An introduction to the Hadoop ecosystem on Azure HDInsight

This article provides an introduction to Hadoop on Azure HDInsight, its ecosystem, and big data. Learn about the Hadoop components, common terminology, and scenarios for big data analysis.

What is Hadoop on HDInsight?

Hadoop refers to an ecosystem of open-source software that is a framework for distributed processing, storing, and analysis of big data sets on clusters of computers. Azure HDInsight makes the Hadoop components from the Hortonworks Data Platform (HDP) distribution available in the cloud, deploys managed clusters with high reliability and availability, and provides enterprise-grade security and governance with Active Directory.

Apache Hadoop was the original open-source project for big data processing. Following was the development of related software and utilities considered part of the Hadoop technology stack, including Apache Hive, Apache HBase, Apache Spark, Apache Kafka, and many others. See Overview of the Hadoop ecosystem in HDInsight for details.

What is big data?

Big data describes any large body of digital information, from the text in a Twitter feed, to the sensor information from industrial equipment, to information about customer browsing and purchases on a website. Big data can be historical (meaning stored data) or real-time (meaning streamed directly from the source). Big data is being collected in ever-escalating volumes, at increasingly higher velocities, and in an expanding variety formats.

For big data to provide actionable intelligence or insight, you must collect relevant data and ask the right questions. You must also make sure the data is accessible, cleaned, analyzed, and then presented in a useful way. That's where big data analysis on Hadoop in HDInsight can help.

Overview of the Hadoop ecosystem in HDInsight

HDInsight is a cloud distribution on Microsoft Azure of the rapidly expanding Apache Hadoop technology stack for big data analysis. It includes implementations of Apache Spark, HBase, Kafka, Storm, Pig, Hive, Interactive Hive, Sqoop, Oozie, Ambari, and so on. HDInsight also integrates with business intelligence (BI) tools such as Power BI, Excel, SQL Server Analysis Services, and SQL Server Reporting Services.

Hadoop, HBase, Spark, Kafka, Interactive Hive, Storm, customized, and other clusters

HDInsight offers the following cluster types:

Example customization scripts

Script actions are Bash scripts on Linux that run during cluster provisioning, and can be used to install additional components on the cluster.

The following example scripts are provided by the HDInsight team:

  • Hue: A set of web applications used to interact with a cluster. Linux clusters only.
  • Giraph: Graph processing to model relationships between things or people.
  • R: An open-source language and environment for statistical computing used in machine learning.
  • Solr: An enterprise-scale search platform that allows full-text search on data.

For information on developing your own Script Actions, see Script Action development with HDInsight.

What are the Hadoop components and utilities?

The following components and utilities are included on HDInsight clusters.

  • Ambari: Cluster provisioning, management, monitoring, and utilities.
  • Avro (Microsoft .NET Library for Avro): Data serialization for the Microsoft .NET environment.
  • Hive & HCatalog: Structured Query Language (SQL)-like querying, and a table and storage management layer.
  • Mahout: For scalable machine learning applications.
  • MapReduce: Legacy framework for Hadoop distributed processing and resource management. See YARN, the next-generation resource framework.
  • Oozie: Workflow management.
  • Phoenix: Relational database layer over HBase.
  • Pig: Simpler scripting for MapReduce transformations.
  • Sqoop: Data import and export.
  • Tez: Allows data-intensive processes to run efficiently at scale.
  • YARN: Part of the Hadoop core library and next generation of the MapReduce software framework.
  • ZooKeeper: Coordination of processes in distributed systems.

For information on the specific components and version information, see Hadoop components, versioning, and service offerings in HDInsight


Apache Ambari is for provisioning, managing and monitoring Apache Hadoop clusters. It includes an intuitive collection of operator tools and a robust set of APIs that hide the complexity of Hadoop, simplifying the operation of clusters. HDInsight clusters on Linux provide both the Ambari web UI and the Ambari REST API, while clusters on Windows provide a subset of the REST API. Ambari Views on HDInsight clusters allow plug-in UI capabilities.

See Manage HDInsight clusters using Ambari (Linux only), Monitor Hadoop clusters in HDInsight using the Ambari API, and Apache Ambari API reference.

Avro (Microsoft .NET Library for Avro)

The Microsoft .NET Library for Avro implements the Apache Avro compact binary data interchange format for serialization for the Microsoft .NET environment. It uses JavaScript Object Notation (JSON) to define a language-agnostic schema that underwrites language interoperability, meaning data serialized in one language can be read in another. Detailed information on the format can be found in the Apache Avro Specification. The format of Avro files supports the distributed MapReduce programming model. Files are “splittable”, meaning you can seek any point in a file and start reading from a particular block. To find out how, see Serialize data with the Microsoft .NET Library for Avro.


Hadoop Distributed File System (HDFS) is a distributed file system that, with MapReduce and YARN, is the core of the Hadoop ecosystem. HDFS is the standard file system for Hadoop clusters on HDInsight.

Hive & HCatalog

Apache Hive is data warehouse software built on Hadoop that allows you to query and manage large datasets in distributed storage by using a SQL-like language called HiveQL. Hive, like Pig, is an abstraction on top of MapReduce. When run, Hive translates queries into a series of MapReduce jobs. Hive is conceptually closer to a relational database management system than Pig, and is therefore appropriate for use with more structured data. For unstructured data, Pig is the better choice. See Use Hive with Hadoop in HDInsight.

