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Introduction
Azure HDInsight is a service that Microsoft offers. It enables us to use open frameworks for big data analytics. It helps us to process big data by providing a one-stop solution. Azure HDInsight is a cloud distribution of Hadoop Components. In the coming sections, we will learn more about it in detail.
What is Azure HDInsight?
Azure HDInsight helps organizations process large amounts of streaming or historical data. It helps us use open-source frameworks, such as Hadoop, to process big data by providing a one-stop solution. Hadoop is a tool that helps in storing, processing, and analyzing large volumes of streaming or historical data. Not only Hadoop, but we can also use other open-source frameworks like Apache Spark, Apache Hive, Apache Storm, Apache Kafka, LLAP, R, and many more, for processing a vast amount of data. These tools can be used to perform extract, transform, and load (ETL) on data, data warehousing, IoT, and Machine Learning. We will be learning about these terms in the coming sections.
An HDInsight cluster has several Linux Azure Virtual Machines (nodes) that can be used for distributed processing of tasks. It handles implementation details of installation and configuration of individual nodes, so we can only have to provide general configuration information. An HDInsight cluster is deployed by selecting a cluster type which is necessary to determine what topology of virtual machines are deployed.
Types of Clusters in HDInsight
Node types
Every Cluster can have different types of nodes that have a specific purpose in the Cluster. In this table, we will see some of the node types:
Azure HDInsight Features
Let's see some of the main features of Azure HDInsight:
Cloud and on-premises availability: It can help us in big data analytics using Hadoop, interactive query (LLAP), Spark, Kafka, Storm, etc., on the cloud as well as on-premises.
Scalable and economical: It can scale down or up according to the requirement, meaning we have to pay for only what we use. We can upgrade our HDInsight when required, eliminating having to pay for unused resources.
Security: Azure HDInsight makes sure that we remain safe in the Azure Virtual Network through encryption and integration with Active Directory.
Monitoring and analytics: It helps us to closely monitor our clusters to analyze and make decisions based on that.
Global availability: It is available more globally than any other big data analytics service.
Highly productive: Productive tools for Spark and Hadoop can be used in HDInsight in different development environments like Visual Studio, Eclipse, VSCode, and IntelliJ for Scala, Python, R, Java, etc.
Azure HDInsight Metastore Best Practices
The Apache Hive Metastore is a central schema repository for big data access resources like Apache Spark, Interactive Query (LLAP), Presto, and Apache Pagan, and that's why it is one of the important aspects of the Apache Hadoop architecture. It is interesting to know that HDInsight uses Azure SQL as its Hive metastore database.
When it comes to HDInsight, there are two types: default metastores and custom metastores.
A default metastore can be created for any cluster type, but it cannot be shared if one is created.
It is recommended to use custom metastores as they can be created and removed without the loss of metadata. To isolate compute and metadata, it is suggested to use custom metadata.
Upon cluster destruction, HDInsight immediately deletes the Hive metastore. We don't have to remove it when deleting the Cluster by storing Hive metastore in Azure DB. We can also monitor metadata store performance using the tools provided by the Azure Log Analysis and Azure Portal. Always ensure that HDInsight and metastore are in the exact location if we use them in the same region.
Azure HDInsight Uses
There are different scenarios in which we can use Azure HDInsight. Some of the important ones are discussed below:
Data Warehousing
Data Warehouse is a centralized repository of integrated data. It is the storage of a huge amount of data that can be retrieved at any point for analysis from one or more disparate sources. Businesses used warehouses to make strategic decisions based on these data by analyzing them. HDInsight can perform queries on the structure and unstructured data stored in the warehouse on a vast scale.
IoT refers to the millions of physical devices connected to each other on the Internet. Nowadays, our life is surrounded by lots of smart devices, which makes our life comfortable. These IoT-enabled devices help us complete the task of making small decisions regarding our devices by analyzing and processing data coming from millions of smart devices around the world. So from this, we can conclude that data is the backbone of IoT and this data maintenance and processing is vital for the proper functioning these IoT-enabled devices. And Azure HDInsight can help in processing these large volumes of data from different smart devices worldwide.
Data Science uses very complex machine learning algorithms to build predictive models. It is the domain of study which requires vast volumes of data. Applications that are based on data science need to be powerful enough to process large volumes of data which can be easily done with the help of Azure HDInsight, and make decisions based on that. A real-time example can be evaluating an athlete player's performance.
When companies use both public and private clouds for their workflows, then it is known as Hybrid Cloud. In this type, we can enjoy the benefits of both, including the benefits of security, flexibility, scalability, etc.
In a hybrid situation, Azure HDInsight can be used to extend a company's on-premises infrastructure to the cloud for better analytics and processing.
Azure HDInsight pricing is directly dependent on the quantity of use of the Cluster and node.
It also varies based on the region
Frequently Asked Questions
Does migrating a Hive metastore also migrate the default policies of the Ranger database?
No, the policy definition is in the Ranger database, so migrating the Ranger database will migrate its policy.
What is the difference between cloud and Azure?
Microsoft Azure services and Google cloud both offer virtual machines (VMs). Microsoft calls them Azure virtual machines, while Google calls them Compute Engines. Azure offers boot-disk-only and full machine VMs where the cloud is boot-disk-only. Both have autoscaling included.
Can I install Data Analytics Studio (DAS) and an ESP cluster?
No, DAS is not supported on ESP clusters.
Can I share a metastore across multiple clusters?
Yes, you can share custom metastore across multiple clusters as long as they're using the same version of HDInsight.
Conclusion
In this article, we have discussed What is Azure HDInsight, architecture, what are the features and uses of Azure HDInsight. We have also discussed pricing of it based on the region.
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