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What is Azure HDInsight?

Today, our lives are greatly influenced by technology. We use gadgets everyday that help us make our lives easier. All these gadgets and tools produce and consume data. It is, therefore. necessary to maintain infrastructures that can cater to the data needs of the current systems.

The size of this data we are talking about is big. According to Forbes.com, in 2019, Americans used 4,416,720 GB of internet data including 188,000,000 emails, 18,100,000 texts, and 4,497,420 Google searches every single minute.

If this was the data consumption in one country in a single minute, you can imagine how big the data consumption of the world today would be. A huge portion of this data needs to be stored and processed. This is where tools such as Apache Spark, Hadoop, Hive, etc., come to the picture.

In this blog about big data analytics, we will discuss Azure HDInsight through the following topics.

• What is Azure HDInsight?
• Azure HDInsight Features
• Azure HDInsight Architecture
• Azure HDInsight Metastore Best Practices
• Azure HDInsight Migration
• Azure HDInsight Security and DevOps
• Azure HDInsight Uses
  • Data Warehousing
  • Internet of Things (IoT)
  • Data Science
  • Hybrid Cloud
• Azure HDinsight Pricing
• Conclusion

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What is Azure HDInsight?

Apache Hadoop is the most commonly used tool for big data analytics. Hadoop can help in storing, processing, and analyzing large volumes of streaming or historical data. It also has the capability to be scaled up as and when required. Azure HDInsight helps us to use open source frameworks, such as Hadoop, to process big data by providing a one-stop solution.

Azure HDInsight is a service offered by Microsoft, that enables us to use open source frameworks for big data analytics. Azure HDInsight allows the use of frameworks like Hadoop, Apache Spark, Apache Hive, LLAP, Apache Kafka, Apache Storm, R, etc., for processing large volumes of data. These tools can be used on data to perform extract, transform, and load (ETL,) data warehousing, machine learning, and IoT.

Check out this Azure tutorial to learn more about Azure!

Azure HDInsight Features

The main features of Azure HDInsight that set it apart are:

• Cloud and on-premises availability: Azure HDInsight can help us in big data analytics using Hadoop, Spark, interactive query (LLAP,) Kafka, Storm, etc., on the cloud as well as on-premises.

• Scalable and economical: HDInsight can be scaled up or down as and when required. The ability to be scaled also means that you have to pay for only what you use. You can upgrade your HDInsight when required, and this eliminates having to pay for unused resources.

• Security: Azure HDInsight protects your assets with industry-standard security. The encryption and integration with Active Directory makes sure that your assets are safe in the Azure Virtual Network.

• Monitoring and analytics: HDInsight’s integration with Azure Monitor helps us to closely watch what is happening on our clusters and take actions based on that.

• Global availability: Azure HDInsight is more globally available than any other big data analytics service.

• Highly productive: Productive tools for Hadoop and Spark can be used in HDInsight in different development environments like Visual Studio, VSCode, Eclipse, and IntelliJ for Scala, Python, R, Java, etc.

Azure HDInsight Architecture

Before getting into the uses of Azure HDInsight, let’s understand how to choose the right Architecture for Azure HDInsight. Listed below are best practices for Azure HDInsight Architecture:

• It is recommended that you migrate an on-premises Hadoop cluster to Azure HDInsight using multiple workload clusters rather than a single cluster. A large number of clusters will increase your costs unnecessarily if used over time.
• On-demand transient clusters are used so that the clusters are deleted after the workload is complete. As a result, resource costs may be reduced since HDInsight clusters are rarely used. By deleting a cluster, you will not be deleting the associated meta-stores or storage accounts, so you can use them to recreate the cluster if necessary.
• In HDInsight clusters, as storage-and-compute can be used from Azure Storage, Azure Data Lake Storage, or both, it is best to separate data storage from processing. In addition to reducing storage costs, it will also allow you to use transient clusters, share data, and scale storage and compute independently.

Azure HDInsight Metastore Best Practices

The Apache Hive Metastore is an important aspect of the Apache Hadoop architecture since it serves as a central schema repository for other big data access resources including Apache Spark, Interactive Query (LLAP), Presto, and Apache Pig. It is worth noting that HDInsight uses Azure SQL as its Hive metastore database.

There are two types when it comes to HDInsight metastores: default metastores or custom metastores.

• A default metastore can be created for free for any cluster type, but if one is created it cannot be shared.
• The use of custom metastores is recommended for production clusters, since they can be created and removed without loss of metadata. It is suggested to use a custom metastore to isolate compute and metadata and to periodically back it up.

HDInsight immediately deletes the Hive metastore upon cluster destruction. By storing Hive metastore in Azure DB, you will not have to remove it when deleting the cluster.

Azure Log Analysis and Azure Portal provide monitoring tools for monitoring metadata store performance. If you are using HDInsight in the same region as your metastore, make sure that they are in the same location.

Azure HDInsight Migration

The following are best practices for Azure HDInsight migration:

Script migration or replication can be used to migrate Hive metastore. You can migrate Hive metastore with scripts by creating Hive DDLs from the existing metastore, editing the generated DDL to replace HDFS URLs with WASB/ADLS/ABFS URLs, and then running the modified DDL on the metastore. Both the on-premises and cloud versions of the metastore need to be compatible.

