Top 50+ AWS Services Explained in 10 Minutes

Summary

This informative video script delves into Amazon Web Services (AWS), starting from its 2006 launch with three products to its current expansive suite of over 200 services. It covers a range of topics from robotics simulation with RoboMaker and IoT Core, to quantum computing with Braket. The script explains foundational services like EC2 and introduces higher-level abstractions such as Elastic Beanstalk and serverless computing with Lambda. It also explores databases, analytics, machine learning, and essential tools for developers, emphasizing ease of use and the vast capabilities of AWS in cloud computing.

Takeaways

• 🚀 Amazon Web Services (AWS) has expanded from three products in 2006 to over 200 services today, catering to a wide range of developer needs.
• 🤖 Specialized services like RoboMaker, IoT Core, and Ground Station cater to developers working on robotics, IoT devices, and satellite data connectivity.
• 🧪 AWS Bracket offers access to quantum computing, allowing users to experiment with this emerging technology.
• 💻 The foundational EC2 (Elastic Compute Cloud) service allows users to create virtual computers in the cloud with customizable specs and pay-per-second pricing.
• 🔄 Elastic Load Balancing and Auto Scaling services work together to distribute traffic and scale resources based on demand, optimizing application performance and cost.
• 📦 Elastic Beanstalk simplifies the deployment process by providing a PaaS (Platform as a Service) layer that abstracts away much of the infrastructure management.
• 🌟 AWS Lambda introduced serverless computing, where users only pay for the compute time and requests used, without managing servers.
• 🛳 AWS Outposts allows enterprises to run AWS services on their own infrastructure, integrating with existing systems without discarding legacy hardware.
• 📦 S3 (Simple Storage Service) is the original storage offering by AWS, designed for general-purpose file storage with high durability and availability.
• 🔍 Amazon Redshift is a data warehouse service that enables large enterprises to consolidate and analyze data from various sources.
• 🤖 AWS SageMaker facilitates the building, training, and deployment of machine learning models, providing managed Jupyter notebooks and GPU instances for developers.

Q & A

When did Amazon Web Services (AWS) initially launch and what products did it start with?

Amazon Web Services launched in 2006 with three products: storage buckets, compute instances, and a messaging queue.

How many services does AWS offer as of the video's current date, and what is the comparison made to describe the variety?

As of the video's current date, AWS offers over 200 services, which is compared to shopping at a big grocery store with different aisles of product categories.

What AWS service is mentioned for simulating and testing robots at scale?

RoboMaker is the AWS service mentioned for simulating and testing robots at scale.

How does AWS support the management of robots in people's homes?

AWS supports the management of robots in people's homes through IoT Core, which allows for data collection, software updates, and remote management.

What AWS service can be used to connect data from a satellite orbiting Earth?

AWS Ground Station is the service that can be used to connect data from a satellite orbiting Earth.

What is the name of the service that allows interaction with a quantum computer for future computing research?

The service that allows interaction with a quantum computer for future computing research is called Braket.

What is the fundamental building block of AWS for creating a virtual computer in the cloud?

Elastic Compute Cloud (EC2) is the fundamental building block of AWS for creating a virtual computer in the cloud.

What AWS service was introduced in 2009 to distribute traffic across multiple instances automatically?

Elastic Load Balancing was introduced in 2009 to distribute traffic across multiple instances automatically.

How does AWS Elastic Beanstalk simplify the deployment of applications?

Elastic Beanstalk simplifies deployment by providing a template-based approach where developers can deploy their code and let auto-scaling happen automatically.

What is the AWS service that allows serverless deployment of containerized applications?

AWS Fargate is the service that allows serverless deployment of containerized applications.

What is the first product offered by AWS for storing any type of file or object?

The first product offered by AWS for storing any type of file or object is Simple Storage Service (S3).

What AWS service is designed for building and managing relational SQL databases?

Amazon Relational Database Service (RDS) is designed for building and managing relational SQL databases.

What AWS service is mentioned for creating data lakes to store a large amount of unstructured data?

AWS Lake Formation is the service mentioned for creating data lakes to store a large amount of unstructured data.

What AWS service is used for analyzing real-time data streams?

