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In this week’s Azure Every Day posts, I’ll begin a series focusing on big data and the HDInsight offerings. If you don’t know, HDInsight is a fully managed, full spectrum open source analytics service for enterprises that allows you to use open source frameworks such as Hadoop, Spark, Hive, among others. It was introduced to Azure in 2013 and they’ve added more recent options, such as domain join clusters capabilities.
Today’s focus is on HDInsight Hadoop. What we’re talking about here is being able to work with big data workloads. These large amounts of data can be structured, unstructured or semi-structured data, like table structures, documents or photos.
It can be historical data that you’re looking to analyze or stream data that’s coming in real time. The goal of this is for you to process the data and generate information from it. Some advantages are:
Some of the uses for Hadoop HDInsight are:
Another benefit is HDInsight clusters support multiple programming languages, like Java, Python, Scala, Pig Latin, Hive QL and Spark SQL. Basically, all common programming languages in the open source community that allow you to take advantage of the great, high performing technology for these big data workloads.
Coming up, I’ll discuss some of the cluster types available, such as HDInsight Spark, HBase, Storm, Kafka, Interactive Query and R-Server. At Pragmatic Works, we love working on data projects or anything on Azure. If you have questions about Hadoop or HDInsight or any Azure topic, we are your best resource. Click the link below or contact us—we’re here to help.
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