azure

Living in a world of rapid technological changes, one of the things that is indispensable is data security. If you are a developer, chances are that you have at least some idea about cloud services and encryption. Here we will try to understand what is Azure Storage Encryption at Rest and why it is worth looking into.

What’s encryption, and why should we care?

Encryption simply means shuffling information so unauthorized users can’t read the original data. There is a secret code that only a person with the correct key can use and read the information data. It is just like sending a postcard, but in a language no one can understand unless they have your special decoder.

It solves the following problems:

Keeps your data private, an unauthorized person cant get access to the correct information
Protects it from hackers, even if someone steals it, it’s useless without the key.
Helps instill confidence in your customers because their information is secure.

Significance of Cloud Encryption

Your data if stored in the cloud such as Azure, AWS, or Google Cloud, is stored on remote servers. Cloud encryption ensures that it’s secure even there. There are usually three types:

Encryption in Transit: Protects data when data is in motion such as while uploading/downloading files.
In-use encryption: It keeps the data secure when being accessed or processed.
At-rest encryption: When data is just sitting in storage, this makes sure that your data is encrypted.
With cyber threats on the rise and with privacy laws getting more significant, GDPR encryption is no longer optional, rather it is the first line of defense.

Azure Storage is the cloud-based file storage for Microsoft. Azure Storage can store files, databases, and even big video files. It is scalable from very small to very large and secure. It is one of the most important storage options available for various storage needs in an Azure application.

What Is Encryption at Rest?

Think of a vault full of confidential information, now imagine that a thief breaks in and finds all this information. Encryption at Rest protects the information in storage from being read by unauthorized persons without the correct key.

Azure Storage Encryption at Rest

Azure Storage makes encrypting at rest very easy. By default, all of your data is encrypted without doing anything additional. This indicates that every blob or file, queue or message, table, or structured data stored in Azure is secure.

Microsoft uses a super-strong algorithm called AES-256 encryption, which is also trusted by banks and governments. You can manage the encryption keys yourself, or let Azure handle them for you-a win-win for flexibility and control.

Why Does Encryption at Rest Matter

Let’s try to understand with some real-world scenarios:

Securing Customer Data in an E-commerce App

You are storing customer information, including names, addresses, and purchase history, in Azure Blob Storage. If there is encryption at rest, even if any miscreant gains access to storage, the data would be illegible without an encryption key.

Protecting Health Records

For a healthcare application, sensitive patient data must be stored. Encryption at rest ensures that the law is followed. If data is stolen, it will still be encrypted and therefore unreadable.

Backing Up Important Files

Consider a backup of your company’s financial records to Azure. Encryption at rest ensures those records are secure, even in the event of compromise of the storage location.

Important points about Azure Storage Encryption at Rest

Key Management: Microsoft-managed keys offer simplicity, while BYOK enables customers to own encryption keys for tight control.
Azure Key Vault Integration: Store and manage your encryption keys safely in Azure Key Vault.
Resource Configuration: Configuration in storage account creation should be done via Azure Portal, CLI, or SDKs.
Auditing and Monitoring: Utilize Azure Monitor and diagnostic logs to monitor the encryption activities, ensuring encryption activities occur within organizational security policies.

Encryption in the cloud is important for building a secure and reliable system. With Azure Storage Encryption at Rest, this encryption is easy to deal with and automated and you may focus on writing code rather than worrying about data breaches.

ashish

codecompiled.com

Follow on:

When I initially thought about taking azure certification related to data science or machine learning , the most obvious question was which one should I go for considering multiple factors such as:

Value for my time and money investment
Finding the certification most aligned with my career goals and interests
Is it even worth it preparing for the certification as will always find critics who would say knowledge and experience matters more than any examination.

It was a learning experience for me just trying to understand about these.And I am glad that I went through all this.I had some experience of developing ML applications and also had experience of consuming different Azure services in developing applications such as ServiceBus,Azure Storage,Functions and Logic Apps.But as you start exploring Machine Learning options in Azure you will soon realize that there are lot of services for different scenarios.When it comes to developing any Machine Learning solution, it has certain life cycle or steps which include:

Defining the problem
Getting the data
Preparing the data
Training the model
Integrating and Monitoring the model

Considering Azure certifications for AI,ML and Data Engineering

While preparing for these certifications not only did I learn the relevance of these steps and how to implement them but which of the Azure services would best implement the requirement,

For example questions such as should we develop custom solutions using Azure Machine Learning or we can use the prebuilt models and use cognitive services.What are the different concepts which one should be familiar with while working in any AI or ML solution.There are some major differences in what is covered in these.I hope this will help you understand more and select the one which is most appropriate for you.

