Azure HDinsight Course Overview

Azure HDinsight Course Overview

The Azure HDInsight course is an in-depth educational program designed to equip learners with the skills to administer, provision, and manage HDInsight clusters on Azure. It provides knowledge on how to deploy secure multi-user environments, ingest data for various types of processing, and configure clusters for optimal performance. Learners will also gain expertise in implementing big data solutions, including batch processing with Hive and Pig, and designing ETL solutions with Spark. The course covers interactive processing with Spark SQL, Hive, and Apache Phoenix, along with real-time processing techniques using Spark Streaming, Structured Streaming, Apache Storm, Kafka, and HBase. By the end of the course, participants will be proficient in building and managing robust big data applications on Azure HDInsight, gaining valuable skills for a career in big data analytics.

This is a Rare Course and it can be take up to 3 weeks to arrange the training.

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Course Prerequisites

Certainly! In order to successfully undertake training in the Azure HDInsight course, the minimum required prerequisites are:

  • Basic understanding of cloud computing concepts, particularly within the Microsoft Azure ecosystem.
  • Knowledge of big data concepts, including the types of data and basic data processing frameworks.
  • Familiarity with data processing languages such as SQL for querying data sets.
  • Fundamental knowledge of programming principles and experience with a programming language, preferably Python or Scala, as they are commonly used with Spark.
  • Basic command-line interface (CLI) skills for interacting with the Azure portal and HDInsight clusters.
  • An introductory level of understanding of distributed systems and their challenges.
  • Enthusiasm to learn about big data processing and a commitment to following through the course material and hands-on labs.

These prerequisites are designed to ensure that learners have a solid foundation upon which to build their Azure HDInsight skills without being overly daunting. The course is structured to guide learners through more advanced topics as they progress.

Target Audience for Azure HDinsight

Azure HDInsight course provides in-depth knowledge on managing big data workloads on HDInsight and implementing real-time processing solutions.

Target audience for the Azure HDInsight course includes:

  • Data Engineers
  • Big Data Analysts
  • Data Scientists
  • Hadoop Developers
  • Data Architects
  • IT Professionals with a focus on data and analytics
  • Software Engineers interested in big data technologies
  • System Administrators managing Hadoop and Spark clusters
  • Technical Team Leads overseeing big data projects
  • Cloud Solutions Architects working with Azure services
  • Business Intelligence Professionals seeking to leverage big data
  • Database Professionals transitioning to big data roles

Learning Objectives - What you will Learn in this Azure HDinsight?

Introduction to Learning Outcomes:

Gain expertise in managing and processing big data with Azure HDInsight, mastering cluster deployment, data ingestion, big data batch and interactive processing, and real-time analytics.

Learning Objectives and Outcomes:

  • Deploy and configure Azure HDInsight clusters tailored for different workloads and security requirements.
  • Ingest and process data using batch and interactive methods for comprehensive data analysis.
  • Manage HDInsight clusters and debug jobs to maintain high performance and reliability.
  • Implement batch processing solutions using Hive and Apache Pig to analyze large datasets efficiently.
  • Design and operationalize scalable batch ETL solutions leveraging Spark for big data transformation.
  • Execute interactive queries and perform data exploration with Spark SQL and Interactive Hive to derive insights from big data.
  • Utilize Apache Phoenix for efficient interactive processing on top of HBase.
  • Create and manage Spark streaming applications to process data in real-time using the DStream and structured streaming APIs.
  • Develop real-time processing solutions with Apache Storm for complex event processing.
  • Integrate Kafka for building robust big data solutions that require messaging and HBase for NoSQL data storage and real-time querying.