Migration Using DB Replication: When migrating your Hive metastores using DB replication, you can use the Hive MetaTool to replace HDFS URLs with WASB/ADLS/ABFS URLs. Here’s an example code:

./hive --service metatool -updateLocation 
 hdfs://nn1:8020/ 
wasb://@.blob.core.windows.net/

Azure offers two approaches for migrating data from on-premises: migrating offline or migrating over TLS. It will probably depend on how much data you need to migrate to determine the best choice for you.

Migrating over TLS: Microsoft Azure Storage Explorer, Azure Copy, Azure Powershell, and Azure CLI can be used to migrate data over TLS to Azure storage.

Migrating offline: DataBox, DataBox Disk, and Data Box Heavy devices are also available for the offline shipment of large amounts of data to Azure. As an alternative, you can also use native tools such as Apache Hadoop DistCp, Azure Data Factory, or AzureCp to transfer data over the network.

Azure HDInsight Security and DevOps

To protect and maintain the cluster, it is wise to use Enterprise Security Package (ESP), which provides directory-based authentication, multi user assistance, and role-based access control. The ESP framework can be used with a range of clusters, including Apache Hadoop, Apache Spark, Apache Hbase, Apache Kafka, and Interactive Query (Hive LLAP).

To ensure your HDInsight deployment is secure, you need to take the following steps:

Azure Monitor: Use the Azure Monitor service for monitoring and alerting.

Stay on top of updates: Always upgrade HDInsight to the latest version, install OS patches, and reboot your nodes.

Enforce end-to-end enterprise security, with features such as auditing, encryption, authentication, authorization, and a private pipeline.

Azure Storage Keys should also be encrypted. By using Shared Access Signatures (SAS), you can limit access to your Azure storage resources. Azure Storage automatically encrypts data written to it using Storage Service Encryption (SSE) and replication.

Make sure to update HDInsight at regular intervals. In order to do this, you can follow the steps outlined below:

• Set up a new HDInsight cluster and apply the most recent update to HDInsight.
• Ensure the current cluster has enough workers and workloads.
• As needed, change applications, or workloads.
• A backup should be made of all temporary data stored on cluster nodes.
• Delete the existing cluster.
• Install HDInsight on a fresh new cluster with the same default data and metastore as previously.
• Import any temporary file backups.
• Finish processing jobs with the new cluster or start new ones.

Azure HDInsight Uses

The main scenarios in which we can use Azure HDInsight are:

Data Warehousing

Data warehousing is the storage of large volumes of data for retrieval and analysis at any point of time. Data warehouses are maintained by businesses to analyze them and make strategic decisions based on them.

HDInsight can be used for data warehousing by performing queries at very large scales on structured or unstructured data.

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Internet of Things (IoT)

We are surrounded by a large number of smart devices that make our life easier. These IoT-enabled devices help us in taking off the task of making small decisions regarding our devices.

IoT requires the processing and analytics of data coming in from millions of smart devices. This data is the backbone of IoT and maintaining and processing it is vital for the proper functioning of IoT-enabled devices.

Azure HDInsight can help in processing large volumes of data coming from numerous devices.

Data Science

Building applications that can analyze data and do tasks based on it are vital for AI-enabled solutions. These apps need to be powerful enough to process large volumes of data and make decisions based on that.

An example worth noting would be the software used in self-driving cars. This software has to constantly keep on learning from new experiences as well as from historical data to make real-time decisions.

Azure HDInsight helps in making applications that can extract vital information from analyzing large volumes of data.

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Hybrid Cloud

A hybrid cloud is when companies use both public and private cloud for their workflows. In this, they will get the benefits of both such as security, scalability, flexibility, etc.

Azure HDInsight can be used to extend an company’s on-premises infrastructure to the cloud for better analytics and processing in a hybrid situation.

Azure HDInsight Pricing

The pricing is based on the quantity of the cluster and nodes that are used. The pricing also changes based on the region.

The pricing by the hour for central India is:

Component	Pricing
Hadoop, Spark, Interactive Query, Storm, HBase	Base price/node-hour + ₹0/core-hour
HDInsight Machine Learning Service	Base price/node-hour + ₹1.153/core-hour
Enterprise Security Package	Base price/node-hour + ₹0.721/core-hour

The pricing by the hour for central US is:

Component	Pricing
Hadoop, Spark, Interactive Query, Storm, HBase	Base price/node-hour + $0/core-hour
HDInsight Machine Learning Services	Base price/node-hour + $0.016/core-hour
Enterprise Security Package	Base price/node-hour + $0.01/core-hour

For more details about the pricing of nodes, you can visit Azure HDInsight Pricing.

Conclusion

Azure HDInsight provides a unified solution for using open source frameworks, such as Hadoop, Spark, etc., for big data analytics. This enables Azure HDInsight to be used in multiple scenarios; it also renders itself as a powerful data analytics tool for both cloud and on-premises.

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