Amazon Kinesis is used for analyzing real-time data streams.

What is the serverless product in AWS that simplifies data extraction, transformation, and loading?

AWS Glue is the serverless product that simplifies data extraction, transformation, and loading.

What AWS service is mentioned for machine learning model building and deployment?

Amazon SageMaker is the service mentioned for machine learning model building and deployment.

What AWS tool is used for managing security rules and access on an AWS account?

AWS Identity and Access Management (IAM) is used for managing security rules and access on an AWS account.

What service in AWS helps with user authentication and session management for web or mobile apps?

Amazon Cognito helps with user authentication and session management for web or mobile apps.

What AWS service can be used to send push notifications to app users?

Amazon Simple Notification Service (SNS) can be used to send push notifications to app users.

What is the AWS service for sending emails to users?

Amazon Simple Email Service (SES) is the service for sending emails to users.

How can developers provision and manage AWS services using templates?

Developers can use AWS CloudFormation to create templates based on their infrastructure in YAML or JSON, enabling the provisioning of multiple services with a single click.

What AWS service provides SDKs for connecting front-end applications to cloud infrastructure?

AWS Amplify provides SDKs for connecting front-end applications to cloud infrastructure.

What is the importance of using AWS Cost Explorer and Budgets mentioned in the video?

AWS Cost Explorer and Budgets are important for managing and monitoring costs associated with AWS services to avoid unexpected expenses.

Outlines

00:00

🚀 AWS Evolution and Advanced Services Overview

Amazon Web Services (AWS) has expanded from its initial launch in 2006 with three products to offering over 200 services today. The script discusses the complexity of AWS's service offerings, which can be daunting due to their variety and overlapping functionalities. It highlights advanced services such as RoboMaker for robot simulation, IoT Core for smart device management, Ground Station for satellite connectivity, and Braket for quantum computing experimentation. The paragraph also touches on more practical services like Elastic Compute Cloud (EC2) for virtual server deployment, Elastic Load Balancing for traffic distribution, and Auto Scaling for dynamic resource allocation. The introduction of Elastic Beanstalk and Lightsail provides easier deployment options, while Lambda represents a serverless computing approach, only charging for the exact usage.

05:01

🛠 AWS Compute and Storage Solutions

This paragraph delves into AWS's compute options, starting with the foundational EC2 for virtual computing needs and moving on to serverless computing with AWS Lambda. It also covers Outpost for running AWS services on-premises and Snowball devices for data transfer in remote locations. For containerization, the script mentions Elastic Container Registry and Elastic Container Service (ECS), as well as EKS for Kubernetes management and Fargate for serverless container execution. App Runner is highlighted as a simplified deployment option for containerized applications. On the storage front, AWS offers Simple Storage Service (S3) for general file storage, Glacier for archival, Elastic Block Store for high-performance needs, and Elastic File System for managed file storage solutions. The paragraph also introduces various database services, from the original SimpleDB to the scalable DynamoDB, DocumentDB, and RDS, with its proprietary SQL variant, Aurora, and serverless option.

10:01

🔍 Exploring AWS Analytics and Machine Learning Capabilities

The script explores AWS's suite of analytics and machine learning tools, starting with data storage options like Redshift for data warehousing and Lake Formation for unstructured data lakes. It discusses real-time data processing with Kinesis and alternatives like Apache Kafka via Amazon MSK. Glue is presented as a serverless data integration service. For machine learning, the script covers AWS's offerings like SageMaker for building ML models and various APIs for image and speech recognition. It also mentions Lex for conversational AI and the DeepRacer device for learning ML through a racing car game. The paragraph concludes with a brief mention of other essential developer tools like IAM for security, Cognito for authentication, SNS for notifications, SES for emails, CloudFormation for infrastructure management, and Amplify for front-end integration.

🛡️ AWS Security and Cost Management

The final paragraph focuses on AWS's security and cost management tools. It starts with IAM, which allows for the creation of access control rules within an AWS account. Cognito is highlighted for its ability to manage user authentication and sessions across web and mobile apps. The script then touches on SNS for push notifications and SES for email services. CloudFormation is introduced as a way to provision infrastructure through templates, and Amplify is mentioned for connecting front-end applications to AWS services. The video concludes with a reminder of the financial implications of using AWS services and the importance of using AWS Cost Explorer and Budgets to manage expenses effectively.

