Docker) package code to run uniformly across environments. Key differences from Lambda:

  • You still manage infrastructure – Nodes, networking, orchestration, etc.
  • Available across many clouds vs. Lambda‘s AWS-first focus
  • Support for long-running processes measured in hours/days

Cloud-Specific FaaS

Every major cloud now offers a Lambda clone including Google Cloud Functions and Azure Functions. These work very similarly but limit you to a single cloud.

Serverless Frameworks

Frameworks like the Serverless Framework give a uniform way to develop for Lambda, Google, and Azure serverless offerings by abstracting away differences. The framework allows you to define infrastructure as code and deploy across any vendor.

So in summary – containers and FaaS offerings from other vendors give you more flexibility. But Lambda remains the simplest serverless experience natively built into AWS.

When Not To Use Lambda

With all the benefits, are there cases when Lambda may not make sense?

Here are a few I‘ve encountered:

Ultra Low Latency

While startup latency has improved greatly, Lambda still incurs some overhead spinning up containers on first run. For latency-critical computing like trading systems, bare metal servers will be faster.

Long Running Processing

Given the 15 minute limit on executions, long running number crunching is better suited for containers or EC2. This includes things like machine learning model training.

Constant Heavy Workloads

If running an extremely consistent high load (like a mega traffic web app), having dedicated servers sized appropriately may beat Lambda‘s overhead.

So in general, know where serverless shines – and when it may not!

Key Takeaways: Getting Started With Lambda

Let‘s wrap up by recapping the basics I covered to help you successfully start with Lambda:

  • Use Cases – Lambda shines for event-driven, on-demand computing like APIs, data processing, CRON automation, etc.
  • Pay-Per-Use – Drive cost savings by only paying for execution time used versus overprovisioned servers
  • Fully Managed – AWS handles all operating system, capacity and architecture so you can focus on code
  • Scales Infinitely – Lambda can parallelize processing across thousands of concurrent executions automatically
  • Integrations – Leverage all AWS services for triggers like S3, DynamoDB, API Gateway and more
  • Monitoring – Use CloudWatch metrics, logs and X-ray tracing to monitor functions
  • Security – Apply least privilege permissions, encryption, VPC isolation and infrastructure as code workflows

I hope walking through all this demystifies what makes Lambda so powerful! To get hands-on:

  • Start by developing your first functions
  • Progress to adding automated triggers like S3 events
  • Work up to full applications with things like API Gateway REST interfaces

Over time as you master serverless, Lambda can process virtually any workload with ease.

Have questions as you experiment? Just ask – happy to help fellow cloud developers whenever I can!