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Azure Application Insights | Learn Application Insights


What is Azure Application Insights?

Application Insights is an Application Performance Management (APM) service for developers to monitor live applications. The anomalies in performance are automatically detected. It also includes powerful analytics tools that help in diagnosing issues. The insights help to understand how users are interacting with the application. With the Application Insights, developers can continuously improve performance and usability.

Application Insights works on applications built with various languages like .NET, Node.js, Java, and Python. The applications can be hosted on-premise or on the cloud, or hybrid. It can integrate with DevOps processes. It also integrates with Visual Studio App Center and can monitor telemetry from mobile apps.

All the data in the Application insights service can be exported to a database or any external tools. Application Insights SDKs are available for web services hosted in ASP.NET servers, Java EE, Azure. They are also available for web clients, desktop apps, mobile devices like Windows Phone, iOS, and Android.

How does it Work?

To monitor your application, all you have to do is enable the Application Insights from the Azure portal or install a small instrumentation package (SDK) in your application. The application will be monitored by this instrumentation package. It will use a unique GUID, which is also known as an Instrumentation Key, to direct the telemetry data to an Application Insights resource. 

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Since we install the instrumentation package in the application, it doesn’t have to be hosted on Azure. The application can run anywhere. We can instrument any background components of an application and the JavaScript in the web pages too. Application Insights can also collect telemetry data from Azure diagnostics, Docker logs, or performance counters when they are integrated into Azure Monitor. 

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What does the Application Insights Monitor?

Application Insights focuses on the performance of an application to ease the work of the development team. It monitors the following constraints,

  • Request rates, response times, and failure rates – It tells us which pages are being visited the most and at what times of the day.
  • Dependency rates, response times, and failure rates – It shows any external sources that might slow the application down.
  • Exceptions – It reports both server and browser exceptions. It gives an aggregate statistics of all the instances. We can further drill down to get statistics of individual instances.
  • It will also monitor the page views and load performance collected from the user’s browser.
  • It monitors AJAX calls from web pages, users, and session counts.
  • It will show the performance of memory, CPU, and network usage.
  • We can get host diagnostics from Docker or Azure.
  • We can correlate events with requests using the diagnostic trace logs of the application.
  • It also shows the custom events or metrics that the developer includes in the code.

Uses of Application Insights

Once we install Application Insights for an application, we can get the following benefits.

  • The load, responsiveness, and the performance of page loads, dependencies, AJAX calls can be known through an intuitive application dashboard.
  • We can identify the slowest requests and determine the requests that are failing often.
  • When a new release of an application is deployed, the statistics of it can be seen through a live stream.
  • If users are affected, we can get an alert so we can check how many users are being affected.
  • If there are any request failures, we can correlate them with the exceptions, dependency calls, and traces.
  • When a new feature of the app has to be deployed, we can measure the effectiveness of it.

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Limitations of Azure Application Insights

Like any other solution, Application Insights has some limitations.

  • If your code uses dynamic SQL, the Application Insights collects the full query into Azure, which might result in uploading sensitive data contained in the query.
  • The reports show up to the server and database level. But it cannot monitor individual SQL queries on how long they are being executed.
  • When you add Application Insights and deploy the application to Azure, it won’t collect the SQL queries unless a site extension is installed for it.
  • It cannot collect first chance exceptions.
  • It cannot show common exceptions across all applications.
  • If you are using ASP.NET for your application, Application Insights does not support asynchronous HttpClient calls.
  • There is no alert severity specified.
  • We cannot configure alerts to go to specific distribution lists based on severity.

Data collection, retention, and storage of Application Insights

When Azure Application Insights SDK is installed in your application, it starts sending telemetry data from your app to the cloud. Each SDK uses different techniques to collect telemetry data from different kinds of applications. You can also include custom telemetry to send your data. Azure runs some processes called availability tests to web applications regularly. The results from the test will be sent back to the Application Insights service.

You can test which data is being sent by the SDK. You can view the data in the output windows of the IDE and browser while testing the application. The data in the Application Insights service can retain up to 730 days. Users can set up a retention duration. The debug snapshots are stored for 15 days in the Application Insights service.

If the SDK is not able to reach the endpoint, the telemetry channels store the data in local storage temporarily by creating temp files. Once the issue is resolved, the new data, along with the persisted data, will be sent to Azure by the telemetry channel.

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Enable Application Insights for your Application

Create Application Insights Service

Navigate to the Azure portal at https://portal.azure.com/ and login to your account. Click on ‘+ New’ from the left side menu. Search for ‘Application Insights’ in the search bar. You can see the service in the search results. Click on it to open the service and click on ‘Create’. Give a name for your service, select your application type from the drop-down menu, and select your subscription. Choose ‘Create new’ for the ‘Resource Group’ field and give the same name that you gave for the service. Select a location and click on ‘Create’.