Apache HCatalog is a table and storage management layer for Hadoop that presents you with a relational view of data. In HCatalog, you can read and write files in any format for which a Hive SerDe (serializer-deserializer) can be written.


Apache Mahout is a scalable library of machine learning algorithms that run on Hadoop. Using principles of statistics, machine learning applications teach systems to learn from data and to use past outcomes to determine future behavior. See Generate movie recommendations using Mahout on Hadoop.


MapReduce is the legacy software framework for Hadoop for writing applications to batch process big data sets in parallel. A MapReduce job splits large datasets and organizes the data into key-value pairs for processing.

YARN is the Hadoop next-generation resource manager and application framework, and is referred to as MapReduce 2.0. MapReduce jobs run on YARN.

For more information on MapReduce, see MapReduce in the Hadoop Wiki.


Apache Oozie is a workflow coordination system that manages Hadoop jobs. It is integrated with the Hadoop stack and supports Hadoop jobs for MapReduce, Pig, Hive, and Sqoop. It can also be used to schedule jobs specific to a system, like Java programs or shell scripts. See Use Oozie with Hadoop.


Apache Phoenix is a relational database layer over HBase. Phoenix includes a JDBC driver that allows you to query and manage SQL tables directly. Phoenix translates queries and other statements into native NoSQL API calls - instead of using MapReduce - thus enabling faster applications on top of NoSQL stores. See Use Apache Phoenix and SQuirreL with HBase clusters.


Apache Pig is a high-level platform that allows you to perform complex MapReduce transformations on very large datasets by using a simple scripting language called Pig Latin. Pig translates the Pig Latin scripts so they’ll run within Hadoop. You can create User-Defined Functions (UDFs) to extend Pig Latin. See Use Pig with Hadoop.


Apache Sqoop is tool that transfers bulk data between Hadoop and relational databases such a SQL, or other structured data stores, as efficiently as possible. See Use Sqoop with Hadoop.


Apache Tez is an application framework built on Hadoop YARN that executes complex, acyclic graphs of general data processing. It's a more flexible and powerful successor to the MapReduce framework that allows data-intensive processes, such as Hive, to run more efficiently at scale. See "Use Apache Tez for improved performance" in Use Hive and HiveQL.


Apache YARN is the next generation of MapReduce (MapReduce 2.0, or MRv2) and supports data processing scenarios beyond MapReduce batch processing with greater scalability and real-time processing. YARN provides resource management and a distributed application framework. MapReduce jobs run on YARN.

To learn about YARN, see Apache Hadoop NextGen MapReduce (YARN).


Apache ZooKeeper coordinates processes in large distributed systems by means of a shared hierarchical namespace of data registers (znodes). Znodes contain small amounts of meta information needed to coordinate processes: status, location, configuration, and so on.

Programming languages on HDInsight

HDInsight clusters--Hadoop, HBase, Storm, and Spark clusters--support a number of programming languages, but some aren't installed by default. For libraries, modules, or packages not installed by default, use a script action to install the component. See Script action development with HDInsight.

Default programming language support

By default, HDInsight clusters support:

  • Java
  • Python

Additional languages can be installed using script actions: Script action development with HDInsight.

Java virtual machine (JVM) languages

Many languages other than Java can be run using a Java virtual machine (JVM); however, running some of these languages may require additional components installed on the cluster.

These JVM-based languages are supported on HDInsight clusters:

  • Clojure
  • Jython (Python for Java)
  • Scala

Hadoop-specific languages

HDInsight clusters support the following languages that are specific to the Hadoop ecosystem:

  • Pig Latin for Pig jobs
  • HiveQL for Hive jobs and SparkSQL

Advantages of Hadoop in the cloud

As part of the Azure cloud ecosystem, Hadoop in HDInsight offers a number of benefits, among them:

To read more about the advantages on Hadoop in HDInsight, see the Azure features page for HDInsight.

HDInsight Standard and HDInsight Premium

HDInsight provides big data cloud offerings in two categories, Standard and Premium. HDInsight Standard provides an enterprise-scale cluster that organizations can use to run their big data workloads. HDInsight Premium builds on that and provides advanced analytical and security capabilities for an HDInsight cluster. For more information, see Azure HDInsight Premium

Resources for learning more about big-data analysis, Hadoop, and HDInsight

Build on this introduction to Hadoop in the cloud and big data analysis with the resources below.

Hadoop documentation for HDInsight

Apache Hadoop

  • Apache Hadoop: Learn more about the Apache Hadoop software library, a framework that allows for the distributed processing of large datasets across clusters of computers.
  • HDFS: Learn more about the architecture and design of the Hadoop Distributed File System, the primary storage system used by Hadoop applications.
  • MapReduce Tutorial: Learn more about the programming framework for writing Hadoop applications that rapidly process large amounts of data in parallel on large clusters of compute nodes.

Microsoft business intelligence

Familiar business intelligence (BI) tools - such as Excel, PowerPivot, SQL Server Analysis Services, and SQL Server Reporting Services - retrieve, analyze, and report data integrated with HDInsight by using either the Power Query add-in or the Microsoft Hive ODBC Driver.

These BI tools can help in your big-data analysis: