Introduction to Azure Services
In this article, we will see an outline of Azure Services. In today’s world, a huge amount of data gets generated on a daily basis for storage and faster processing of data to get business insights and to improve the business strategy by applying machine learning and statistical algorithms are the basic needs. To store such a huge amount of data, organizations need to create and maintain on-premise data centers which incur a huge cost. To reduce the cost and for ease of maintenance, task organizations are moving their data to the cloud.
Top 3 Azure Services
Microsoft azure is a lead player when it comes to cloud computing. Azure provides various services that are divided into 3 types:
- IAAS (infrastructure as a service)
- PAAS (Platform as a service)
- SAAS (Software as a service)
1. IAAS (Infrastructure as a Service)
In IAAS, setting up infrastructure such as computing platform, virtual network, storage, etc will be provided by Microsoft. The client will be having full control over the infrastructure and based on requirements can increase or decrease storage capacity and compute power. This helps organizations to save on-premise cluster maintenance cost and provide smooth processing.
Example: Virtual machines or storage accounts etc.
2. PAAS (Platform as a Service)
In PAAS, the Platform according to the client’s requirement is delivered via the web. And all the software maintenance activities such as operating system updates, software updates, infrastructure, and storage are maintained by Microsoft. The client doesn’t need to bother regarding platform maintenance and only needs to concentrate on the software development part. It allows an application to be developed on top of the platform and make it scalable and highly available. Such a type of service also helps in application migration from a legacy platform to a cloud platform.
Example: HDInsight cluster, operating systems, etc.
3. SaaS (Software as a Service)
In SaaS, there are applications provided as a service commonly used for business purposes. Application services are delivered over the internet which helps to reduce maintenance, download, installation, etc. These types of applications are generally hosted from a central location and are accessible over the internet.
Example: MS Outlook and MS Office are the most commonly used software as a service.
Commonly Used Azure Services
The Microsoft Azure cloud offers a lot of tools for almost any scenario you might need. The most commonly used Azure Services are listed below:
1. Azure HDInsight
- It is a cloud-based service provided by Microsoft, which is a managed cluster based on top of Hortonworks data platform includes the implementation of Hadoop tools like Spark, Oozie, Sqoop, Hive, Pig, Ambari, HBase, etc.HDInsight by default uses blob as a storage service, but it can also be configured to use ADLS and ADLS gen2. It is highly available and supports features such as scalability, Auto-scaling, Role-based access control, authentication, etc.
- It also integrates well with reporting tools such as PowerBi, Zeppelin, Tableau, Apache DBeaver, etc. It also supports trending technologies such as Machine Learning and the Internet of things etc which allows a business to develop there big data application and process massive data without any technical staff to manage on-premise clusters etc. It allows organizations just need to focus on application development rather than maintenance of clusters.
2. Azure Data Factory (ADF)
- Azure data factory is a service used to deploy end to end workflow in the form of pipelines. Developers can create a pipeline by integrating separate services provided by azure cloud such as storage, HDInsight cluster, SQL server, etc. There are various drag-drop activities provided within the data factory to connect hive, spark, etc. Their activities can be configured to clean, transform or mask data in different types. Their pipeline supports conditional, irrational, and lookup services which help in building pipelines.
- Pipelines can be triggered based on events or can be scheduled on time. Data Factory provides azure preview canvas where you can see pipeline layouts and can establish relationships, dependencies between source, sink respectively across the pipeline. It can interact with Azure SQL Server, Azure Database for MySQL, storage like blob and ADLS, etc which helps in migration of applications from legacy system to cloud very smoothly. Data Factory also allows the creation of computing cluster HDInsight or Databricks at run time to avoid unnecessary spinning costs.
3. Resource Group
Azure resource group is a service that helps to keep all the resources within a group required to deploy an azure solution. In resources group resources such as storage account, clusters, logic apps, function apps, SQL servers, etc are grouped to maintain and deploy them from one place.
4. Storage Accounts
Azure storage account is a key player when it comes to data storage on the cloud. It provides a pay-as-you-go facility to only pay the cost of the resources which are used for data. Its capacity can be expanded limitlessly. Storage accounts used to store data in the form of blob, tables, files or queues.
- Blob storage can be used to store unstructured data such as pictures, raw data or semi-structured data such as CSV or XML files. Their files are stored in a directory like structure called a container.
- Azure tables, as the name, suggests stored data in the form of tables. These tables are NoSQL tables i.e. follow schema-less kind of structure. These tables can be created very easily and can be accessed in code with the help of the provided URL. It stored data in key, value-form in the backend.
- Azure File Storage is mainly used when the legacy system’s file server needs to be migrated. It stores data on a file share that can be mounted as a local directory on azure VM’s and can be accessed by an on-premise application using Rest-API.
- Azure queues, as the name suggests used to queue-up messages, transfer them within the application. The process can interact with queues, pick-up messages, perform the required operation and probably save results either on storage or database.
Hence Azure Services helps enterprises to improve their business strategy by developing large scale big data solutions on a huge amount of data, with faster processing and with the support of machine learning, AI, statistical algorithms.
This is a guide to Azure Services. Here we discuss the basic concept and we also looked at Azure which offers a wide range of services such as IAAS, PAAS, SAAS. You can also go through our other suggested articles to learn more –