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When working with databases, each technology has its own designated character that can be used as a wildcard character. Usually these characters are * and %.
Recently, I was stuck for several hours on a problem which was related to wildcards. Let me set the scene.
I was using a Microsoft Access database with data that had to be queried via PowerShell. The problem I encountered was that the wildcard character for SQL queries in Microsoft Access use â*â, but when the query was being sent from PowerShell, the result from the $cmd.ExecuteReader() command was blank.Â
Ultimately, I discovered that with PowerShell, you cannot use â*â for the wildcard, it wonât pass through PowerShell to the Access database. Once I updated the query to use â%â, everything worked correctly!
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It supports a maximum of 8640 data points. Break this down into smaller requests to improve the performance.
When to use Anomaly Detector
Process the algorithm against an entire set of data at one time
It creates a model based on your complete data set and the finds anomalies
Uses streaming data by comparing previously seen dat points to the last datapoint to determine if your latest one is an anomaly.
Model is created using the data points you send and determines if the current point is an anomaly.
Microsoft Azure AI Fundamentals: Explore natural language processing
Analyze Text with the Language Service
Used to describe solutions that involve extracting information from large volumes of unstructured data.
Analyzing text is a process to evaluate different aspects of a document or phrase, to gain insights about that text.
Text Analytics Techniques
Interpret words like âpowerâ, âpoweredâ, and âpowerfulâ as the same word.
Convert to tree like structures (Noun phrases)
Often used for sentiment analysis
Determine the language of a document or text
Perform sentiment analysis (positive or negative)
Extract key phrases from text to indicate key talking points
Identify and categorize entities (places, people, organizations, etc)
Get started with Text analysis
Language name
ISO 6391 language code
Score as a level of confidence n the language returned.
Evaluates text to return a sentiment score and labels for each sentence
Useful for detecting positive or negative sentiment
Classification is between 0 to 1 with 1 being most positive
A score of 0.5 is indeterminate sentiment.
The phrase doesnât have sufficient information to determine the sentiment.
Mixing language content with the language you tell it will return 0.5 also
Key Phrase extraction
Used to determine the main talking points of a text or a document
Depending on the volume this can take longer, so you can use the key phrase extraction capabilities of the Language Service to summarize main points.
Key phrase extraction can provide context about the document or text
Entity Recognition
Person
Location
OrganizationQuantity
DateTime
URL
Email
US-based phone number
IP address
Recognize and Synthesize Speech
Acoustic model - converts audio signal to phonemes (representation of specific sounds)
Language model - maps the phonemes to words using a statistical algorithm to predict the most probably sequence of words based on the phonemes
ability to generate spoken output
Usually converting text to speech
This process tokenizes the set to break it down into individual words, assign phonetic sounds to each word
It then breaks the phonetic transcription to prosodic units to create phonemes for the audio
Get started with speech on Azure
Use this for demos, presentations, or scenarios where a person is speaking
In real time it can translate to many lunges as it processes
Audio files with Shared access signature (SAS) URI can be used and results are received asynchronously.
Jobs will start executing within minutes, but no estimate is provided for when the job changes to running state
Used to convert text to speech
Voices can be selected that will vocalize the text
Custom voices can be developed
Voices are trained using neural networks to overcome limitations in speech synthesis with regards to intonation.
Translate Text and Speech
Where each word is translated to the corresponding word in the target language
This approach has issues. For example, a direct word to word translation may not exist or the literal translation may not be the correct meaning of the phrase
Machine learning has to also understand the semantic context of the translation.
This provides more accurate translation of the input phrase or phrases
Grammar, formal versus informal, colloquialism all need to be considered
Text and speech translation
Profanity filtering - remove or do not translate profamity
Selective translation - tag content that isnât to be translated (brand names, code names, etc)
Speech to text - transcribe speech from an audio source to text format.
Text to speech - used to generate spoken audio from a text source
Speech translation - translate speech in one language to text or speech in another
Create a language model with Conversational language Understanding
A None intent exists.
This should be used when no intent has been identified and should provide a message to a user.
Getting started with Conversational Language Understanding
Authoring the model - Defining entities, intents, and utterances to use to train the model
Entity Prediction - using the model after it is published.
Define intents based on actions a user would want to perform
Each intent should include a variety of utterances as examples of how a user may express the intent
If the intent can be applied to multiple entities, include sample utterances for each potential entity.
Machine-Learned - learned by the model during training from context in the sample utterances you provide
List - Defined as a hierarchy of lists and sublists
RegEx - regular expression patterns
Pattern.any - entities used with patterns to define complex entities that may be hard to extract from sample utterances
After intents and entities are created you train the model.
Training is the process of using your sample utterances to teach the model to match natural language expressions that a user may say to probable intents and entities.
Training and testing are iterative processes
If the model does not match correctly, you create more utterances, retrain, and test.
When results are satisfactory, you can publish the model.
Client applications can use the model by using and endpoint for the prediction resource
Build a bot with the Language Service and Azure Bot Service
Knowledge base of question and answer pairs. Usually some built-in natural language processing model to enable questions and can understand the semantic meaning
Bot service - to provide an interface to the knowledge base through one or more channels
Microsoft Azure AI Fundamentals: Explore knowledge mining
Used to describe solutions that involve extracting information from large volumes of unstructured data.
It has a services in Cognitive services to create a user-managed index.
The index can b meant for internal use only or shared with the public.
It can use other Cognitive Services capabilities to extract the information
What is Azure Cognitive Search?
Provides a programmable search engine build on Apache Lucene
Highly available platform with 99.9% uptime SLA for cloud and on-premise assets
Data from any source - accepts data form any source provided in JSON format with auto crawling support for selected data sources in Azure
Full text search and analysis - Offers full text search capabilities supporting both simple query and full Lucene query syntax
AI Powered search - has Cognitive AI capabilities built in for image and text analysis from raw content
Multi-lingual - offers linguistic analysis for 56 langues
Geo-enabled - supports geo-search filtered based on proximity to a physical location
Configurable user experience - it includes capabilities to improve the user experience (autocomplete, autosuggest, pagination, hit highlighting, etc)
Identify elements of a search solution
Folders with files,
Text in a database
Etc
Use a skillset to Define an enrichment pipeline
Key Phrase Extraction - uses a pre-trained model to detect important phrases based on term placement, linguistic rules, proximity to terms
Text Translation - pre-trained model to translate the input text into various languages for normalization or localization use cases
Image Analysis Skills - uses an image detection algorithm to identify the content of an image an generate a text description
Optical Character Recognition Skills - extract printed or handwritten text from images, photos, videos
Understand indexes
Index schema - index includes a definition of the structure of the data in the documents to read.
Index attributes - Each field in a document the index stores its name, the data type, supported behaviors (searchable, sortable, etc)
Best indexes use only the features that are required/needed
Use an indexer to build an index
Push method - JSON data is pushed into a search index via a REST API or a .NET SDK. Most flexible and with least restrictions
Pull method - Search service indexer pulls from popular Azure data sources and if necessary exports the Tinto JSON if its not already in that format
Use the pull method to load data with an indexer
Azure Cognitive searchâs indexer is a crawler that extracts searchable text and metadata form an external Azure data source an populates a search index using field-to-field mapping between the data and the index.
