Zara  Bryant

Zara Bryant

1626793407

Managers: Learn Azure Your Way Pt. 2

Are you a manager and want to help your team learn more about Azure, but just don’t know where to start? Join April Edwards on a guided tour of Microsoft Certifications, Instructor Led Training, Microsoft Docs and GitHub with tips and tricks for managers.

  • 0:00 Introduction
  • 0:42 Why should managers pay attention to certification?
  • 1:27 How can managers get started with certification?
  • 1:51 What are some features that managers should pay attention to?
  • 2:29 What is one thing that managers might not know about Certifications?
  • 3:27 Why should managers pay attention to Instructor-Led Training?
  • 4:01 How should a manager get started with Instructor-Led Training?
  • 4:29 What are some tips and tricks you would share with managers?
  • 5:10 What’s one thing that managers might not know about Instructor-Led Training?
  • 6:02 What is Microsoft Docs, and how can a manager get started with it?
  • 7:09 What are some tips or tricks for using Microsoft Docs?
  • 8:34 How does Microsoft Docs help people succeed?
  • 9:19 Why should managers go to GitHub?
  • 9:55 How can managers get started with using GitHub?
  • 10:22 What GitHub features should managers pay attention to?
  • 11:06 What’s one thing that managers might not know about GitHub?

#azure

Managers: Learn Azure Your Way Pt. 2
Eric  Bukenya

Eric Bukenya

1624713540

Learn NoSQL in Azure: Diving Deeper into Azure Cosmos DB

This article is a part of the series – Learn NoSQL in Azure where we explore Azure Cosmos DB as a part of the non-relational database system used widely for a variety of applications. Azure Cosmos DB is a part of Microsoft’s serverless databases on Azure which is highly scalable and distributed across all locations that run on Azure. It is offered as a platform as a service (PAAS) from Azure and you can develop databases that have a very high throughput and very low latency. Using Azure Cosmos DB, customers can replicate their data across multiple locations across the globe and also across multiple locations within the same region. This makes Cosmos DB a highly available database service with almost 99.999% availability for reads and writes for multi-region modes and almost 99.99% availability for single-region modes.

In this article, we will focus more on how Azure Cosmos DB works behind the scenes and how can you get started with it using the Azure Portal. We will also explore how Cosmos DB is priced and understand the pricing model in detail.

How Azure Cosmos DB works

As already mentioned, Azure Cosmos DB is a multi-modal NoSQL database service that is geographically distributed across multiple Azure locations. This helps customers to deploy the databases across multiple locations around the globe. This is beneficial as it helps to reduce the read latency when the users use the application.

As you can see in the figure above, Azure Cosmos DB is distributed across the globe. Let’s suppose you have a web application that is hosted in India. In that case, the NoSQL database in India will be considered as the master database for writes and all the other databases can be considered as a read replicas. Whenever new data is generated, it is written to the database in India first and then it is synchronized with the other databases.

Consistency Levels

While maintaining data over multiple regions, the most common challenge is the latency as when the data is made available to the other databases. For example, when data is written to the database in India, users from India will be able to see that data sooner than users from the US. This is due to the latency in synchronization between the two regions. In order to overcome this, there are a few modes that customers can choose from and define how often or how soon they want their data to be made available in the other regions. Azure Cosmos DB offers five levels of consistency which are as follows:

  • Strong
  • Bounded staleness
  • Session
  • Consistent prefix
  • Eventual

In most common NoSQL databases, there are only two levels – Strong and EventualStrong being the most consistent level while Eventual is the least. However, as we move from Strong to Eventual, consistency decreases but availability and throughput increase. This is a trade-off that customers need to decide based on the criticality of their applications. If you want to read in more detail about the consistency levels, the official guide from Microsoft is the easiest to understand. You can refer to it here.

Azure Cosmos DB Pricing Model

Now that we have some idea about working with the NoSQL database – Azure Cosmos DB on Azure, let us try to understand how the database is priced. In order to work with any cloud-based services, it is essential that you have a sound knowledge of how the services are charged, otherwise, you might end up paying something much higher than your expectations.

If you browse to the pricing page of Azure Cosmos DB, you can see that there are two modes in which the database services are billed.

  • Database Operations – Whenever you execute or run queries against your NoSQL database, there are some resources being used. Azure terms these usages in terms of Request Units or RU. The amount of RU consumed per second is aggregated and billed
  • Consumed Storage – As you start storing data in your database, it will take up some space in order to store that data. This storage is billed per the standard SSD-based storage across any Azure locations globally

Let’s learn about this in more detail.

#azure #azure cosmos db #nosql #azure #nosql in azure #azure cosmos db

Learn NoSQL in Azure: Diving Deeper into Azure Cosmos DB
Layla  Gerhold

Layla Gerhold

1597160392

Azure Machine Learning Service

In a series of blog posts, I am planning to write down my experiences of training, deploying and managing models and running pipelines with Azure Machine Learning Service. This is part-1 where I will be walking you through the creation of workspace in Azure ML service

About Azure Machine Learning Service

Azure Machine Learning Service is a cloud based platform from Microsoft to train, deploy, automate, manage and track ML models. It has a facility to build models by using drag-drop components in Designer along with traditional code based model building. Azure ML service makes our job very ease in maintaining developed models and also helps in hassle free deployment of models in lower(QA, Unit) and higher(Prod) environments as APIs. It is integrated with various components in Azure like Azure Kubernetes Services, **Azure Databricks, Azure Monitor, Azure Storage accounts, Azure Pipelines, MLFlow, Kubeflow **to carry out various activities which will be discussed in upcoming posts.