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Go to the newly created resource group and click on ‘app insights resource’. You will get the details of the resource. Copy the ‘Instrumentation Key’ from the page.

Add the Instrumentation key to the Application

Open Visual Studio and navigate to the appsettings.json file of your application. Add the below code in the file.

"Application Insights": {
"InstrumentationKey": "Your_instrumentation_key"
}

Replace the ‘Your_instrumentation_key’ with the one you copied before. It appears as a NuGet package. Go to the package.json file in your application, and you can see the Application Insights package added. You have successfully configured Application Insights to your application.

View the telemetry data

Launch the application from Visual Studio and play around with it. Stop the application. Right-click on the application, select ‘Application Insights’, and select the ‘Search Debug Session Telemetry’ option. You can see the telemetry data captured by your application. You can also see the details in Application Insights. Right-click on the application, select ‘Application Insights’, and select the ‘Open Application Insights Portal’ option.

The Application Insights portal opens up, and you can see the telemetry data collected from your application. You can drill down to see the page load metrics and more.

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Conclusion

Application Insights is a simple way for developers to detect and diagnose application performance issues of live applications. The SDKs vary for different applications and different platforms. Each SDK component sends different data. So choose one that is suitable for your application and install it. You can also include code in your application to send unhandled exceptions. The Azure Application Insights has a built-in map feature that can be used to identify the performance of dependencies.



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Cyber Security vs Data Science| Which to choose as a career


Last updated on
Jan 19, 2024


Saritha Reddy


Hrushikesh


  • Reviewed by


  • Hrushikesh


    (Ethical hacker, Certified Security Analyst)

Cyber Security VS Data Science – Table of Content

What is cyber security?

The cyber security industry is a fascinating field in the IT sector and apt for those who are ready to accept the challenges. The term cyber security can be defined as it is a type of IT application that designs and implements secure network solutions specially designed to act as a shield against hackers, persistence attacks, and any cyber-attacks. The cyber security market is diverse that is ranging from a cyber professional service endpoint to mobile security. It has a diverse range of applications from financial service, retail, health care, infrastructure, and transport. There is huge demand has been created for cyber security professionals, and the companies looking out to hire cyber security engineers. The companies we would like to mention are PWC, Deloitte, Telesoft technologies, VMware, Intel, and many more.

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What is Data Science?

 Data science is also known as data-driven science and is also defined as a data tool that helps to solve complex data-related problems using patterns, models, and analytics. It is also an interdisciplinary field about scientific methods, processes, and systems to extract knowledge or data insights in various forms, either in structured or unstructured formats or you can define it as data mining

Cyber Security VS Data Science:

Here we thought to list out the major differences between cyber security and data science based on professional categories.

.Most IT professionals one or some other day think about a kick start their career as a cyber security engineer or data scientist. This section clears all your doubts related to choosing the right career path.

Cyber security engineer roles and responsibilities:

 Cyber security engineers are those who involve in designing and implementing security solutions to defend against various threats, cyber-attacks, and malware attacks. They are also involved in testing and monitoring the system devices to make us assure that all the system devices are up-to-date and ready to defend against any type of attack.

Data scientist roles and responsibilities:

A data scientist is responsible for collecting, analyzing, and also interpreting a large volume of data. The data scientist role is a combination of mathematician, scientist, statistician, and computer professional.

Cyber security engineer job description:

Here is a list of cyber security engineer job descriptions:

  •  Implementing security firewalls to networking systems.
  • Determining the access authorizations.
  • Securing the information technology infrastructure.
  • Involve in monitoring the network for signs of cyberattacks.
  • Eliminate the potential threats or attempted breaches.
  • Identifying the cyber attackers.
  • Informing the organization’s workers about security policies.

Data scientist job description:

Here is a list of data scientist job descriptions:

  • Designing the data modeling processes or applications (for ex: Denodo).
  • Building the machine learning algorithms or models.
  • Developing and maintaining the databases.
  • Assessing the quality of datasets.
  • Cleansing the unstructured/ unpatterned data.
  • Preparing the data reports for the executive and project team.
  • Proposing solutions to the executive team.
  • Creating data visualizations to present information.
  • Collaborating with other teams.
  • Combining models through ensemble modeling.

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Cyber security engineer skills

To become a cyber security engineer, the following are the mandatory skillsets anyone must have:

  • Secure coding practices, ethical hacking, and threat modeling.
  • Proficiency in programming languages like python, C++, Java, Ruby, Go, and Power shells.
  • IDS/IPS penetration and vulnerability testing.
  • Firewall and intrusion detection and prevention protocols.
  • Have basic knowledge on how to use various operating systems such as Windows, Linux, and UNIX.
  • Virtualization technologies and MYSQL database server.
  • Application security and encryption technologies.

Data scientist skill:

To become a data scientist, you should have these mandatory skill sets.

  • Data scientist professionals must have strong foundation knowledge in mathematics and statistics.
  • Additionally, they should have strong programming knowledge in Python or R programming and later use them for performing various operations like data mining, manipulations, calculations, graphical display, and also running embedded systems.
  • Data scientist professionals should have additional knowledge in data statistical modeling software such as SQL database and the Hadoop platform.
  • In addition to the above-mentioned skill sets, data scientists must have strong communication, problem-solving, collaboration, and out-of-the-box thinking capabilities.

Cyber security career path:

  • Cyber security engineers must hold a bachelor’s degree in computer science, and IT system engineering.
  • They should possess a minimum of two years of work experience in cybersecurity-related roles such as incident detection, responses, and forensics.  
  • . Should have experience with the functionalities, operations, and maintenance of firewalls and various forms of endpoint system device security.
  •   Must have proficiency in languages and tools such as C++, Java, Node, Python, Go, Power shells, and Go.
  •  They should have the ability to work in fast-paced work environments, often under some work pressure.

Data scientist career path:

  • The basic education qualification required to become a data scientist is an undergraduate or bachelor’s degree in computer science. 
  • Senior-level data scientist professionals must have a master’s degree with a few years of work experience.
  • Taking some certification exams also boosts up their professional career.

 Cyber security engineer salary:

As per the indeed.com job portal, the basic salary for any cyber security engineer professional ranging from $77,000, and an experienced cyber security engineer earns more than $135,000 depending on the individual’s experience, and knowledge.

Data scientist salary:

As per the indeed.com job portal, an average salary for any data scientist ranges from $80,000 and an experienced data scientist earns more than $145,000 depending on an individual’s experience, and knowledge.

Cyber security engineer certification:

Below is the list of major cyber security engineer certifications:

  • COBIT 5 control objectives for information and related technologies.
  • COBIT 5 Professional certification.
  • CompTIA security+certification -SYO-601.
  • CISA certification and training
  • CND – certified network defender
  • CHFI – Computer hacking forensic investigator certification
  • CISSP certification

Data science certification

  • SAS Certification. 
  •  SAS Certified Big Data Professional. 
  •  SAS Certified Advanced Analytics Professional. 
  •  Senior Data Scientist. 
  • Principal Data Scientist.
  • Microsoft Certified: Azure Data Scientist Associate. 
  •   IBM Data Science Professional Certificate.

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Benefits of Cyber Security:

Once you know the definition, you will start thinking about the key benefits of this domain. This section is dedicated to fulfilling your requirements. The following are the key benefits of using Cyber security:

  • Cyber security will defend us from critical attacks.
  • It helps us to browse the safe website.
  • Internet security processes all the incoming and outgoing data on your computer.
  • Security will defend from hacks and viruses.
  • The application of cyber security used in our PC needs update every week.
  • The security developers will update their database every week once. Hence the new virus was also detected.

Benefits of Data Science:

Here also we are going to make a list of key benefits of data science:

  • Empowering management and officers to make better decisions.
  • Data scientists direct the actions based on trends which in turn help in defining goals.
  • Data scientist challenge the staff to adopt the best practices and focus on issues that matter.
  • Identifying opportunities and decision making with quantifiable, data-driven evidence.
  • Improving fraud detections in financial institutions and also identifying the best delivery routes.

Key features of Cybers Security:

Below are the key features of cyber security:

  • Identify management unique IDs for personal and products for authentication.
  • Access control specifies the role and other constraints for authorization.
  • Agree on cryptographic details for securing network protocols.
  • Validate the source and integrity of the software and framework.
  • Validate the integrity of the process data. 
  • Validate the integrity of the OT settings.

Key features of Data Science:

Below are the key features of data science:

  • Responsive data construct and flexible to manage.
  • Easily trainable and parallel neural networking.
  • Opens source and feature columns.
  • Availability of statistical distribution.
  • Layered components and feature columns.

frequently asked Cyber security Interview questions and Answers !!

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Final Words:

In this Cyber security VS data science post, we did not concentrate not only on explaining basic things but also tried to explain the professional differences too. Both data science and cyber security are the hottest domains, to become a master or expertise in these technologies is a dream of many people. The main purpose to develop these kinds of articles are to help our readers to enhance their skill sets with appropriate domains and also choose the right career. We are hoping that you people enjoy reading our blogs. Stay tuned for more updates.

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  1. Cyber Security Technologies
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