Data import monitoring and verification
Indexers only import new or updated documents. It is normal to see zero documents indexed
Health information is displayed in a dashboard.
You can monitor the progress of the indexing
Making changes to an index
You need to drop and recreate indexes if you need to make changes to the field definitions
An approach to update your index without impacting your users is to create a new index with a new name
After importing data, switch to the new index.
Persist enriched data in a knowledge store
A knowledge store is persistent storage of enriched content.
The knowledge store is to store the data generated from Ai enrichment in a container.
Microsoft Azure AI Fundamentals: Explore visual studio tools for machine learning
What is machine learning? A technique that uses math and statistics to create models that predict unknown values
Types of Machine learning
Regression - predict a continuous value, like a price, a sales total, a measure, etc
Classification - determine a class label.
Clustering - determine labels by grouping similar information into label groups
x = features
y = label
Azure Machine Learning Studio
You can use the workspace to develop solutions with the Azure ML service on the web portal or with developer tools
Web portal for ML solutions in Sure
Capabilities for preparing data, training models, publishing and monitoring a service.
First step assign a workspace to a studio.
Compute targets are cloud-based resources which can run model training and data exploration processes
Compute Instances - Development workstations that data scientists can use to work with data and models
Compute Clusters - Scalable clusters of VMs for on demand processing of experiment code
Inference Clusters - Deployment targets for predictive services that use your trained models
Attached Compute - Links to existing Azure compute resources like VMs or Azure data brick clusters
What is Azure Automated Machine Learning
Jobs have multiple settings
Provide information needed to specify your training scripts, compute target and Azure ML environment and run a training job
Understand the AutoML Process
ML model must be trained with existing data
Data scientists spend lots of time pre-processing and selecting data
This is time consuming and often makes inefficient use of expensive compute hardware
In Azure ML data for model training and other operations are encapsulated in a data set.
You create your own dataset.
Classification (predicting categories or classes)
Regression (predicting numeric values)
Time series forecasting (predicting numeric values at a future point in time)
After part of the data is used to train a model, then the rest of the data is used to iteratively test or cross validate the model
The metric is calculated by comparing the actual known label or value with the predicted one
Difference between the actual known and predicted is known as residuals; they indicate amount of error in the model.
Root Mean Squared Error (RMSE) is a performance metric. The smaller the value, the more accurate the modelâs prediction is
Normalized root mean squared error (NRMSE) standardizes the metric to be used between models which have different scales.
Shows the frequency of residual value ranges.
Residuals represents variance between predicted and true values that canât be explained by the model, errors
Most frequently occurring residual values (errors) should be clustered around zero.
You want small errors with fewer errors at the extreme ends of the sale
Should show a diagonal trend where the predicted value correlates closely with the true value
Dotted line shows a perfect modelâs performance
The closer to the line of your modelâs average predicted value to the dotted, the better.
Services can be deployed as an Azure Container Instance (ACI) or to a Azure Kubernetes Service (AKS) cluster
For production AKS is recommended.
Identify regression machine learning scenarios
Regression is a form of ML
Understands the relationships between variables to predict a desired outcome
Predicts a numeric label or outcome base on variables (features)
Regression is an example of supervised ML
What is Azure Machine Learning designer
Allow you to organize, manage, and reuse complex ML workflows across projects and users
Pipelines start with the dataset you want to use to train the model
Each time you run a pipelines, the context(history) is stored as a pipeline job
Encapsulates one step in a machine learning pipeline.
Like a function in programming
In a pipeline project, you access data assets and components from the Asset Library tab
You can create data assets on the data tab from local files, web files, open at a sets, and a datastore
Data assets appear in the Asset Library
Azure ML job executes a task against a specified compute  target.
Jobs allow systematic tracking of your ML experiments and workflows.
Understand steps for regression
To train a regression model, your data set needs to include historic features and known label values.
Use the designerâs Score Model component to generate the predicted class label value
Connect all the components that will run in the experiment
Average difference between predicted and true values
It is based on the same unit as the label
The lower the value is the better the model is predicting
The square root of the mean squared difference between predicted and true values
Metric based on the same unit as the label.
A larger difference indicates greater variance in the individual  label errors
Relative metric between 0 and 1 on the square based on the square of the differences between predicted and true values
Closer to 0 means the better the model is performing.
Since the value is relative, it can compare different models with different label units
Relative metric between 0 and 1 on the square based on the absolute of the differences between predicted and true values
Closer to 0 means the better the model is performing.
Can be used to compare models where the labels are in different units
Also known as R-squared
Summarizes how much variance exists between predicted and true values
Closer to 1 means the model is performing better
Remove training components form your data and replace it with a web service inputs and outputs to handle the web requests
It does the same data transformations as the first pipeline for new data
It then uses trained model to infer/predict label values based on the features.
Create a classification model with Azure ML designer
Classification is a form of ML used to predict which category an item belongs to
Like regression this is a supervised ML technique.
Understand steps for classification
True Positive - Model predicts the label and the label is correct
False Positive - Model predicts wrong label and the data has the label
False Negative - Model predicts the wrong label, and the data does have the label
True Negative - Model predicts the label correctly and the data has the label
For multi-class classification, same approach is used. A model with 3 possible results would have a 3x3 matrix.
Diagonal lien of cells were the predicted and actual labels match
Number of cases classified as positive that are actually positive
True positives divided by (true positives + false positives)
Fraction of positive cases correctly identified
Number of true positives divided by (true positives + false negatives)
Overall metric that essentially combines precision and recall
Classification models predict probability for each possible class
For binary classification models, the probability is between 0 and 1
Setting the threshold can define when a value is interpreted as 0 or 1. Â If its set to 0.5 then 0.5-1.0 is 1 and 0.0-0.4 is 0
Recall also known as True Positive Rate
Has a corresponding False Positive Rate
Plotting these two metrics on a graph for all values between 0 and 1 provides information.
Receiver Operating Characteristic (ROC) is the curve.
In a perfect model, this curve would be high to the top left
Area under the curve (AUC).
Remove training components form your data and replace it with a web service inputs and outputs to handle the web requests
It does the same data transformations as the first pipeline for new data
It then uses trained model to infer/predict label values based on the features.
Create a Clustering model with Azure ML designer
Clustering is used to group similar objects together based on features.
Clustering is an example of unsupervised learning, you train a model to just separate items based on their features.
Understanding steps for clustering
Prebuilt components exist that allow you to clean the data, normalize it, join tables and more
Requires a dataset that includes multiple observations of the items you want to cluster
Requires numeric features that can be used to determine similarities between individual cases
Initializing K coordinates as randomly selected points called centroids in an n-dimensional space (n is the number of dimensions in the feature vectors)
Plotting feature vectors as points in the same space and assigns a value how close they are to the closes centroid
Moving the centroids to the middle points allocated to it (mean distance)
Reassigning to the closes centroids after the move
Repeating the last two steps until tone.