Keywords

💡Amazon Web Services (AWS)

AWS is a comprehensive cloud computing platform launched by Amazon in 2006. It offers a wide range of services, from computing and storage to machine learning and analytics. In the video, AWS is the central theme, as it discusses the evolution from three initial products to over 200 services, highlighting its vast array of offerings for developers and businesses.

💡Elastic Compute Cloud (EC2)

EC2 is a foundational AWS service that allows users to create virtual computers in the cloud, choosing the operating system, memory, and computing power as needed. It is pivotal for developers looking to deploy applications, as it provides the flexibility to scale resources up or down. The script mentions EC2 as one of the original AWS products, emphasizing its importance in cloud computing.

💡Elastic Load Balancing

This AWS service automatically distributes incoming traffic across multiple EC2 instances, ensuring that no single instance is overwhelmed and improving the fault tolerance of applications. The video script discusses Elastic Load Balancing as a solution to distribute traffic as an application grows, enhancing the scalability and reliability of web applications.

💡Elastic Beanstalk

Elastic Beanstalk is a PaaS (Platform as a Service) offering from AWS that simplifies the deployment and scaling of web applications. It abstracts away much of the infrastructure management, allowing developers to focus on their code. The script describes Elastic Beanstalk as a service that makes deploying applications like Ruby on Rails much easier by handling the auto-scaling processes automatically.

💡AWS Lambda

Lambda is a serverless computing service that allows developers to run code without provisioning or managing servers. It executes code in response to events and automatically scales the application. The video mentions Lambda as a way to upload code and have it run in response to specific events, with AWS handling the underlying infrastructure.

💡Elastic Container Service (ECS)

ECS is a service that supports the deployment, management, and scaling of containerized applications using Docker containers. It simplifies the process of running containers on AWS by providing an API to start, stop, and allocate virtual machines to the containers. The script refers to ECS as a tool for running containers, highlighting its role in modern application deployment strategies.

💡Elastic File System (EFS)

EFS is a managed service that provides a simple, scalable, and fully compatible file storage system for use with AWS cloud services. It offers high performance and is ideal for applications that require a shared file system. The video script positions EFS as a fully managed, high-performance file storage solution for applications that demand more than basic file storage capabilities.

💡Amazon RDS

Amazon RDS is a managed database service that supports various SQL database engines, simplifying the process of setting up, operating, and scaling a relational database. It handles time-consuming tasks such as hardware provisioning, database setup, patching, and backups. The script mentions RDS as a service that supports different SQL flavors and manages database operations, making it a go-to choice for relational database needs.

💡Amazon Aurora

Aurora is a proprietary SQL database offered by AWS that is compatible with PostgreSQL and MySQL. It provides high performance, scalability, and is designed to be cost-effective. The video script introduces Aurora as an AWS-specific SQL database option that can be operated with better performance and at a lower cost than traditional SQL databases.

💡AWS Glue

AWS Glue is a serverless data integration service that makes it easy to discover, prepare, and combine data for analytics, machine learning, and application development. It can automatically extract, transform, and load data without requiring code. The script describes Glue as a tool that simplifies the process of working with data, allowing users to create jobs without writing source code.

💡Amazon SageMaker

SageMaker is a fully managed service that provides developers and data scientists the ability to build, train, and deploy machine learning models quickly. It removes the heavy lifting of setting up and managing the infrastructure required for machine learning tasks. The video script highlights SageMaker as a service that simplifies the process of building and deploying machine learning models with popular frameworks like TensorFlow or PyTorch.

Highlights

Amazon Web Services (AWS) launched in 2006 with three products and has expanded to offer over 200 services.

AWS services are diverse, with some catering to niche markets like robotics simulation with RoboMaker, IoT device management with IoT Core, satellite data connectivity with Ground Station, and quantum computing with Braket.

Elastic Compute Cloud (EC2) is a foundational AWS service allowing users to create virtual computers in the cloud with customizable operating systems, memory, and computing power.

Elastic Load Balancing was introduced in 2009 to automatically distribute traffic across multiple EC2 instances.

CloudWatch collects logs and metrics from EC2 instances, which can be utilized by Auto Scaling to adjust instance numbers based on traffic and utilization.

Elastic Beanstalk, launched in 2011, simplifies deployment by providing an abstraction layer for auto-scaling and other AWS features.

For users seeking an even simpler deployment process, Lightsail offers point-and-click deployment with minimal configuration.

AWS Lambda, introduced in 2014, enables serverless computing where users only pay for the exact number of requests and computing time used.

The Serverless Application Repository allows users to deploy pre-built functions with a single click.

Outpost allows enterprises to run AWS APIs on their own infrastructure, integrating existing servers without replacement.

AWS offers various storage solutions, including S3 for general file storage, Glacier for archiving, and Elastic Block Storage for intensive data processing.

Elastic File System provides a fully managed, high-performance file storage solution at a higher cost.

AWS provides a range of database services, from SimpleDB to DynamoDB, DocumentDB, and Amazon RDS for SQL databases.

Aurora is a proprietary SQL-compatible database option that offers better performance and lower costs.

Neptune is a graph database optimized for highly connected datasets, such as social graphs or recommendation engines.

Elasticache is a fully managed in-memory database that offers extremely low-latency data delivery.

AWS Lake Formation facilitates the creation of data lakes for storing and analyzing large amounts of unstructured data.

Kinesis and Amazon MSK are services for capturing and processing real-time data streams.

AWS Glue is a serverless data integration service that simplifies the extraction, transformation, and loading of data.

SageMaker enables users to build, train, and deploy machine learning models with popular frameworks like TensorFlow or PyTorch.

AWS provides pre-built machine learning services like the Recognition API for image analysis and Lex for conversational bots.

AWS DeepRacer is an educational device that allows users to experiment with machine learning through autonomous racing.

AWS Identity and Access Management (IAM) allows users to create access rules and manage permissions within AWS accounts.

Amazon Cognito simplifies user authentication and session management for web and mobile apps.

Amazon SNS and SES are services for delivering push notifications and emails to app users, respectively.

AWS CloudFormation allows users to create and manage infrastructure templates in YAML or JSON, enabling the deployment of multiple services with a single action.

AWS Amplify provides SDKs for connecting front-end applications to AWS services from various frameworks.

AWS Cost Explorer and Budgets are tools for managing and monitoring AWS service costs to avoid unexpected expenses.

