Azure Basics

Cloud - Also known as 'landing layer'
=====
HDFS = Hadoop Distributed File System

RDBMS = Relational DB Management System
If we r using someone else's server to host or to process the data, it is called

Cloud.
(the practice of using a network of remote servers hosted on the internet to store,
manage,
and process data, rather than a local server or a personal computer.)
Pros:
1. Universal Access - Any time, any where, any device can access data.
2. Scalable - Can increase or decrease the size of servers quickly.
3. Pay as you go - We have to pay for what we r using.
4. Access any service - Software as a Service(SaaS), Platform as a Service(PaaS),
Infra -
structure as a Service(IaaS)
Resources available in current IT Infrastructure

1) Servers
2) DBs
3) Storage
4) Network
All the above Services provided thro' internet is called Cloud Computing.
On Premises - Appln, Data, Runtime, Middleware, OS, Visuz, Server, Storage &
Networking to be
managed by us onl.y
IaaS - Delivery of tech infrastructure on demand. Server, Storage, Visualization &
Networking
will be taken care by Cloud Provider
PaaS - Provides runtime environment for appns, development, deployment etc.
Runtime,
Middleware, OS, Visuz, Server, Storage and N/w managed by Cloud Provider
SaaS - All managed by Cloud Provider
Core Azure Service Domains

--------------------------
Compute, Networking, File Storage, DB+Analytics, AI+ML, Identity, Management
1) Compute:
a) Virtual Machine: A machine which is provided by the Cloud provider which

contains OS only.
b) Function App : Backend Server
c) App Servie: "PaaS" Can be used for launch the services and deploy web
applications also.
d) Azure Kubernetes Services: Computers will act as containers which will act as
seperate
Virtual Machines isolated from each other. Its an automatic service. For some
reason when
any of the server/container is not working, Kubernetes will automatically detects
the fault
and delete the container and launches a new copy of the container.
2) Networking:
a) Virtual Networks: Isolated N/Ws or isolated environments in the Azure

environment. It helps
all the VMs created in the Azure to interact/talk to each other.
b) Load Balancers: It will randomly spread the request into n no. of servers for
high
availability. Whenever a request comes, Load balancer will check the serevrs for
which of them
are having less load and the data will be diverted to that server.
c) Application Gateway: Based on the request Gateway Load balancer will route our
request
to the particular path.
d) DSN Zones: Route your request to Domain name.
e) CDN Profiles(Content Delivery Network): For delivering high bandwidth content.
It basically
cache the static data.
3) Storage:
a) Blob(Binary Log Object): Stores large amount of unstructured data such as text,
video,
audio. Blob serves images or docs dirtectly to a browser. Streaming audio & video.
b) File Storage: If we want to share files to multiple servers, we can moount a
particular
drive to File Storage. It act as a central storage. A change in the file in a
particular
server will alert other servers also.
c) Tables: NoSQL Data storage for large amount of structured data. Store Structured
data in
non-relational manner.
d) Queues: Storing large number of messages that can be accessed from anywhere in
the world.
e) Data Lake Storage: Repository for Big Data Analytics. Large amount of data van
be
retrieved fastly. Good Optimization also.
f) Data Box: Move stored data to Azure quickly, ie migrating data to Azure.
Processing of data: Supported by Data Lake Storage.

1) Batch Processing: Processing data once in a day
2) Interactive Pro: Processing data in a particular time period.
3) Streamline Pro: Online processing of data.
4) Database + Analytics:
a) SQL DB: Intelligent, scalable, cloud database. OS access is not provided. So it

comes
under PaaS.
b) CosmoDB: Fully managed SQL service. The data cluster created in CosmosDB, a
replica of DB
will be created and stored in multiple servers and available all over the world.
c) Data Factory: It is an ETL (Extract, Transform and Load data) Service especially
used for
Big Data Analytics.
d) Event Hubs: Real - time data ingestion service. Data from multiple sources will
be pushed
into particular landing layer/path.
e) Data Lake Analytics: Cloud based data processing architecture. It pairs with
Data Lake
Storage services for Big Data Analytics.
5) AI + ML:
a) Cognitive Services: APIs,SDKs(Software Development Kit) services avaiable to

help developers
build intelligent applns like Image and Text processing.
b) Bot Services: AI chatbot services
c) Machine Learning Studio: Drag and drop tool u can use to build, test, and deploy
ML models.
Data Cleansing, Prediction can be done also using this.
6) Identity:
Identifying a particular user using Azure Active Directory(Azure AD)
7) Management:
a) Log Analytics: We can store all the logs of applns in this service and can be
used to
analyse them for Root Cause Analysis(RCA) by support Enggs.
b) Cost management + Billing: Helps you analyze costs, create and manage budgets
and review
on optimization recommendations to save money.It can send alerts to all admins to
make note
of limits of usgae.
c) Automation Account:
d) Metrics: Interactive analysis in the Azure portal.

Azure Basics

Uploaded by

Copyright:

Available Formats

Azure Basics

Uploaded by

Document Information

Original Description:

Copyright

Available Formats

Share this document

Share or Embed Document

Sharing Options

Did you find this document useful?

Is this content inappropriate?

Copyright:

Available Formats

Azure Basics

Uploaded by

Copyright:

Available Formats

Cloud - Also known as 'landing layer'

HDFS = Hadoop Distributed File System

If we r using someone else's server to host or to process the data, it is called

Resources available in current IT Infrastructure

Core Azure Service Domains

a) Virtual Machine: A machine which is provided by the Cloud provider which

a) Virtual Networks: Isolated N/Ws or isolated environments in the Azure

Processing of data: Supported by Data Lake Storage.

a) SQL DB: Intelligent, scalable, cloud database. OS access is not provided. So it

a) Cognitive Services: APIs,SDKs(Software Development Kit) services avaiable to

Identifying a particular user using Azure Active Directory(Azure AD)

You might also like