Maximum distances between each point and the centroid of that pointâs cluster.
If the value is high it can mean that cluster is widely dispersed.
With the Average Distance to Closer Center, we can determine how spread out the cluster is
Remove training components form your data and replace it with a web service inputs and outputs to handle the web requests
It does the same data transformations as the first pipeline for new data
It then uses trained model to infer/predict label values based on the features.
The term used to describe solutions that involve extracting information from large volumes of often unstructured data to create searchable knowledge stores
Azure Cognitive Search is an example of this service (for internal use or external)
Utilizes the built in AI capabilities of Azure Cognitive Services such as image processing, content extraction, and NLP to perform knowledge mining of documents.
Capabilities exist to index previously unsearchable documents and to extract and surface insights from large amounts of data quickly
Challenges and risks with AI
Bias can affect results
Errors may cause harm
Data could be exposed
Solutions may not work for anyone
Users may trust a complex system
Whoâs liable for AI-driven decisions?
Understand Responsible AI (RAI)
AI systems should treat all people fairly
For example RAI with Face service retires facial recognition capabilities that can be use to stereotype or discriminate people
AI systems should perform reliably and safely
Systems should be subjected to rigorous testing and deployment management processes to ensure they work as expected before release.
AI systems should be secure and respect privacy
System decisions and predictions should keep the data used to train it and the responses private
AI systems should empower everyone and engage people
They should not only benefit a small group of people.
AI systems should be understandable.
Users should be made aware of how the system works, its limitations, and its purpose
People should be accountable for AI systems
Everyone involved in designing and building AI solutions should work within a framework of governance and organizational principles
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â Live Streamingâ Interactive Chatâ Private Showsâ HD Quality
Anya is LIVE right now
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Anomaly detection looks for values outside the expected range.
A large number of data points of normal behavior are collected and train the model. Then the actual data is run through the model to detect anomalies
Anomaly Detector provides an application programming interface (API) that developers can use to create their own anomaly detection solutions
Understand computer vision
An area of AI that deals with visual processing.
The Seeing AI app is good example of the power of computer vision.
Seeing AI describes the world around you
It recognizes people, text, objects, etc.
Computer Vision models and capabilities
Image classification - This involves training a ML model to classify images based on the contents.
Object detection - Training a ML model to classify individual objects within an image and identify their location with a bounding box
Semantic Segmentation - An advanced ML learning technique where individual pixels in the image are classified based on the object they belong to, the area is masked
Image analysis - Specialized form of object detection that locates human faces
Optical character recognition (OCR) - technique used to detect and read text in images. It can be used to read documents or images.
Computer vision services in Microsoft Azure
Computer vision - use this service to analyze images and video and extract descriptions, tags, objects, and text
Custom vision - use this service to train custom image classification and object detection models using your own images
Face - use this service to build face detection and facial recognition solutions
Form Recognizer - use this service to extract information from scanned forms and invoices
Understand Natural Language Processing (NLP)
Analyze and interpret text in documents, emails, etc.
Interpret spoken language and synthesize speech responses
Automatically translate spoken or written phrases between languages
Interpret commands and determine the appropriate action to take
Natural language processing in Microsoft Azure
This service provides a platform for conversational AI
Capability of software agent to participate in a conversation
Devs can use the Bot Framework to create a bot and manage it via Azure Bot service
Microsoft Azure AI Fundamentals: Get started with artificial intelligence
Introduction to AI
Improves health care
Enables people to overcome physical disadvantages
Empowers smart infrastructure
What is AI?
foundation to AI systems
Humans teach computers and generate models
The models can make predictions and draw conclusions based on the data used to train the model
Understanding Machine Learning
Machine learning (ML) is the foundation of most AI solutions
How machine learning works
Machines learn from data!
Every day we create huge volumes of data and that data can be used to train models (text messages, emails, social media, photos, video, etc)
Data is also created by devices, sensors that are everywhere in our environment (cars, cities, factories, etc)
Data scientists use the data to train machine learning models
The models make predictions and inferences based on the relationships in the data
Machine learning in Microsoft Azure
Automated machine learning - enables non-experts to quickly create an effective machine learnings model from data
Azure machine learning designer - graphical interface enabling a no-code development environment for machine learning solution
Data and compute management - Cloud based data storage and computer resources to run data experiments at scale by professional data scientists
Pipelines - Data scientists, software engineers, and IT operations professionals can define pipelines to orchestrate model training, deployment and management tasks
VPN uses an encrypted tunnel within another network.
VPNs are typically deployed to connect two ore more trusted private networks to one another over an untrusted network
Traffic is encrypted while traveling over an untrusted network
VPN Gateways
A type of a virtual network gatewayÂ
Deployed in a dedicated subnet of the virtual network and enabled the following connectivity
Content on-premises data centers to virtual networks through a site-to-site connection.Â
Content individual devices to virtual networks through a point-to-site connection.Â
Connect virtual networks to other virtual networks through a network-to-network connection
All transfers are encryptedÂ
Only one VPN gateway per virtual networkÂ
Two types of gateway configuration and they both use a pre-shared key for authentication
Policy-based
Specify statically the IP address of packets that should be encrypted in the tunnelÂ
This type of devices evaluates every data package against those sets of IP addresses to choose the tunnel where that packet is going to be sent through
Route-based
IPSec tunnels are modeled as a network interface or virtual tunnel interface.Â
 IP routing (static or dynamic router protocols) decides which one of these tunnel interfaces to use when sending each package.Â
Preferred connection method for on-premises devices (more resilient to topology changes like creation of new subnets)
Use route-based VPN gateways if you need the following connectivity
Connections between virtual networks
Point-to-site connectionsÂ
Multisite connectionsÂ
Coexistence with an Azure ExpressRoute gateway
Describe Azure ExpressRoute
Express route lets you extend your on-premises network into the Microsoft cloud over a private connection.
The connection is also called an ExpressRemote Circuit.
This allows you to connect offices, data centers, and other facilities to the Microsoft cloud.
Each location would have its own ExpressRoute circuit
Connectivity can be fromÂ
any-to-any (IP VPN)networkÂ
Point-to-point ethernet networkÂ
Virtual cross-connection through a connectivity provider at a colocation facilityÂ
ExpressRoute connections donât go over the public internetÂ
This allows connections to offer more reliability, faster speeds, consistent latencies, and higher security than typical connections over the internet.
Features and benefits of ExpressRoute
Connectivity to Microsoft cloud services across all regions in the geopolitical regionÂ
Global connectivity to Microsoft services across all regions with express route global reach.Â
Dynamic routing between your network and Microsoft via Border Gateway Protocol (BGP)Â
Built in redundancy in every peering location for higher reliability
Direct access to these services in all regions
Microsoft Office 365Â
Microsoft Dynamics 365Â
Azure computer services such as Virtual machinesÂ
Azure cloud services such as Azure Cosmos DB and Azure storage
Global connectivity -Â ExpressRoute Global reach to exchange data across your premises sites by connecting your ExpressRoute circuits.