Transcripts

amazon web services launched in 2006

with a total of three products

storage buckets compute instances and a

messaging queue

today it offers a mind-numbing 200 and

something services

and what's most confusing is that many

of them appear to do almost the exact

same thing it's kind of like shopping at

a big grocery store where you have

different aisles of product categories

filled with things to buy that meet the

needs of virtually every developer

on the planet in today's video we'll

walk down these aisles to gain an

understanding of over 50 different aws

products so first let's start with a few

that are above my paygrade that you may

not know exist

if you're building robots you can use

robomaker to simulate and test your

robots at scale

then once your robots are in people's

homes you can use iot core to collect

data from them

update their software and manage them

remotely if you happen to have a

satellite orbiting earth you can tap

into amazon's global network of antennas

to connect data through its ground

station service

and if you want to start experimenting

and researching the future of computing

you can use bracket to

interact with a quantum computer but

most developers go to the cloud to solve

more practical problems and for that

let's head to the compute aisle one of

the original aws products was

elastic compute cloud it's one of the

most fundamental building blocks on the

platform and allows you to create a

virtual computer in the cloud

choose your operating system memory and

computing power

then you can rent that space in the

cloud like you're renting an apartment

that you pay for by the second a common

use case is to use an instance as a

server for web application

but one problem is that as your app

grows you'll likely need to distribute

traffic

across multiple instances in 2009 amazon

introduced elastic load balancing which

allowed developers to distribute traffic

to multiple instances

automatically in addition the cloudwatch

service can collect

logs and metrics from each individual

instance the data collected from

cloudwatch can then be passed off to

auto scale

in which you define policies that create

new instances as they become needed

based on the traffic and utilization of

your current infrastructure these tools

were revolutionary at the time

but developers still wanted an easier

way to get things done

and that's where elastic bean stock

comes in most developers in 2011 just

wanted to deploy a ruby on rails app

elastic beanstalk

made that much easier by providing an

additional layer of abstraction

on top of ec2 and other auto scaling

features

choose a template deploy your code and

let all the auto scaling stuff happen

automatically

this is often called a platform as a

service but in some cases

it's still too complicated if you don't

care about the underlying infrastructure

whatsoever

and just want to deploy a wordpress site

lightsail is an alternative option where

you can point and click at what you want

to deploy and worry even less about the

underlying configuration in all these

cases you are deploying a static server

that is always running in the cloud

but many computing jobs are ephemeral

which means they don't rely

on any persistent state on the server so

why bother deploying a server for code

like that

in 2014 lambda came out which are

functions as a service

or serverless computing with lambda you

simply upload your code

then choose an event that decides when

that code should run

traffic scaling and networking are all

things that happen entirely in the

background and unlike a dedicated server

you only pay for the exact number of

requests and computing time that you use

now if you don't like writing your own

code you can use the serverless

application repository

to find pre-built functions that you can

deploy with the click of a button

but what if you're a huge enterprise

with a bunch of its own servers outpost

is a way to run aws apis on your own

infrastructure without needing to throw

your old servers in the garbage

in other cases you may want to interact

with aws from remote or extreme

environments like if you're a scientist

in the arctic snow devices are like

little mini data centers that can work

without internet and hostile

environments

so that gives us some fundamental ways

to compute things but many apps today

are standardized with docker containers

allowing them to run on multiple

different clouds or computing

environments with very little effort

to run a container you first need to

create a docker image and store it

somewhere elastic container registry

allows you to upload an image

allowing other tools like elastic

container service to pull it back down

and run it ecs is an api for starting

stopping and allocating virtual machines

to your containers

and allows you to connect them to other

products like load balancers

some companies may want more control

over how their app scales in which case

eks is a tool for running kubernetes but

in other cases you may want your

containers to behave

in a more automated way fargate is a

tool that will make your containers

behave like serverless functions

removing the need to allocate ec2

instances for your containers but if

you're building an application

and already have it containerized the

easiest way to deploy it to aws

is app runner this is a new product in

2021 where you simply point it to a

container image

while it handles all the orchestration

and scaling behind the scenes

but running an application is only half

the battle we also need to store data in

the cloud

simple storage service or s3 was the

very first product offered by aws

it can store any type of file or object

like an image or video

and is based on the same infrastructure

as amazon's ecommerce site

it's great for general purpose file

storage but if you don't access your

files very often

you can archive them in glacier which

has a higher latency but a much lower

cost

on the other end of the spectrum you may

need storage that is extremely fast and

can handle a lot of throughput

elastic block storage is ideal for

applications that have intensive data

processing requirements

but requires more manual configuration

by the developer now if you want

something that's highly performant and

also fully managed

elastic file system provides all the

bells and whistles but at a much higher

cost in addition to raw files

developers also need to store structured

data for their end users

and that brings us to the database aisle

which has a lot of different products to

choose from

the first ever database on aws was

simpledb

a general purpose no sql database but it

tends to be a little too simple for most

people

everybody knows you never go full

it was followed up a few years later

with dynamodb