I have listed three of the Azure certifications related to AI/ML and Data Engineering on Azure.Which certification to take depends upon whether you’re drawn to the analytical side with data science or want to dive deep into AI model-building.

When considering AI/ML certifications in Azure, here are some relevant options:

Designing and implementing a data science solution on Azure , DP-100

Microsoft says this is suitable for candidates who have”subject matter expertise in applying data science and machine learning to implement and run machine learning workloads on Azure.”What i found was this significantly involves working with Azure Machine Learning.This means that you will be working on things such as training ,deploying and monitoring the models.This will require good understanding of AI concepts such as Evaluation metrics for measuring the effectiveness of a machine learning model.You should also know about different algorithms and which one should be used in a given scenario.If you like working with languages such as Python, R, or SQL or tools and libraries like scikit-learn and pandas then you may find this interesting.

After clearing this exam you earn the cadential Microsoft Certified: Azure Data Scientist Associate.

Designing and Implementing an Azure AI Solution ,AI-102

If you are developer working on developing applications using languages such as C# or Python and looking to integrate AI capabilities in your application then you would be interested in this certification.You should be familiar with REST-based API concepts and how to integrate it in your application.You should know about integrating Azure AI Services in your application. Azure AI Services is a collection of services that are building blocks of AI functionality.There are services for Computer vision,Azure AI Language for developing NLP solutions,Azure AI Search,Bot Services, and Azure OpenAI.Preparing for this exam you will get familiar with tasks such as integrating chat bot in your application.

After clearing this exam you earn the cadential Microsoft Certified:Azure AI Engineer Associate

Data Engineering on Microsoft Azure, DP-203

As the name suggests this is all about managing data in Azure..If you are a DBA or have experience working with database then you should consider this. This requires familiarity building analytics solutions.It sets you up nicely to handle tasks involving massive amounts of data flow, storage transformation through platforms such as Azure Data Lake & Synapse Analytics which play pivotal roles in establishing solid ML workflows.

This include familiarity with tasks such as:

Integrating, transforming, and consolidating data
Knowledge of Modern data warehouse and Big data
Knowledge of data processing languages mainly – SQL,Python,Scala
Knowledge of data architecture patterns.
Ability to create data processing solutions using services such as ADF,- Azure Databricks, Azure Data Lake Storage.

After clearing this exam you earn the cadential Microsoft Certified: Azure Data Engineer Associate

Choosing the Right Certification for You

For choosing the relevant certification consider your career goals:

If you’re interested in building AI solutions, consider the Azure AI Engineer certification. It’s ideal for developers who want to work directly with AI models, chatbots, and other smart applications.
For those with a data science background For developers who want to implementing end to end machine learning solutions on Azure, the Data Scientist certification would be appropriate. It’s especially useful if you plan to work with data modeling and analytics on the Azure platform.
Data-focused developers aiming to support ML models with clean and reliable data might prefer the Data Engineer certification. This is a great choice for those who enjoy the “backend” side of AI.

Preparing for the Exam and Study Tips

Though I didn’t find these too challenging but I would say preparation depends on your prior experience with the services and concepts expected in these exams.As I had some experience developing AI and ML applications so I would say AI-102 was the easiest for me, followed by DP-203 and DP-100.It’s also because the expectations are quite different in each of these exams.In DP-100 you should be familiar developing end to end ML solutions on Azure.While AI-102 is focussed more on consuming Azure cognitive services and DP-203 is about data architecture and data processing.

So I would say depending on your prior experience, a 1-3-month plan, dedicating 4-8 hours per week, is a quite realistic target. I found Microsoft’s Learn platform helpful in preparation as they are focused on the syllabus for these exams and also help build confidence.Following are the links for the 3 certifications:

DP-100

AI-102

DP-203

Combining these with tutorials and hands-on labs will make a difference. Building a small project on Azure can solidify learning and add to your portfolio as well.

Final Thoughts

So reasons why you should consider taking these certification exams are:

Cloud skills are essential in today’s tech landscape, especially when it comes to AI, data science, and machine learning.Investing time in these credentials will certainly help will gaining knowledge around these.
Because Microsoft Azure integrates with many AI and data science tools, this expertise helps with both skills and strong portfolio development.