Dynamic routing
ExpressRoute uses the BGP to exchange routes between on-premises networks and resources running in Azure.Â
Enables dynamic routing between your on-premises network and services running in the Microsoft cloud
Built-in redundancy -Â Each connectivity provider uses redundant devices to ensure connections established with Microsoft are highly available.
ExpressRoute connectivity models
CloudExchange colocation
Your data center, office, or facility being physically co-located at a cloud exchange such as an ISPÂ
If youâre co-located at a cloud exchange you can request a virtual cross-connect to the Microsoft cloud
Point-to-Point Ethernet connection - Connecting your facility to the Microsoft cloud
Any-to-Any connection
You can integrate your wide area network (WAN) with azure by providing connections to your offices and data centers.
Integrates your WAN connections to provide a connection like you would have between your datacenter and any branch offices.
Directly from ExpressRoute sites -Â Connect into Microsoft global network at a peering location strategically distributed across the world
Security considerations -Â Everything runs over the private connection except for DNS queries, certificate revocation, etc.
Describe Azure DNS
Azure DNS is a hosting service for DNS domains that provides name resolution
Benefits of Azure DNS
Reliability and performance
Hosted on  Azureâs global network of DNS servers providing resiliency and high availabilityÂ
Uses any cast networking so each DNS query is answered by the closes DNS server
Security
Based on Azure Resource ManagerÂ
Provides RBAC control to who has access to specific actionsÂ
Activity logs to monitor how a resource is changedÂ
Resource locking to lock a subscription, resource group, resource
Ease of use
Manage DNS records for Azure and non-Azure resourcesÂ
Integrated with your Azure portal for bulling and support
Customizable virtual networks
Supports private DNS domainsÂ
Feature allows you to use your own custom domain names in your private networks.
Alias records
Supports alias record sets.Â
Use an alias record to refer to an Azure resource, such as an Azure public address, an Azure Traffic manager profile, etc.
Describe Azure Storage Services
Storage accounts provide unique namespaces for your Azure Storage data that is accessible from anywhere in the world over HTTP or HTTPs.
Data in this account is very secure, highly available, durable, and massively scalable
When creating a storage account, pick the account type which determines the storage services and redundancy options and has impact on the use cases.
Redundancy options
Locally redundant storage (LRS)Â
Geo-redundant storage (GRS)Â
Read-access geo-redundant storage (RA-GRS)Â
Zone-redundant storage (ZRS)Â
Geo-zone-redundant storage (GZRS)Â
Read-access geo-zone-redundant storage (RA-GZRS)
Storage account endpoints
Unique namespaces for your dataÂ
Every storage account must have a unique-in-azure account nameÂ
Combination of account name with Azure storage service end point, form the endpoints for a storage account.
Describe Azure storage redundancy
Always stores multiple copies of your data so that its protected from planned and unplanned events
Shoes best option for your scenario (tradeoffs between lower cost and higher availability)
Factors include
How data is replicated in the primary regionÂ
Whether data is replicated to a second region that is geographically distant to the primary region to protect against regional disastersÂ
Whether your application requires read access to the replicated data in the secondary region if the primary region becomes unavailable.
Evaluates your Azure resources and makes recommendations to improve reliability, security, and performance, achieve operational excellence and reduce costsÂ
Recommendations are available via the API and portal. You can setup notifications to alert new recommendationsÂ
Divided into five categories
Reliability used to ensure and improve the continuity of your business critical applicationsÂ
Security used to detect threats and vulnerabilities that might lead to security breachesÂ
Performance used to improve the speed of your applicationsÂ
Operational Excellence used to help achieve process and workflow efficiency, resource manageability, and deployment best practicesÂ
Cost is used to optimize and reduce your overall Azure spending
Azure Service Health
Helps you keep track of Azure resources, both your specifically deployed resources and overall status of Azure.
Azure Status
Broad picture of the status of Azure globallyÂ
Informs you of service outages in AzureÂ
Global view of health of all Azure services across all Azure regions.
Service Health
Narrower view of Azure services and regionsÂ
Focuses on the Azure service and regions you are usingÂ
Can use your authenticated account to identify your services and then report on status of thoseÂ
Alerts can be setup to notify you when service issues, planned maintenance or other changes may affect your services and/or region.
Resource Health
Tailored view of your actual Azure resourcesÂ
Information about the health of your individual cloud resourcesÂ
Use Azure monitor to configure alerts to notify of availability changes to your resources
Azure Monitor
Platform collecting data on your resources, analyzing the data, visualizing the information and even acting on the resultsÂ
Can monitor Azure resources, on-prem resources, and even multi-cloud resources hosted on a different provider.
Configure alerts based on critical events
Azure Log Analytics
Tool in Azure Portal where youâll write and run log queries on data from the Azure Monitor.Â
Supports simple and complex queries and data analysis
Azure Monitor Alerts
Are automated to stay informed when Azure monitor detects a threshold being crossed.Â
Set alert conditions, the notification actions, and then Azure monitor Alerts notifies when an alert is triggered.Â
It can sometimes attempt corrective actionsÂ
Alerts can trigger on certain log events too.
Application Insights
Azure Monitor feature that monitors your web applicationsÂ
Capable of monitoring applications that run in Azure, on-premises, or in a different cloud environmentÂ
Two ways to configure Application Insights
Install SDK in your applicationÂ
Use the Application Insights agent -Â Agent supported in C#, VB.NET, Java, JavaScript, Node.is, and Python
It can monitor
Request rates, response times, and failure ratesÂ
Dependency rates, response times and failure rates for external servicesÂ
Page views and load performance reported by users browsersÂ
AJAX calls from web pagesÂ
User and session countsÂ
Performance counters for OS information
It can monitor, but also send synthetic requests to check if your application is up
Azure Fundamentals: Describe Azure architecture and services
Supports SaaS, PaaS, and IaaS
You need an Azure subscription to create and use Azure services
Structure of objects in Azure
Core Architectural components of Azure
Physical infrastructure
The physical âstuffâ that is in a data center (racks, dedicated power, cooling, networking infrastructure)Â
Azure has data centers around the world.Â
Data centers are not directly accessible, but they are grouped into Azure Regions or Azure Availability Zones to provide resiliency and reliability.
Regions
Geographical area that contains at least one, but possibly more data centersÂ
Networked together with a low latency networkÂ
Azure assigns workloads across the region to balance workloads
Availability Zones
Physically separate data centers within an Azure regionÂ
Each zone is made up of one or more data centers with independent power, cooling, and networkingÂ
Availability zone is setup to be an isolation boundaryÂ
If one goes down the others continue to workÂ
Connected with high-speed private fiber optic networks
Use Availability zones in your apps
Ensure services and data are redundant to protect your informationÂ
Azure can make your app highly available through availability zonesÂ
Primarily for VMs, managed disks, load balancers, and SQL databaseÂ
Three categories
Zone service - You pin the resource to as specific zone (VMs, managed disks, etc)Â
Zone-redundant services - The platform replicates automatically across zones (zone-redundant storage, databases)Â
Non-regional services - always available from Azure geographies and resilient to zone-wide outages as well as region-wide outages.
