BIG DATA and its terms


Azure HDInsight is a service that deploys and provisions Apache™ Hadoop® clusters in the cloud, providing a software framework designed to manage, analyze, and report on big data.

Big data

Data is described as “big data” to indicate that it is being collected in ever escalating volumes, at increasingly high velocities, and for a widening variety of unstructured formats and variable semantic contexts. Big data collection does not provide value to an enterprise on its own. For big data to provide value in the form of actionable intelligence or insight, not only must the right questions be asked and data relevant to the issues be collected, the data must be accessible, cleaned, analyzed, and then presented in a useful way, often in combination with data from various other sources that establish perspective and context in what is now referred to as a mashup.

Apache Hadoop

Apache Hadoop is a software framework that facilitates big data management and analysis. Apache Hadoop core provides reliable data storage with the Hadoop Distributed File System (HDFS), and a simple MapReduce programming model to process and analyze, in parallel, the data stored in this distributed system. HDFS uses data replication to address hardware failure issues that arise when deploying such highly distributed systems.


To simplify the complexities of analyzing unstructured data from various sources, the MapReduce programming model provides a core abstraction that underwrites closure for map and reduce operations. The MapReduce programming model views all of its jobs as computations over datasets consisting of key-value pairs. So both input and output files must contain datasets that consist only of key-value pairs. The primary takeaway from this constraint is the MapReduce jobs are, as a result, composable.

Other Hadoop-related projects such as Pig and Hive are built on top of HDFS and the MapReduce framework. Projects such as these are used to provide a simpler way to manage a cluster than working with the MapReduce programs directly. Pig, for example, enables you to write programs using a procedural language called Pig Latin that are compiled to MapReduce programs on the cluster. It also provides fluent controls to manage data flow. Hive is a data warehouse infrastructure that provides a table abstraction for data in files stored in a cluster which can then be queried using SQL-like statements in a declarative language called HiveQL.


Azure HDInsight makes Apache Hadoop available as a service in the cloud. It makes the HDFS/MapReduce software framework and related projects such as Pig and Hive available in a simpler, more scalable, and cost-efficient environment.

One of the primary efficiencies introduced by HDInsight is in how it manages and stores data. HDInsight uses Azure Blob storage as the default file system. Blob storage and HDFS are distinct file systems that are optimized, respectively, for the storage of data and for computations on that data.

  • Azure Blob storage provides a highly scalable and available, low cost, long term, and shareable storage option for data that is to be processed using HDInsight.
  • The Hadoop clusters deployed by HDInsight on HDFS are optimized for running MapReduce computational tasks on the data.

HDInsight clusters are deployed in Azure on compute nodes to execute MapReduce tasks and can be dropped by users once these tasks have been completed. Keeping the data in the HDFS clusters after computations have been completed would be an expensive way to store this data. Blob storage is a robust, general purpose Azure storage solution. So storing data in Blob storage enables the clusters used for computation to be safely deleted without losing user data. But Blob storage is not just a low cost solution: It provides a full-featured HDFS file system interface that provides a seamless experience to customers by enabling the full set of components in the Hadoop ecosystem to operate (by default) directly on the data that it manages.

HDInsight uses Azure PowerShell to configure, run, and post-process Hadoop jobs. HDInsight also provides a Sqoop connector that can be used to import data from an Azure SQL database to HDFS or to export data to an Azure SQL database from HDFS.

Microsoft Power Query for Excel is available for importing data from Azure HDInsight or any HDFS into Excel. This add-on enhances the self-service BI experience in Excel by simplifying data discovery and access to a broad range of data sources. In addition to Power Query, the Microsoft Hive ODBC Driver is available to integrate business intelligence (BI) tools such as Excel, SQL Server Analysis Services, and Reporting Services, facilitating and simplifying end-to-end data analysis.

The Hadoop ecosystem on Azure


HDInsight offers a framework implementing Microsoft’s cloud-based solution for handling big data. This federated ecosystem manages and analyses large data amounts, exploiting the parallel processing capabilities of the MapReduce programming model. The Apache-compatible Hadoop technologies that can be used with HDInsight are itemized and briefly described in this section.

HDInsight provides implementations of Hive and Pig to integrate data processing and warehousing capabilities. Microsoft’s big data solution integrates with Microsoft’s BI tools, such as SQL Server Analysis Services, Reporting Services, PowerPivot, and Excel. This enables you to perform a straightforward BI on data stored and managed by HDInsight in Blob storage.

Other Apache-compatible technologies and sister technologies that are part of the Hadoop ecosystem and have been built to run on top of Hadoop clusters can also be downloaded are used with HDInsight. These include open source technologies such as Sqoop which integrate HDFS with relational data stores.


Pig is a high-level platform for processing big data on Hadoop clusters. Pig consists of a data flow language, called Pig Latin, supporting writing queries on large datasets and an execution environment running programs from a console. The Pig Latin programs consist of dataset transformation series converted under the covers, to a MapReduce program series. Pig Latin abstractions provide richer data structures than MapReduce, and perform for Hadoop what SQL performs for Relational Database Management Systems (RDBMS). Pig Latin is fully extensible. User Defined Functions (UDFs), written in Java, Python, Ruby, C#, or JavaScript, can be called to customize each processing path stage when composing the analysis. For additional information, see Welcome to Apache Pig!


Hive is a distributed data warehouse managing data stored in an HDFS. It is the Hadoop query engine. Hive is for analysts with strong SQL skills providing an SQL-like interface and a relational data model. Hive uses a language called HiveQL; a dialect of SQL. Hive, like Pig, is an abstraction on top of MapReduce and when run, Hive translates queries into a series of MapReduce jobs. Scenarios for Hive are closer in concept to those for RDBMS, and so are appropriate for use with more structured data. For unstructured data, Pig is better choice. For additional information, see Welcome to Apache Hive!


Sqoop is tool that transfers bulk data between Hadoop and relational databases such a SQL, or other structured data stores, as efficiently as possible. Use Sqoop to import data from external structured data stores into the HDFS or related systems like Hive. Sqoop can also extract data from Hadoop and export the extracted data to external relational databases, enterprise data warehouses, or any other structured data store type. For additional information, see the Apache Sqoop Web site.

By Sriramjithendra Posted in Big Data

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