Top 50+ AWS Services Explained in 10 Minutes
Summary
TLDRThis 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
🚀 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.
🛠 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.
🔍 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.
Mindmap
Keywords
💡Amazon Web Services (AWS)
💡Elastic Compute Cloud (EC2)
💡Elastic Load Balancing
💡Elastic Beanstalk
💡AWS Lambda
💡Elastic Container Service (ECS)
💡Elastic File System (EFS)
💡Amazon RDS
💡Amazon Aurora
💡AWS Glue
💡Amazon SageMaker
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
to support the channel or become a pro
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
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