Region Pairs
Most Azure regions are paired with another region within he same geography (at least 300 miles away)Â
Allows for replication of resources across a geography mitigating risks of one data center going downÂ
Provides fail-over for a regionÂ
Examples of Azure are West US with East US and South-East Asia with East Asia
Additional Advantages
Planned Azure updates are rolled out to paired regions with one going down at a timeÂ
Data continues to reside within the same geography as its pair (except for Brazil South) for tax and law enforcement jurisdiction purposes
Sovereign Regions
Azure has sovereign regionsÂ
Isolated from the main instance of AzureÂ
US DoD Central, US Gov Virginia, US Gov Iowa and more for the US government. Operated by screened US personnel with additional compliance certificationsÂ
China East, China North and more, regions available through a unique partnership between Microsoft and 21vianet. MS doesnât maintain the data centers.
Describe Azure management Infrastructure
Azure resources and resource groups
Resource - basic building block of AzureÂ
Anything you create, provision, deploy, etc is a resource.Â
Resource Groups are grouping of resources and cannot be nestedÂ
When are resource is created, it must be added to a resource groupÂ
Actions applied to a resource group apply to all the resources within the groupÂ
Current and future resources are applied the setting if one is added.Â
Deleting a group will result in deleting all resources in the groupÂ
Grant or deny access to a group and it is inherited by the resources within the group
Azure subscriptions
Unit of management, billing, and scaleÂ
Similar to how resource groups are away to logically organize resources, subscriptions allow you to logically organize your resource groups and facilitate billing
Provides you with authenticated and authorized access to AzureÂ
Links to an Azure account which is an identity in Azure Active Directory, or in a directly that Azure AD trustsÂ
Accounts can have multiple subscriptionsÂ
Multi-subscription accounts can use subscriptions to configure billing models or different access-management policies.
Billing boundary
Determines how an azure account is billedÂ
You can configure different billing requirements.Â
Different reports and invoices are created for each subscription
Access control boundary
Applies access-management policies at the subscription levelÂ
Can create separate subscriptions to reflect different organizational structures.Â
Each department can have a different subscription policies
Create additional subscriptions
Environments - primarily good for resource access controlsÂ
Organizational Structures - reflect different organizational structures via subscriptionsÂ
Billing - Aggregated at the subscription level, can set up additional subscriptions for managing and tracking cost base on needs
Azure Management Groups
Resources are gathered into resource groups
Resource groups are gathered into Subscriptions
Management Groups If you have many subscriptions and want to manage access, policies, and compliance for those you can organize subscriptions into containers
You can apply governance conditions to the management groups
Management groups can be nested
You can build flexible structure of management groups and subscriptionsÂ
Can be used to apply policies, provide user access to multiple subscriptionsÂ
10000 management groups can be supported in a single directoryÂ
Depth of the Tree can be 6 levels (Does not include the root level or the subscription level) Each management group and subscription can support only one parent
Describe Azure compute and networking services
Azure virtual machines
VMs provide SaaS in the form of a virtualized serverÂ
Flexibility of virtualization without having to buy and maintain physical hardware that runs the vm.Â
You can use images to create VMs at scale.
Scale VMs in Azure
You can run a single VM or group VMs togetherÂ
Grouped VMs provide high availability, scalability, and redundancy.Â
You can manage the grouping of VMs for you with features such as scale sets and availability sets
Virtual Machine Scale sets
Lets you create and manage a group of identical, load-balanced VMs.Â
If you create them one at at time, you must configure them identical and then set up network routing parameters to ensure efficiency.Â
Azure automates most of that work with scale setsÂ
Allow you to centrally manage, configure, and update a large number of VMs in minutesÂ
The number of VMs can automatically increase o decrease in response to demand or can be configured to scale based on a defined schedule.Â
Automatically deploy a load balancer to make sure that your resources are used efficiently.
Virtual Machine Availability sets
Another tool to help you build a more resilient, highly available environmentÂ
Availability sets are designed to ensure that Vm stagger updates and have varied power and network connectivityÂ
Availability sets do this by grouping VMs into two ways
Update Domain
VMs can be rebooted at the same timeÂ
Allows to apply updates while knowing that only one update domain grouping will be offline at a time.Â
Update group going through the update process has 30 minutes to recover before maintenance of the next update domain starts
Fault Domain
Groups VMs by common power source and network switch.Â
By default availability set will split your VMS across three fault domains.Â
This helps protect against physical power or network failure
Azure Virtual Desktop
Another type of virtual machineÂ
It is a desktop and application virtualization service in the cloudÂ
You can get to Cloud hosted versions of Windows over the internet.Â
Accessible from clients that are available from native OSâ and HTML5 browsers.Â
Remote Desktop Services (RDS) use to be expensive an difficult to setup - Could take weeks to set up
Now setup the rules in Azure PaaS. The users connect to the VMs on a secure connectionÂ
You have control of how the users are spread across those VMs and workloadsÂ
You can have multiple users on a single VM with a multisession Win10 EnterpriseÂ
For users, their experience is the sameÂ
A globe indicates that the application is virtual, otherwise it seems like being on the same workstation/PCÂ
Enhance security
Provides centralized security management of usersâ desktopsÂ
You can enable MFA to secure signingÂ
Secure access to data by assigning granular RBAC to usersÂ
Data and apps are separate from the local hardwareÂ
User sessions are isolated in both a single or multi-session environment
Multi-Session Windows 10 or Windows 11
The only client-based operating system that enables multiple sessions on the same single VMÂ
More consistent experience similar to that of a windows server based operating system with broader application support
Describe Azure Containers
Although VMs are excellent to reduce cost versus investment for physical hardware, they are limited to a single operating system per virtual machine
If you need multiple instances of an application on a single host machine, you can use containers
Containers
Virtualization environmentÂ
Multiple containers on a single physical or virtual hostÂ
You donât manage the OS for the containerÂ
They appear as an instance of an OS that you connect to and manage.Â
Light weight and designed to be created, scaled out, and stopped dynamicallyÂ
You can quickly restart if there is a crash or hardware interruption.Â
Most popular engine is Docker and it is supported by Azure
Compare Virtual Machine to Containers
Virtual Machines provide abstraction layer for CPU, memory, Storage
VMs you are in control with OS, tools, and packagesÂ
Downsides - only one OS at a time. If the apps require different runtime environments, you may need multiple VMs
Containers
Bundles an app and its dependenciesÂ
Deploys it as a unit to a container host -Â The container host has a standardized runtime environment and abstracts away the OS and hardware
VM machines virtualize hardwareÂ
Containers virtualize the Operating systemÂ
Containerized apps are smaller in sizeÂ
You wait for the app and not the OS to restart if you reboot itÂ
Development process is simplified because your development environment can look like productionÂ
Use Cluster orchestration without wondering which server to put it on.Â
Complete control of environment may mean using a VMÂ
Portability and management capabilities of containers may be a better choice
Azure Container Instances
Offer fastest and simplest way to run a container in AzureÂ
Without having to manage any virtual machines or adopt services.Â
Azure container instances allow you to upload your containers and the services will run them for youÂ
Use containers in your solution
Used to create solutions by using a micro services architectureÂ
Break your solution into smaller independent piecesÂ
You could scale one part of the application. For example your backend can be called if the front-end is not being stressed.
