Explain the Features of AWS Lambda

This recipe explains what the Features of AWS Lambda

Recipe Objective - Explain the Features of AWS Lambda?

The AWS Lambda is a widely used service and is defined as an event-driven, serverless computing platform provided by Amazon as part of Amazon Web Services. It is further defined as a computing service that runs code in response to events and automatically manages the computing resources required by that code. The AWS Lambda was introduced in November 2014. The AWS Lambda officially supports Node.js, Python, Java, Go, Ruby, and C# (through . NET) as of 2018. The AWS Lambda supports running native Linux executables via calling out from a supported runtime such as Node.js for example Haskell code which can be run on Lambda. The AWS Lambda was designed for use cases such as the image or object uploads to Amazon S3, updates to DynamoDB tables, responding to website clicks, or reacting to sensor readings from an IoT connected device. The AWS Lambda can also be used to automatically provision the back-end services triggered by custom HTTP requests, and "spin down" such services when not in use, to save resources and further these custom HTTP requests are to be configured in the AWS API Gateway, which can also handle authentication and authorization in conjunction with AWS Cognito. The AWS Lambda automatically responds to the code execution requests at any scale, from a dozen events per day to the hundreds of thousands per second.

Benefits of AWS Lambda

  • The AWS Lambda helps in executing code at the capacity you need as specified. It can be scaled to match the data volume automatically enabling custom event triggers and can process data at scale. The AWS Lambda can be combined with other AWS services to create secure, stable, and scalable online experiences to run interactive web and mobile backends. The AWS Lambda can preprocess the data before feeding it to the machine learning (ML) model and with Amazon Elastic File System (EFS) access, AWS Lambda handles the infrastructure management and provisioning to simplify scaling that is it enables powerful ML insights. The AWS Lambda builds event-driven functions for easy communication between the decoupled services and further reduce costs by running applications during times of peak demand without the crashing or over-provisioning resources that is it creates event-driven applications.

System Requirements

  • Any Operating System(Mac, Windows, Linux)

This recipe explains AWS Lambda and the features of AWS Lambda.

Features of AWS Lambda

    • Allows users to add custom logic to AWS, therefore, extending other AWS services with custom logic

AWS Lambda allows users to add the custom logic to Amazon Web Services and further extend the other AWS services with the custom logic.

    • Helps in building custom backend services

AWS Lambda offers services in building custom backend services for the users.

    • Provides complexity automated administration

AWS Lambda offers complex automated administration for the users.

    • Packages and deploys functions as the container images

AWS Lambda offers services for packaging and deploying functions as container images for the users.

    • Provides built-in fault tolerance

AWS Lambda offers a built-in fault tolerance for users.

    • Provides support of connection to relational databases.

AWS Lambda offers support of connection to the relational databases for the users.

    • Runs code in response to the Amazon CloudFront requests

AWS Lambda offers services to run code in response to the Amazon CloudFront.

    • Provides an integrated security model

AWS Lambda offers services that provide a security model for the users.

    • Provides a flexible resource model

AWS Lambda offers services that provide a flexible resource model.

    • Provides integration with various operational tools

AWS Lambda offers services that provide integration with the various operational tools.

What Users are saying..

profile image

Ed Godalle

Director Data Analytics at EY / EY Tech
linkedin profile url

I am the Director of Data Analytics with over 10+ years of IT experience. I have a background in SQL, Python, and Big Data working with Accenture, IBM, and Infosys. I am looking to enhance my skills... Read More

Relevant Projects

Deploy an Application to Kubernetes in Google Cloud using GKE
In this Kubernetes Big Data Project, you will automate and deploy an application using Docker, Google Kubernetes Engine (GKE), and Google Cloud Functions.

Hive Mini Project to Build a Data Warehouse for e-Commerce
In this hive project, you will design a data warehouse for e-commerce application to perform Hive analytics on Sales and Customer Demographics data using big data tools such as Sqoop, Spark, and HDFS.

Python and MongoDB Project for Beginners with Source Code-Part 2
In this Python and MongoDB Project for Beginners, you will learn how to use Apache Sedona and perform advanced analysis on the Transportation dataset.

Build a Scalable Event Based GCP Data Pipeline using DataFlow
In this GCP project, you will learn to build and deploy a fully-managed(serverless) event-driven data pipeline on GCP using services like Cloud Composer, Google Cloud Storage (GCS), Pub-Sub, Cloud Functions, BigQuery, BigTable

Azure Data Factory and Databricks End-to-End Project
Azure Data Factory and Databricks End-to-End Project to implement analytics on trip transaction data using Azure Services such as Data Factory, ADLS Gen2, and Databricks, with a focus on data transformation and pipeline resiliency.

GCP Project to Learn using BigQuery for Exploring Data
Learn using GCP BigQuery for exploring and preparing data for analysis and transformation of your datasets.

GCP Data Ingestion with SQL using Google Cloud Dataflow
In this GCP Project, you will learn to build a data processing pipeline With Apache Beam, Dataflow & BigQuery on GCP using Yelp Dataset.

Talend Real-Time Project for ETL Process Automation
In this Talend Project, you will learn how to build an ETL pipeline in Talend Open Studio to automate the process of File Loading and Processing.

Hadoop Project-Analysis of Yelp Dataset using Hadoop Hive
The goal of this hadoop project is to apply some data engineering principles to Yelp Dataset in the areas of processing, storage, and retrieval.

Build a Data Pipeline with Azure Synapse and Spark Pool
In this Azure Project, you will learn to build a Data Pipeline in Azure using Azure Synapse Analytics, Azure Storage, Azure Synapse Spark Pool to perform data transformations on an Airline dataset and visualize the results in Power BI.