An Introduction to Azure HDInsight

Apache Hadoop is an open-source, fast, and scalable framework that manages and processes exceptionally large volumes of data. We have already discussed Apache Hadoop and Hadoop ecosystem in detail in a previous blog. Hadoop is used by data scientists for offline or batch processing. The framework can be scaled up by adding nodes in the cluster. 

Enterprises who are already into Big Data analytics and have used Hadoop face some significant challenges like

  • Changing Data Characteristics: Every two or three years the nature and volume of data are growing exponentially. So, data management becomes difficult.
  • Cost and Performance: Although the per-unit price of Big Data is low, the sheer scale of it makes it expensive. Companies gaining strategic insights from their data find it difficult to strike the right balance between price and performance.
  • Fragmented Architecture: Enterprises have made significant investments in the on-premise system. With everything moving to the cloud they find it difficult to keep the current systems up and running and with minimal investments to reach the cloud.

This is where Azure HDInsight comes into the picture. Azure HDInsight by Microsoft is a cloud-based service for processing and analysing large volumes of streaming and historical data for enterprises. 

HDInsight offers Spark, Hadoop, Spark, Hive, LLAP, Kafka, Storm, R, and many more open-source frameworks to build big data applications. Its features include simplified data extraction, data transformation, data loading, and data warehousing, machine learning, and IoT.

Important Aspects of HDInsight

Uses Hortonworks Data Platform

The Azure HDInsight is based on the Hortonworks Data Platform (HDP). Horton Data Platform is the complete set of the most important components of the Hadoop ecosystem. HDInsight uses the HDP platform as it is, but the only difference is that the storage layer used is the Azure Data Lake or other Azure storage systems like Blob.

The HDP with its tools and features helps enterprises gain valuable insights by analysing both structured and unstructured data. It is also the reason behind HDInsight’s robust data processing and analytics services.

Cluster Creation and Scaling

Often developers must put in extra effort and time to create clusters and scale existing clusters depending on the data volume. HDInsight makes it possible for developers to scale workloads vertically. This can bring down costs by creating clusters only when required and paying only for the resources being used.

Another major feature of HDInsight is that the HDInsight clusters are managed by Microsoft with 99.9 percent service legal agreement (SLA). Hence, a large amount of data can be processed without focusing on cluster management. 

Also, there is full monitoring support with Microsoft operations management suite i.e. HDInsight with the Azure log analytics can display all the alerts and events from the HDInsight cluster on a single dashboard along with other Azure services.

Enterprise Level Security and Compliance

With HDInsight prime focus is given on security and it meets government compliance standards like GDPR. Your enterprise data can be protected using Kerberos authentication and Apache Ranger based access control and integration with Azure Active Directory.

Productivity and Flexibility

With HDInsight developers can use rich productive tools for Hadoop and Spark along with their preferred development environments like Visual Studio, VSCode, Eclipse, and IntelliJ for Scala, Python, R, Java, and .NET support. Popular notebooks such as Jupyter and Zeppelin are also available for data scientists.

Use Cases

As a robust cloud-based service Azure HDInsight can be used for a wide variety of scenarios in the processing process massive amounts of data and build custom big data solutions. It can handle historical data (data that’s already been collected and stored) or streaming data (real-time data streamed from the source).

A few of these scenarios include

Extract, Transform, and Load (ETL), or Batch Processing as it is commonly called is a process where structured and unstructured data is extracted from a wide variety of sources. It is then converted into a structured format and transferred into a data store. The stored data can later be used for data science or data warehousing.

Data Warehousing and Data Science

Enterprises sit on a vast amount of data (which can be many petabytes or terabytes). HDInsight can be used to carry out interactive queries from these structured or unstructured data in any format. These can be integrated with BI tools to build models to derive valuable business insights. Azure Machine Learning along with HDInsight can also be utilized to predict future trends. The above are only a few user instances supported by HDInsight. It is an expanding ecosystem with an array of popular data applications that can be installed from the Azure Marketplace with a simple click for tasks ranging from interactive analytics to application migration.

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