Describe Azure Functions
Event-driven, server less compute option that doesnât require maintaining virtual machines or containers
With Azure Functions an event wakes the function and prevents having the resource (VM or container) always running
Serverless computing
Removes the server management tasks from the usersâ responsibilitiesÂ
Allows you to focus on pushing your applications to the customersÂ
âServers are there, but you donât need to manage themâÂ
Take your mind off the infrastructure concerns and move to application.Â
Often triggered based on an event (a REST request), timer, or message from another serviceÂ
Benefits
No infrastructure managementÂ
ScalabilityÂ
Only pay for your use
By default functions are stateless.Â
When they are stateful (called Durable Functions) a context is passed through the function to track prior activity
Describe application hosting options
Azure Ap Service
Enables you to build and host web apps, background jobs, mobile back-ends, and RESTful APIs in the language you choose
No management of infrastructure
Automatic scaling and high availability
Supports Windows and Linux
Automated deployments from Github, Azure DevOps, or any git repo
Types of app services
Web apps -Â Full support for hosting web apps using ASP.NET, ASP.NET core, Java, Ruby, Node.js, PHP, or python Linux or windows
API apps
You can build REST-based web APIs using your language and frameworkÂ
Full Swagger support and ability to publish your API in Azure Marketplace
Web Jobs
Allow you to run a program (.exe, Java, PHP, Python, or Node.js) or script (.cmd, .bat, PowerShell, or bash) in the same context as a web app, API, or mobile app.Â
Often used to run background tasks as part of your application logic.
Mobile Apps
Quickly build back-end for iOS an android appsÂ
Store mobile data in cloud-based sql databaseÂ
Authenticate customers against social providers, such as MSA, Google, Twitter, and FacebookÂ
Send push notificationsÂ
Execute custom back-end logic in c# or node.js
Handles most of the infrastructure decisions you deal with in hosting web-accessible apps
Deployment and management are integrated into the platformÂ
Endpoints can be securedÂ
Sites can be scaled quickly to handle high traffic loads
The built-in load balancing and traffic manager provide high availability
Describe Azure Virtual Networking
Azure Virtual Networking
Allows you to create virtual networks and virtual subnets to enable Azure resources to communicate
With each other, users on the internet and with your on-premises client computers.
You define a private address space using either public or private addressesÂ
You can use internal or eternal DNS servers for name resolution
Internet communications -Â Assign a public ip address to the resource or put it behind a public load balancer to have internet connectivity
Communicate between Azure resources
Virtual networks can connect not only VMs, but the resources tooÂ
Service endpoints can connect to other resource typesÂ
This enables you to link multiple Azure resources to virtual networks to improve security and provide optimal routing between resources
Communicate with on-premises resources
Virtual networks allow you to link resources together in your on-premises environment with your azure subscription.Â
Network that spans both your local and cloud environments.Â
Three mechanism for this
Point to Site
Virtual networks connections are from a computer outside the origination back into your corporate networkÂ
A client computer initiates an encrypted VPN connection to connect to the Azure virtual network
Site to Site
Virtual private networks link your on-premise VPN device or gateway to an Azure VPN gateway in a virtual network.Â
Devices in Azure can appear as being on the local network.Â
 The connection is encrypted and works over the internet.Â
Azure VPN device (also known as Virtual Network Gateway)Â
Gateway subnets are a virtual subnet gateway located in a dedicated subnet in the Azure virtual network.
Azure Express Route
Dedicated private connectivity to Azure that doesnât travel over the internetÂ
Useful for environments where you need greater bandwidth and even higher levels of security
Route network traffic
Allow you to define rules about how traffic should be directed.Â
You can create custom route tables that control how packets are routed between subnetsÂ
Border Gateway Protocol (BGP) works with Azure VPN gateways, Azure Route Server, or Azure ExpressRoute to propagate on-premises BGP routes to Azure virtual networks
Filter network traffic
Allow you filter traffic between subnetsÂ
Network security groups
Are Azure resources that can contain multiple inbound and outbound security rulesÂ
You can define these rules to allow or block traffic, based on source and destination IP address, port, and protocolÂ
Network virtual appliance
Are specialized VMs that can be compared to hardened network appliances.Â
Carry out a particular network function like a firewall or WAN optimization
Connect virtual networks
Link virtual networks together using virtual network peeringÂ
Peering allows two virtual networks to connect directly to each otherÂ
Traffic between peered networks is private and travels on Microsoftâs backbone network.Â
User-defined Routes (UDR) allow you to control the routing tables between subnets or between virtual networks
It supports both public and private endpoints to enable communication between external and internal resources
Public endpoints have a public IP address can be accessed from anywhere in the worldÂ
Private endpoints exist within a virtual network and have private IP addresses from within the address space of the virtual network
Letâs you standardize cloud subscription or environment deployments.Â
Instead of having to configure features like Azure policy for each new subscription you can define repeatable settings and policies.Â
You can deploy new Test/Dev environments with security and compliance settings already configuredÂ
Development teams can rapidly build and deploy new environments with the knowledge that they are building within organizational requirementsÂ
Artifacts
Each component in the blueprint is known as an artifact.Â
Artifacts may not have additional parameters (configuration) -Â Example: Threat detection rule for SQL server policy
Artifacts can contain one or more parameters that can be configured -Â Example: Allowed location policy for where to deploy resources
You can specify parameter values when you create the blueprint definition or when you assign the blueprint definition to a scope.
Blueprints deploy a new environment based on all the requirements in the artifacts which can include: role assignments, policy assignments, azure resource manager templates, and resource groupsÂ
Blueprints are version-able. You can create an initial version and then make updates laterÂ
The relationship between the blueprint definition (what should be deployed) and the blueprint assignment (what was deployed) is preserved. It maintains a record of each resource and which blueprint defined it. (Auditable)
Azure Policy
A service in Azure that enables you to create, assign, and manage policies that control or audit your resources enables you to define both individual policies and groups of related policies known as initiatives.Â
Evaluates your resource and highlights resources not compliantÂ
Prevents non compliant resources from being createdÂ
Policies can be set at any level: resource, resource group, subscription, etc.Â
Policies are inherited at a high level and applied to all the groupings that fall within the parentÂ
For example, if the resource group has a policy, all the resources in that resource group receive the same policy.Â
Comes with built in policy and initiative definitions for Storage, Networking, Compute, Security Center, and monitoring.Â
Some auto-remediation of non compliant resources is possibleÂ
Azure Policy Initiatives
Are a way of grouping related policies together.Â
Contains all the policy definitions to help track your compliance state for a larger goalÂ
For example: Azure security center has an initiative named Enable monitoringÂ
Included definitions in Security Center
Monitor unencrypted sql databaseÂ
Monitor OS vulnerabilitiesÂ
Monitor missing Endpoint Protection
Resource Locks
Prevent resources from being accidentally deleted or changed.Â
Even with RBAC in place, some risk that people with the appropriate rights can delete resources existsÂ
Resource locks can be applied to resource, resource groups, subscriptionÂ
For example: Are inherited by all resources in a resource groupÂ
Resource Lock Types
Two types. One prevents users from deleting, the other prevents from changing or deleting a resourceÂ
Delete means authorized users can read and modify a resource, but cannot delete the resourceÂ
ReadOnly means authorized users can read a resource, but canât delete it or update it. (Similar to applying a reader role)
Can be managed via Azure portal, Azure cli, PowerShell, and an Azure resource manager templateÂ
How to delete or change a locked resource?
