- Welcome to Azure Essentials.
If you're an AWS Professional,
your knowledge on the AWS platform
is going to make it easier to get started with Azure.
Both are comprehensive cloud platforms,
and in the next few minutes,
I'll show you how key fundamentals
of AWS translate to Azure.
Like AWS, the Azure architecture
gives you the flexibility to build solutions
for both Windows and Linux.
Both platforms also have a rich marketplace
that supports a growing third-party ecosystem
of apps and solutions.
Some key differences when compared to AWS
include the concept of accounts
and subscriptions in Azure.
In Azure, the account owner can delegate
the task of managing subscriptions to application owners,
which is important when the person
paying the bills is not the person responsible
for managing the technology.
Also, imagine being able to run
AWS services in your data center.
With Azure, you can deploy Azure services
in your data center using Azure Stack.
And Azure also supports first-party hybrid integration
between your cloud and on-premises solutions.
Now let's take a look at core services
across compute, storage, and management.
Compute options are very similar between AWS and Azure.
You'll find a similar range of
on-demand virtual machine sizes in Azure
to the EC2 instances in AWS.
Although you'll notice some differences
in the memory, CPU, and storage options.
Just as you'd create AWS instances
in the AWS Management Console,
you can create Azure VMs directly
through the Azure portal using APIs
or via the Azure command-line interface
for Windows or Linux.
In both AWS and Azure, you can use auto-scaling
to scale your application or service dynamically.
In the case of Azure, you'll use VM scale sets
to add or remove VMs automatically
based on the metrics and thresholds you define.
And where in AWS you have cloud formation,
you can use Azure Resource Manager,
or ARM, templates to define your service
or application architecture for multi-tiered workloads.
Now, if you work with containers,
in AWS you're probably working with
the EC2 Container Service.
In Azure, you'll want to use the Azure Container Service.
Azure supports both Linux and Windows containers
in a range of orchestration options,
including Kubernetes,
Mesosphere DC/OS,
and Docker Swarm.
Addtionally, the Azure Container Service
also offers an easier way to run containerized applications.
And Azure Container Instances let you
rapidly create and launch containerized apps
without any overhead
and with an easily scriptable set of commands.
Now moving on to options for serverless.
To build and deploy serverless applications in AWS,
you'll be working with AWS Lambda,
the API Gateway, and other services,
whereas in Azure you can accomplish the same
with Azure Functions and other platform services.
This includes Azure Logic Apps
to model and automate your process workflows visually,
and Azure's Database as a Service offerings.
Lastly, Azure Service Fabric provides
a run time and services to build
distributed, scalable microservices-based applications.
Going beyond compute,
a persistent data store is at the heart
of many applications,
and Azure offers a range of storage options.
If you work with S3 in AWS,
in Azure you'll use Blob Storage.
Like S3, you can use Blob Storage
to store large amounts of unstructured data
either for private access or to share publicly.
Now in AWS, you also have the option
of cool storage using S3 Standard IA
and Glacier for archivical storage.
In Azure, this maps to Azure storage down to Cool
and Azure archival storage.
The database offerings available
through AWS and Azure are also similar,
though there are important differences.
Compared to Amazon RDS relational database options,
in Azure you've got the choice of
SQL database,
Azure Database for MySQL,
and PostgreSQL.
Also, to create non-relational databases,
Azure provides Cosmos DB.
Cosmos DB is similar to Dynamo DB in AWS
with additional functionality,
including SQL Query and one-structured data,
low latency, and geo-replication.
For traditional data warehousing,
as provided in AWS with Redshift,
Azure offers SQL Data Warehouse.
Like Redshift, SQL Data Warehouse
is a fast, fully-managed petabyte-scale data warehouse.
Now if you're using Amazon EMR for
big data analytics, including Hadoop,
in Azure, you'd use HDInsights,
which is a fully-managed Hadoop offering.
Additionally, Azure Datalake Store
allows you to store massive,
unstructured or structured datasets,
enabling analysis of all of your data in one place.
Now moving on to our last topic, management.
In AWS you might start with the AWS Management Console
and leverage Cloud Watch, Cloud Trail, X-Ray,
and multiple third-party solutions,
whereas in Azure, the Azure portal
gives you the consistent control panel
from which to manage security, identity, apps and resources.
For custom troubleshooting,
you can also use the Azure Cloud Shell,
which supports both Bash for Linux
and Power Shell for Windows from the browser.
Through the portal, you can monitor
metrics, logs, health, and the events of your applications,
as well as your VM utilization,
and of the performance metrics.
You can customize the Azure portal
to build your own management dashboards and set up alerts
to other devices and incident management systems.
And where in AWS you might then use
a third-party analytics engine like Splunk,
on Azure you can continue to use these
or use Azure's built-in Azure Log Analytics
and Azure Applications Insights.
These monitor app telemetry and
use Machine Learning to provide
deeper insights and diagnostics
into your apps and workloads over their entire life cycle.
Lastly, if you use AWS Trusted Advisor
for proactive resource optimization,
you can take advantage of Azure Advisor
at no additional cost.
So that was a brief overview of how
the fundamentals of AWS translate to the Azure platform.
I hope it helps you to see how you can
start to apply your existing AWS skillset
when working with Azure.
For more extensive hands-on learning,
please check out the followings links.