Why Azure Machine Learning Service

In the process of building models, one need to play around with various hyperparameters and use various techniques. Also one need to scale out the resources for training the model if the dataset is huge. Bringing your model development and deployment to cloud makes your job easy. In particular Azure Machine Learning Service has below advantages.

  1. Simplifies model management
  2. Automated machine learning simplifies model building
  3. Scales out training to GPU cluster or CPU cluster or Azure Databricks whenever needed with inbuilt integration
  4. Deployment of models to production with Azure Kubernetes Service or Azure IOT edge is very simple.

#microsoft-azure #cloud-machine-learning #deep-learning #machine-learning #azure-machine-learning

Azure Machine Learning Service

TDE customer-managed keys in Azure SQL Database

Azure SQL Database is a Platform-as-a-Service (PaaS) solution that offers managed database service. Azure DB provides many features such as automatic database tuning, vulnerability assessment, automated patching, performance tuning, alerts. It provides a 99.995% availability SLA for the Zone redundant database in the business-critical service tier.

This article explores Transparent Data Encryption (TDE) using the customer-managed key in Azure SQL Database.

Introduction

In an on-premise SQL Server instance, database administrators can enable Transparent Data Encryption (TDE) for securing the data and log files of a database. It is helpful to protect you from a malicious threat by encrypting data at rest. You get real-time encryption of the database, transaction log files and associated backup files without any configuration changes at the application end.

The high-level steps for implementing the TDE encryption are as below.

  • Create Master Key
  • Configure a Certificate protected by the master key
  • Create Database Encryption Key
  • Enable Encryption
  • Backup Certificate
  • Restoring a Certificate

In the following image, we can visualize the TDE hierarchy. If you are new to TDE, you can refer to the following articles to get familiar with TDE.

Azure SQL DB TDE using Service Managed Key

If you migrate your on-premise databases to Azure SQL Database, TDE is enabled by default. You can connect to the Azure portal and verify the configuration. It uses an Azure service managed key. It is Azure responsibility to manage the key without any user intervention. Microsoft automatically uses its internal security policy for rotating these certificates. It protects the certificate root key using its internal secret store.

As shown below, my [labazuresql] database is encrypted using the Transparent data encryption.

#azure #sql azure #azure sql database #azure sql #customer-managed

TDE customer-managed keys in Azure SQL Database
Aisu  Joesph

Aisu Joesph

1626494598

Managed Identities in Azure with Terraform

In this article, I’ll explain the concepts around Managed Identities in Azure, the different types of managed identities, and how to assign them to a VM. Then we will show how to authenticate Terraform to Azure using the managed identity. Lastly, we will configure an Application Gateway to use a managed identity in order to access secrets in an Azure Key Vault.

What is a managed identity?

Managed identities provide an identity for applications to use when connecting to resources that support Azure Active Directory (Azure AD) authentication.

Crucially the management of credentials is handled by the managed identity (hence the word managed), and not by the application or the developer.

Using Managed Identities to Authenticate with Terraform

You can use a _system-assigned _managed identity to authenticate when using Terraform. The managed identity will need to be assigned RBAC permissions on the subscription, with a role of either Owner, or both Contributor and User access administrator.

Azure Application Gateway and Key Vault with Managed Identity in Terraform

Manged identities can also be created and managed using Terraform and then assigned a role. These can then be tied to a resource, like a VM or Application Gateway.

#azure-devops #azure-managed-identities #azure-active-directory #azure #terraform

Managed Identities in Azure with Terraform
Aisu  Joesph

Aisu Joesph

1626490533

Azure Series #2: Single Server Deployment (Output)

No organization that is on the growth path or intending to have a more customer base and new entry into the market will restrict its infrastructure and design for one Database option. There are two levels of Database selection

  • a.  **The needs assessment **
  • **b. Selecting the kind of database **
  • c. Selection of Queues for communication
  • d. Selecting the technology player

Options to choose from:

  1. Transactional Databases:
    • Azure selection — Data Factory, Redis, CosmosDB, Azure SQL, Postgres SQL, MySQL, MariaDB, SQL Database, Maria DB, Managed Server
  2. Data warehousing:
    • Azure selection — CosmosDB
    • Delta Lake — Data Brick’s Lakehouse Architecture.
  3. Non-Relational Database:
  4. _- _Azure selection — CosmosDB
  5. Data Lake:
    • Azure Data Lake
    • Delta Lake — Data Bricks.
  6. Big Data and Analytics:
    • Data Bricks
    • Azure — HDInsights, Azure Synapse Analytics, Event Hubs, Data Lake Storage gen1, Azure Data Explorer Clusters, Data Factories, Azure Data Bricks, Analytics Services, Stream Analytics, Website UI, Cognitive Search, PowerBI, Queries, Reports.
  7. Machine Learning:
    • Azure — Azure Synapse Analytics, Machine Learning, Genomics accounts, Bot Services, Machine Learning Studio, Cognitive Services, Bonsai.

Key Data platform services would like to highlight

  • 1. Azure Data Factory (ADF)
  • 2. Azure Synapse Analytics
  • 3. Azure Stream Analytics
  • 4. Azure Databricks
  • 5. Azure Cognitive Services
  • 6. Azure Data Lake Storage
  • 7. Azure HDInsight
  • 8. Azure CosmosDB
  • 9. Azure SQL Database

#azure-databricks #azure #microsoft-azure-analytics #azure-data-factory #azure series

Azure Series #2: Single Server Deployment (Output)