Remove the lock
Then apply any action you have permissions to perform
Re-apply it after again
Service Trust Portal
Provides access to various content, tools and other resources about Microsoft security, privacy and compliance practices.Â
Contains details of Microsoftâs implementation of controls and processes that protect cloud services and the customer data therein.Â
URL: https://servicetrust.Microsoft.com
Features and Tools for managing and deploying Azure resources
Tools for interacting with Azure
Azure PortalÂ
Azure PowerShellÂ
Azure Command Line Interface (CLI)
Required to interact with the Azure environment, management groups, subscriptions, resource groups, resources, and so onÂ
Azure Cloud Shell
Browser based shell tool that allows you to create configure and manage Azure resources using a shellÂ
Supports Azure PowerShell and Azure command line interface ( a bash shell )Â
No local installation or configuration requiredÂ
Authenticated to your Azure credentialÂ
Use the shell you are most convenient
Azure PowerShell
Shell with which developers, DevOps, and IT professionals run commands called command-lets (cmd-lets)Â
The commands call Azure REST APIs to perform management tasks in AzureÂ
They can run independently or combined to help orchestrate complex actions
Routine setup, tear down, and maintenance of a single resource or multiple connected resourcesÂ
Deployment of an entire infrastructure, which might contain dozens or hundreds of resources from imperative code
Allows to script commands to make the process repeatable and automatable.Â
You can install Azure Cloud shell and configure it via Windows, Linux, and Mac platforms
Azure CLI
Functional equivalent to Azure PowerShell just the syntax is differentÂ
Uses bash commands vs PowerShell, because that is used in the Azure PowerShellÂ
Same benefits as PowerShell
Azure Arc
Allows you to extend Azure compliance and monitoring to your hybrid and multi-cloud configurations via Azure Resource Manager.
Simplifies governance and management by delivering a consistent multi-cloud and on-premises management platform.
Provides a centralized, unified way to:
Managed entire environment together by projecting your existing non-Azure resources into ARMÂ
Manage multi-cloud and hybrid virtual machines, Kubernetes clusters, and databases as if they are on AzureÂ
Use familiar azure services and management capabilities no mater where the resources liveÂ
Continue using traditional ITOps while introducing DevOps practices to support new cloud and native patterns in your environment.Â
Configure custom locations as an abstraction layer on top of Azure Arc-enabled Kubernetes clusters and cluster extensions.
Currently you can manage the following resources outside of Azure
ServersÂ
Kubernetes clustersÂ
Azure data servicesÂ
SQL ServerÂ
Virtual Machines (preview)
Azure Resource Manager and Azure ARM Templates
Deployment and management service for AzureÂ
Provides a management layer that enables you to create, update, and delete resources in your Azure account.Â
When are request comes from any Azure tool, API, or SDK ARM receives the request. It authenticates and authorizes the request.Â
Benefits
Managed your infrastructure through declarative templates rather than scripts. Templates are JSON filesÂ
Deploy, manage, and monitor all resources in the solution as a group, rather than individuallyÂ
Re-deploy your solution throughout the development life-cycle and have confidence your resources are deployed in a consistent stateÂ
Define dependencies and order when deploying themÂ
Apply access control to all services because RBAC is natively integrated into the management platformÂ
Apply tags to resources to logically organize all the resources in your subscriptionÂ
Clarify the organizations billing by viewing costs for a group of resources with the same tag.
ARM Templates
Infrastructure as code - a concept where you manage your infrastructure as lines of code.Â
Uses Azure Cloud Shell, PowerShell, or Azure CLIÂ
Via templates you can describe the resources you want to use using a declarative JSON format.Â
Deployment code is verified before it is runÂ
Ensures resources are created and connected correctly.Â
All resources are created at the same time in parallelÂ
Benefits
Declarative syntax allows you to create and deploy an entire Azure infrastructure declaratively.Â
Repeatable resource, repeatedly deploy your infrastructure throughout the development lifecycle and have confidence your resources are deployed in a consistent manner.Â
Orchestration, no worry about the complexities of ordering operations. It determines the correct order.Â
Modular files, break your templates into smaller, reusable components and link them together at deployment time. Nesting is allowed.Â
Extensibility, you can add PowerShell or bash scripts to your templates. Deployment scripts extend your ability to setup resources during deployment.
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Two calculators to help understand potential expenses.
Pricing calculator
You can estimate the cost of any provisioned resource, including compute, storage, and associated network costs.Â
Even can play with different storage types, access tiers, and redundancy
TCO Calculator
To compare the cost for running an on-premise infrastructure compared to an Azure Cloud infrastructureÂ
You can enter various elements like servers, databases, storage, traffic, etc. to compare.Â
You can add in assumptions for IT labor costs
Azure Cost Management Tool
Provides the ability to quickly check Azure resource costs, create alerts based on resource spend, and create budgets that can be used to automate management of resources.
Cost analysis is a subset of cost management that provides a quick visual for your azure costs.
use cost analysis to explore and analyze your organizational costs.
Cost alerts
Single location for all types of alertsÂ
Budget alerts
Notify when spending, based on usage or cost reaches or exceeds the defined amountÂ
Created using Azure portal or Azure consumption APIÂ
Support both cost based and usage based budgets.
Credit alerts
Alerts when your monetary commitments are consumed.Â
Monetary commitments are for organizations with Enterprise Agreements (EAs)Â
Credit alerts are created automatically at 90% and then 100% of your credit balance.Â
Email sent to account owners
Department spending quota alerts
Alerts when a department reaches a fixed threshold of the quota.Â
Configured in the EA portalÂ
Email sent to department owners at 50 or 75% of the quota
Budgets
When you set a spending limit for Azure.Â
Can set budgets based on a subscription, resource group, service type or other criteria.
Purpose of Tags
Helps you stay organized as your cloud grows.
Can help manage costs
One way to organize is to place resources into subscriptions.
You can also use resource groups to manage related resources.
Resource tags are another way to organize resources.
Tags provide extra information, or metadata, about your resources.