which is a document database that's very

easy to scale horizontally

it's inexpensive and provides fast read

performance but it isn't very good at

modeling relational data

if you're familiar with mongodb another

document database option

is documentdb it's a controversial

option that's technically not mongodb

that has a one-to-one mapping of the

mongodb api

to get around restrictive open source

licensing speaking of which

amazon also did a similar thing with

elasticsearch which itself is a great

option if you want to build something

like a

full text search engine but the majority

of developers out there will

opt for a traditional relational sql

database

amazon relational database service rds

supports a variety of different sql

flavors

and can fully manage things like backups

patching and scale

but amazon also offers its own

proprietary flavor of sql called aurora

it's compatible with postgres or mysql

and can be operated with better

performance at a lower cost

in addition aurora offers a new

serverless option that makes it even

easier to scale

and you only pay for the actual time

that the database is in use

relational databases are a great general

purpose option but they're not the only

option

neptune is a graph database that can

achieve better performance on highly

connected data sets

like a social graph or recommendation

engine if your current database is too

slow you may want to bring in

elastic cache which is a fully managed

version of redis an

in-memory database that delivers data to

your end users with extremely low

latency if you work with time series

data

like the stock market for example you

might benefit from time stream

a time series database with built-in

functions for time-based queries

and additional features for analytics

yet another option

is the quantum ledger database which

allows you to build an immutable set of

cryptographically signed transactions

very similar to decentralized blockchain

technology

now let's shift gears and talk about

analytics to analyze data you first need

a place to store it and a popular

option for doing that is redshift which

is a data warehouse that tries to get

you to shift away from oracle

warehouses are often used by big

enterprises to dump multiple data

sources from the business where they can

be analyzed together when all your data

is in one place it's easier to generate

meaningful analytics and run machine

learning on it

data in a warehouse is structured so it

can be queried but if you need a place

to put a large amount of unstructured

data you can use aws

lake formation which is a tool for

creating data lakes or repositories that

store a large amount of

unstructured data which can be used in

addition to data warehouses to query a

larger variety of data sources

if you want to analyze real-time data

you can use kinesis to capture real-time

streams from your infrastructure then

visualize them in your favorite business

intelligence tool

or you can use a stream processing

framework like apache spark

that runs on elastic mapreduce which

itself is a service that allows you to

operate on massive datasets efficiently

with a parallel distributed algorithm

now if you don't want to use kinesis for

streaming data a popular alternative is

apache kafka

it's open source and amazon msk is a

fully managed service to get you started

but for the average developer all this

data processing may be a little too

complicated

glue is a serverless product that makes

it much easier to extract

transform and load your data it can

automatically connect to other data

sources on aws

like aurora redshift and s3 and has a

tool called glue studio

so you can create jobs without having to

write any actual source code but one of

the biggest advantages of collecting

massive amounts of data

is that you can use it to help predict

the future and aws has a bunch of tools

in the machine learning aisle to make

that process easier

but first if you don't have any high

quality data of your own

you can use the data exchange to

purchase and subscribe to data from

third-party sources

once you have some data in the cloud you

can use sagemaker to connect to it

and start building machine learning

models with tensorflow or pi torch

it operates on multiple levels to make

machine learning easier and provides a

managed jupyter notebook that can

connect to a gpu instance to train a

machine learning model then deploy it

somewhere useful

that's cool but building your own ml

models from scratch is still extremely

difficult if you need to do image

analysis

you may as well just use the recognition

api it can classify all kinds of objects

and images and is likely way better than

anything that you would build on your

own

or if you want to build a conversational

bot you might use lex which runs on the

same technology that powers alexa

devices

or if you just want to have fun and

learn how machine learning works you

might buy a deep racer device

which is an actual race car that you can

drive with your own machine learning

code

now that's a pretty amazing way to get

people to use your cloud platform

but let's change direction and look at a

few other essential tools that are used

by a wide variety of developers

for security we have im where you can

create rules and determine who has

access to what

on your aws account if you're building a

web or mobile app where users can log

into an account

cognito is a tool that enables them to

log in with a variety of different

authentication methods and manages the

user sessions for you

then once you have a few users logged

into your app you may want to send them

push notifications

sns is a tool that can get that job done

or maybe you want to send

emails to your users ses is the tool for

that now that you know about all these

tools you're going to want an

organized way to provision them cloud

formation is a way to create templates

based on your infrastructure

in yaml or json allowing you to enable

hundreds of different services with the

single click of a button from there

you'll likely want to interact with

those services from a front-end

application like ios

android or the web amplify provides sdks

that can connect to your infrastructure

from

javascript frameworks and other

front-end applications now the final

thing to remember

is that all this is going to cost you a

ton of money which goes directly to

getting jeff's rocket up so make sure to

use aws cost explorer and budgets if you

don't want to pay for these big bulging

rockets

that's the end of the video it took a

ton of work so please like and subscribe

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member at fireship io to get access to

more advanced content about building

apps in the cloud thanks for watching

and i will see you in the next one