Course Code: 5625

20776 Engineering Data with Microsoft Cloud Services

Class Dates:
1/21/2019
3/18/2019
Length:
5 Days
Cost:
$2495.00
Class Time:
Technology:
microsoft
Delivery:

Overview

  • Course Overview
  • The main purpose of the course is to give students the ability plan and implement big data workflows on HDInsight.
  • Audience
  • The primary audience for this course is data engineers, data architects, data scientists, and data developers who plan to implement big data engineering workflows on HDInsight.

Course Disclaimer

Course Cost listed does not include the cost of courseware. Course to run is subject to minimum enrollment. Course may run as a virtual instructor led if minimum is not met. If you have any questions, plese contact: 207.775.0244 or email: learn@vtec.org

Follow Up

  • Recommended Training:

Prerequisites

  • In addition to their professional experience, students who attend this course should have:
    Programming experience using R, and familiarity with common R packages
    Knowledge of common statistical methods and data analysis best practices.
    Basic knowledge of the Microsoft Windows operating system and its core functionality.
    Working knowledge of relational databases.

  • Recommended Courses:

Course Details

  • After completing this course, students will be able to:
  • Deploy HDInsight Clusters.
  • Authorizing Users to Access Resources.
  • Loading Data into HDInsight.
  • Troubleshooting HDInsight
  • Implement Batch Solutions.
  • Design Batch ETL Solutions for Big Data with Spark
  • Analyze Data with Spark SQL
  • Analyze Data with Hive and Phoenix.
  • Describe Stream Analytics.
  • Implement Spark Streaming Using the DStream API.
  • Develop Big Data Real-Time Processing Solutions with Apache Storm.
  • Build Solutions that use Kafka and HBase.
  • Module 1: Getting Started with HDInsight
  • This module introduces Hadoop, the MapReduce paradigm, and HDInsight.
  • What is Big Data?
  • Introduction to Hadoop
  • Working with MapReduce Function
  • Introducing HDInsight
  • Lab : Working with HDInsight
  • Provision an HDInsight cluster and run MapReduce jobs
  • Module 2: Deploying HDInsight Clusters
  • Identifying HDInsight cluster types
  • Managing HDInsight clusters by using the Azure portal
  • Managing HDInsight Clusters by using Azure PowerShell
  • Lab : Managing HDInsight clusters with the Azure Portal
  • Create an HDInsight cluster that uses Data Lake Store storage
  • Customize HDInsight by using script actions
  • Delete an HDInsight cluster
  • Module 3: Authorizing Users to Access Resources
  • Non-domain Joined clusters
  • Configuring domain-joined HDInsight clusters
  • Manage domain-joined HDInsight clusters
  • Lab : Authorizing Users to Access Resources
  • Prepare the Lab Environment
  • Manage a non-domain joined cluster
  • Module 4: Loading data into HDInsight
  • Storing data for HDInsight processing
  • Using data loading tools
  • Maximising value from stored data
  • Lab : Loading Data into your Azure account
  • Load data for use with HDInsight
  • Module 5: Troubleshooting HDInsight
  • Analyze HDInsight logs
  • YARN logs
  • Heap dumps
  • Operations management suite
  • Lab : Troubleshooting HDInsight
  • Module 6: Implementing Batch Solutions
  • Apache Hive storage
  • HDInsight data queries using Hive and Pig
  • Operationalize HDInsight
  • Lab : Implement Batch Solutions
  • Deploy HDInsight cluster and data storage
  • Use data transfers with HDInsight clusters
  • Query HDInsight cluster data
  • Module 7: Design Batch ETL solutions for big data with Spark
  • What is Spark?
  • ETL with Spark
  • Spark performance
  • Lab : Design Batch ETL solutions for big data with Spark.
  • Create a HDInsight Cluster with access to Data Lake Store
  • Use HDInsight Spark cluster to analyze data in Data Lake Store
  • Analyzing website logs using a custom library with Apache Spark cluster on HDInsight
  • Managing resources for Apache Spark cluster on Azure HDInsight
  • Module 8: Analyze Data with Spark SQL
  • Implementing iterative and interactive queries
  • Perform exploratory data analysis
  • Lab : Performing exploratory data analysis by using iterative and interactive queries
  • Build a machine learning application
  • Use zeppelin for interactive data analysis
  • View and manage Spark sessions by using Livy
  • Module 9: Analyze Data with Hive and Phoenix
  • Implement interactive queries for big data with interactive hive.
  • Perform exploratory data analysis by using Hive
  • Perform interactive processing by using Apache Phoenix
  • Lab : Analyze data with Hive and Phoenix
  • Implement interactive queries for big data with interactive Hive
  • Perform exploratory data analysis by using Hive
  • Perform interactive processing by using Apache Phoenix
  • Module 10: Stream Analytics
  • Stream analytics
  • Process streaming data from stream analytics
  • Managing stream analytics jobs
  • Lab : Implement Stream Analytics
  • Process streaming data with stream analytics
  • Managing stream analytics jobs
  • Module 11: Implementing Streaming Solutions with Kafka and HBase
  • Building and Deploying a Kafka Cluster
  • Publishing, Consuming, and Processing data using the Kafka Cluster
  • Using HBase to store and Query Data
  • Lab : Implementing Streaming Solutions with Kafka and HBase
  • Create a virtual network and gateway
  • Create a storm cluster for Kafka
  • Create a Kafka producer
  • Create a streaming processor client topology
  • Create a Power BI dashboard and streaming dataset
  • Create an HBase cluster
  • Create a streaming processor to write to HBase
  • Module 12: Develop big data real-time processing solutions with Apache Storm
  • Persist long term data
  • Stream data with Storm
  • Create Storm topologies
  • Configure Apache Storm
  • Lab : Developing big data real-time processing solutions with Apache Storm
  • Stream data with Storm
  • Create Storm Topologies
  • Module 13: Create Spark Streaming Applications
  • Working with Spark Streaming
  • Creating Spark Structured Streaming Applications
  • Persistence and Visualization
  • Lab : Building a Spark Streaming Application
  • Installing Required Software
  • Building the Azure Infrastructure
  • Building a Spark Streaming Pipeline