Resource Management -Â Tags enable you to locate and act on resources associated with specific workloads, environments, business units and owners
Cost management and optimization -Â Tags enable you to group resources so that you can report on cost, allocate internal cost centers, track budgets, and forecast estimated costs
Operations management
Tags enable you to group resources according to how critical their availability is to your businessÂ
This grouping helps you formulate SLAs.
Security Tags -Â Enable you to classify data by its security level (public or confidential)
Governance and regulatory compliance
Tags enable you to identify resources that align with governance or regulatory compliance requirements like ISO27001Â
Tags can also be part of your standards enforcement efforts (i.e all resources are tagged to an owner or department)
Workload optimization and automation
Tags can help you visualize all of the resources that participate in complex deploymentsÂ
For example, tag a resource with its associated workload or application name and use software such as DevOps to perform automated tasks on those resources
How do I manage resource tags?
Add, modify, delete through PowerShell, azure cli, resource manager templates, REST api, or azure portalÂ
Use Azure Policy
To enforce tagging rules or conventionsÂ
Add new tags at time of provisioningÂ
Apply tags again when they are removed
Resources donât inherit tags from subscriptions and resource groups.Â
You can create different tagging schemas that change depending on the level (resource, resource group, subscription)Â
You can assign one or more tags to each Azure resource
Templates ensure deployed resources meet corporate standards and government regulatory requirements.
Cloud based auditing helps flag any resource that is out of compliance with corporate standard and provides mitigation strategies.
Cloud models allow you maximum control of security
IaaS - customer manages OS and patchesÂ
PaaS/SaaS - provider manages patches and maintenance automatically
Benefits of Manageability in the Cloud
Speaks to managing your cloud resource
Automatically scale resource deployment based on need
Deploy resources based on preconfigured templates, removing manual configuration
Monitor health of resources and automatically replace failing resources
Receive automatic alerts based on configured metrics, so you are aware of performance in real time
Management in the cloud
Web portalÂ
Command line interfaceÂ
APIsÂ
PowerShell
Cloud Service Types
Infrastructure as a Service
Maximum control for your cloud resources
Cloud provider is only responsible maintaining hardware, network connectivity, and physical security
Shared responsibility model -Â Customer is responsible for everything else: operating system, configuration, maintenance, storage, network, etc.
Scenarios
Lift and shift migration
Standing up cloud resources similar to on-prem data centerÂ
Simply move existing things running on-prem to the cloudÂ
Testing and development
Quickly replicate established configuration for development and test environmentsÂ
Stand up or shut down different environments rapidly.
Platform as a Service
Middle ground between renting space in a data center and paying for a complete deployed solution.Â
Shared responsibility model
Cloud provider maintains physical infrastructure, physical security, and connection to the internet, operating systems, middle ware, development tools, business intelligence services, etc.Â
You do not worry about licensing of said software components
Well suited for a complete development environment without the headaches of maintaining all the development infrastructure
Scenarios
Development framework - PaaS provides a framework that developers can build upon to develop or customize cloud-based applications.Â
Cloud features such as scalability, high-availability, and multi-tenant capabilities are included reducing the amount of coding that developers must doÂ
Analytics or business intelligence: tools provided as a service allow organizations to analyze and mine their data, finding insights, and patterns and predicting outcomes to improve forecasting, products design decisions, investment returns, and other business decisions
Supplier as a Service
Most complete cloud service model from a product perspective.
Shared Responsibility Model
Places most responsibility with the cloud provider and least responsibility with the user.Â
Customer is responsible for data and user access.
Scenarios
Email and messagingÂ
Business productivity appsÂ
Finance and expense tracking
Azure Fundamentals: Describe Azure management and governance
Factors that affect costs in Azure
Azure shifts development costs from Capital expenses (CapEX) to Operational expenses (OpEx)
Factors that can impact the OpEx costs
Resource type
Consumption
Maintenance
Geography
Subscription type
Azure marketplace
Resource Type can be impacted by
Type of resource
Settings of the resource
Azure region
When creating a resource, Azure creates a metered instance for that resource to track usage and generate usage reports, that are later used to calculate the bill
Examples
Storage account
You can configure type as a blob, a performance tier, an access tier, redundancy settings, and a regionÂ
The same account in another region may cost less
Virtual machine
Consider licensing for the operating system or other softwareÂ
Process and number of coresÂ
Attached storage, interfaceÂ
Same VM available in different regions may cost different.
Consumption
Pay as you go has been a consistent themeÂ
If you use more, you pay moreÂ
You can âreserveâ usage of a resource and pay in advance, this can allow receiving discountsÂ
Many services offer this and it can be a discount up to 72%Â
Commitment to consume and pay for a certain usage of that resource in a given period (1 or 3 year increments)Â
If you need to use more, you pay as you go
Maintenance
Resource groups can help you keep your resources organized.Â
To control cost it is important to maintain your cloud environmentÂ
Monitor to ensure resources are decommissions automatically or not. Sometimes they are not decommissioned right away.
Geography
Deployment and network traffic may have different costs based on geography
Network Traffic
Billing zones are a factor in determining the cost of some services.Â
Bandwidth refers to the data moving in and out of Azure data centers.Â
Inbound traffic is free for some servicesÂ
Outbound traffic is not, based on zonesÂ
A zone is a geographical grouping of Azure regions for billing purposes.
Subscription Type
Some have usage allowances which can impact cost
Azure Marketplace
Letâs you purchase Azure based solutions and services form third parties. Purchasing products from the marketplace may require  additional cost. All solutions are compliant with Azure policies and standards
This post will be a running list of all irecovery commands that I have run across.
To begin place your iOS device into recovery mode by turning it off and holding the home button as you connect the USB cable to it. Hold the button until you see on the screen the Recovery Mode screen (iTunes logo and USB wire)
Run iRecovery from the command line by navigating to the folder where it is and running irecovery -s
I recently took on the challenge of applying myself to pass the Azure Fundamentals AZ-900 exam. Every day on a daily basis I work with Azure and AWS, but I had never felt the need to pursue these certifications.Â
Over the last two weeks I reviewed the Microsoft Learning content for the Azure Fundamentals certification and recently passed it on my first attempt.
Below are the notes I had captured that helped me. Hopefully it can help you as well. Even if one person finds these valuable, then I will be happy that I shared them. :)
The content from my notes will be shared across several posts.
Cloud computing
Power and features to run your software
PC is in the cloud provider data center vs you
Pay for services you use
Others manage up keep of computer
Basic services are compute and storage
Compute power
How much processing your computer may do
Pay for resources you use
Storage
Volume of data you can store on your computer
Over time you need more, here you can request more
They make backups and ensure your system is up to date.
Shared responsibility model
Responsibilities are shared between the cloud provider and the consumer
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It has been quite some time since Iâve maintained this site.Â
My goal is to try to reignite the fire that I had some time ago when I was constantly making updates.Â
The threat landscape has changed and so has technology since I last posted.
I need to rethink through what topics matter today, what is relevant, and what will drive value for all of you!
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