Azure provides over 1O00 cloud-services that enable you to do everything form running existing applications on Virtual Machines(VMs) to exploring new software paradigms. The Azure cloud provides the various infrastructure for enabling compute, storage, and networking resources that are needed to build cloud-hosted/cloud-native applications.
Microsoft Azure provides a more global model that is present more than any other cloud provider with over 54 regions distributed worldwide.
Some of the Azure cloud services:
These services enable a user to deploy and manage VMs.
Azure offers SQL and NoSQL database services to integrate your various preferred application services.
This offers ways to control who can access the services and use encryption keys to protect sensitive information in the cloud.
This enables the resource to be connected and manage traffic and diagnostics, load balancing, DNS hosting and network protection
Azure provides high availability, redundant, secure, and scalable services to persistent storage.
The CDN services include on-demand streaming, digital, content cache right protections, encoding, and media playback, and indexing.
These are a wide range of services that infuse artificial intelligence, machine learning, and cognitive computing capabilities
Azure has implemented various services to ensure that there is proper authorization when accessing your Azure resources.
With the constant change of technology, it seems like on-premises mainframes will be replaced with Cloud Provider data centers. This is because cloud computing brings a lot of benefits and seeks to solve the hassle of on-premise data centers. These are some benefits of the cloud:
Azure allows users to start immediately spin up multiple resources at a global scale in a matter of minutes. Compare to conventional static datacenter which could require hardware, staffing resources, and more time than needed.
This could be the greatest benefit of using Azure. Azure billing works with the pay-as-go model. This gives users the benefits of paying for services when they are in use. The customer is then billed monthly. With a traditional data center, there would be huge Capital Expenditure( CapEx) such as Physical servers and infrastructure devices like routers, load balancers, and network cables, and **Operational Expenditure (OpEx) **like IT expertise, power bills and building rent.
There is always a chance that disaster will strike and important data can be lost. With MicrosoftAzure this data can be made high availability, redundant and robust. This can protect your data in case of any unplanned outcome.
Secure to its Core
Azure has built its datacenter around security. Security is not optional it is at the core of the platform. Customers can choose from a wide range of Security tools, firewalls, encryption, and authentication methods to secure their data, they can even encrypt the data in a way that makes it accessible only to ones with security keys.
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K-means is one of the simplest unsupervised machine learning algorithms that solve the well-known data clustering problem. Clustering is one of the most common data analysis tasks used to get an intuition about data structure. It is defined as finding the subgroups in the data such that each data points in different clusters are very different. We are trying to find the homogeneous subgroups within the data. Each group’s data points are similarly based on similarity metrics like a Euclidean-based distance or correlation-based distance.
The algorithm can do clustering analysis based on features or samples. We try to find the subcategory of sampling based on attributes or try to find the subcategory of parts based on samples. The practical applications of such a procedure are many: the best use of clustering in amazon and Netflix recommended system, given a medical image of a group of cells, a clustering algorithm could aid in identifying the centers of the cells; looking at the GPS data of a user’s mobile device, their more frequently visited locations within a certain radius can be revealed; for any set of unlabeled observations, clustering helps establish the existence of some structure of data that might indicate that the data is separable.
K-means the clustering algorithm whose primary goal is to group similar elements or data points into a cluster.
K in k-means represents the number of clusters.
A cluster refers to a collection of data points aggregated together because of certain similarities.
K-means clustering is an iterative algorithm that starts with k random numbers used as mean values to define clusters. Data points belong to the group represented by the mean value to which they are closest. This mean value co-ordinates called the centroid.
Iteratively, the mean value of each cluster’s data points is computed, and the new mean values are used to restart the process till the mean stops changing. The disadvantage of k-means is that it a local search procedure and could miss global patterns.
The k initial centroids can be randomly selected. Another approach of determining k is to compute the entire dataset’s mean and add _k _random co-ordinates to it to make k initial points. Another method is to determine the principal component of the data and divide it into _k _equal partitions. The mean of each section can be used as initial centroids.
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It’s one of the leaders in the cloud computing space, but what is Azure cloud and what is it used for? This ACG Fundamentals episode will give you a high-level overview of Microsoft Azure cloud, so you can understand this cloud computing platform’s strengths and weaknesses, use cases, market share and competition, and how the Azure services all work together.
Azure Infrastructure (1:07)
Azure Competitors (3:43)
Azure Strengths and Weaknesses (4:18)
Azure Use Cases (6:12)
What’s Next? (7:39)
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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
Options to choose from:
Key Data platform services would like to highlight
#azure-databricks #azure #microsoft-azure-analytics #azure-data-factory #azure series
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.
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.
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:
In most common NoSQL databases, there are only two levels – Strong and Eventual. Strong 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.
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.
Let’s learn about this in more detail.
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A middleware is a software service that glues together multiple services. In today’s business needs, multiple software services and technologies need to work together and communicate with each other. It is not necessary that these distributed software services are compatible with each other and will be able to communicate.
Example Business Case
We have to develop a software service in which we have geo-coordinates of a location and we need to get weather information of the city based on those coordinates. We have a system X that needs to communicate with another system Y. These are distributed systems. System X has information about geo coordinates and system Y will store weather information of the city based on those coordinates.
We will develop a middleware between system X and system Y.
#functions #microsoft azure service #microsoft azure #azure