Azure HDInsight Data Engineering

This module introduces Hadoop, the MapReduce paradigm, and HDInsight.

• What is Big Data?
• Introduction to Hadoop
• Working with MapReduce Function
• Introducing HDInsight

• Provision an HDInsight cluster and run MapReduce jobs

• Describe Hadoop, MapReduce and HDInsight.
• Use scripts to provision an HDInsight Cluster.
• Run a word-counting MapReduce program using PowerShell.

This module provides an overview of the Microsoft Azure HDInsight cluster types, in addition to the creation and maintenance of the HDInsight clusters. The module also demonstrates how to customize clusters by using script actions through the Azure Portal, Azure PowerShell, and the Azure command-line interface (CLI). This module includes labs that provide the steps to deploy and manage the clusters.

• Identifying HDInsight cluster types
• Managing HDInsight clusters by using the Azure portal
• Managing HDInsight Clusters by using Azure PowerShell

• Create an HDInsight cluster that uses Data Lake Store storage
• Customize HDInsight by using script actions
• Delete an HDInsight cluster

• Identify HDInsight cluster types
• Manage HDInsight clusters by using the Azure Portal.
• Manage HDInsight clusters by using Azure PowerShell.

This module provides an overview of non-domain and domain-joined Microsoft HDInsight clusters, in addition to the creation and configuration of domain-joined HDInsight clusters. The module also demonstrates how to manage domain-joined clusters using the Ambari management UI and the Ranger Admin UI. This module includes the labs that will provide the steps to create and manage domain-joined clusters.

• Non-domain Joined clusters
• Configuring domain-joined HDInsight clusters
• Manage domain-joined HDInsight clusters

• Prepare the Lab Environment
• Manage a non-domain joined cluster

• Identify the characteristics of non-domain and domain-joined HDInsight clusters.
• Create and configure domain-joined HDInsight clusters through the Azure PowerShell.
• Manage the domain-joined cluster using the Ambari management UI and the Ranger Admin UI.
• Create Hive policies and manage user permissions.

This module provides an introduction to loading data into Microsoft Azure Blob storage and Microsoft Azure Data Lake storage. At the end of this lesson, you will know how to use multiple tools to transfer data to an HDInsight cluster. You will also learn how to load and transform data to decrease your query run time.

• Storing data for HDInsight processing
• Using data loading tools
• Maximising value from stored data

• Load data for use with HDInsight

• Discuss the architecture of key HDInsight storage solutions.
• Use tools to upload data to HDInsight clusters.
• Compress and serialize uploaded data for decreased processing time.

In this module, you will learn how to interpret logs associated with the various services of Microsoft Azure HDInsight cluster to troubleshoot any issues you might have with these services. You will also learn about Operations Management Suite (OMS) and its capabilities.

• Analyze HDInsight logs
• YARN logs
• Heap dumps
• Operations management suite

• Analyze HDInsight logs
• Analyze YARN logs
• Monitor resources with Operations Management Suite

• Locate and analyze HDInsight logs.
• Use YARN logs for application troubleshooting.
• Understand and enable heap dumps.
• Describe how the OMS can be used with Azure resources.

In this module, you will look at implementing batch solutions in Microsoft Azure HDInsight by using Hive and Pig. You will also discuss the approaches for data pipeline operationalization that are available for big data workloads on an HDInsight stack.

• Apache Hive storage
• HDInsight data queries using Hive and Pig
• Operationalize HDInsight

• Deploy HDInsight cluster and data storage
• Use data transfers with HDInsight clusters
• Query HDInsight cluster data

• Understand Apache Hive and the scenarios where it can be used.
• Run batch jobs using Apache Hive and Apache Pig.
• Explain the capabilities of the Microsoft Azure Data Factory and Apache Oozie—and how they can orchestrate and automate big data workflows.

This module provides an overview of Apache Spark, describing its main characteristics and key features. Before you start, it’s helpful to understand the basic architecture of Apache Spark and the different components that are available. The module also explains how to design batch Extract, Transform, Load (ETL) solutions for big data with Spark on HDInsight. The final lesson includes some guidelines to improve Spark performance.

• What is Spark?
• ETL with Spark
• Spark performance

• 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

• Describe the architecture of Spark on HDInsight.
• Describe the different components required for a Spark application on HDInsight.
• Identify the benefits of using Spark for ETL processes.
• Create Python and Scala code in a Spark program to ingest or process data.
• Identify cluster settings for optimal performance.
• Track and debug jobs running on an Apache Spark cluster in HDInsight.

This module describes how to analyze data by using Spark SQL. In it, you will be able to explain the differences between RDD, Datasets and Dataframes, identify the uses cases between Iterative and Interactive queries, and describe best practices for Caching, Partitioning and Persistence. You will also look at how to use Apache Zeppelin and Jupyter notebooks, carry out exploratory data analysis, then submit Spark jobs remotely to a Spark cluster.

• Implementing iterative and interactive queries
• Perform exploratory data analysis

• Build a machine learning application
• Use zeppelin for interactive data analysis
• View and manage Spark sessions by using Livy

• Implement interactive queries.
• Perform exploratory data analysis.

In this module, you will learn about running interactive queries using Interactive Hive (also known as Hive LLAP or Live Long and Process) and Apache Phoenix. You will also learn about the various aspects of running interactive queries using Apache Phoenix with HBase as the underlying query engine.

• Implement interactive queries for big data with interactive hive.
• Perform exploratory data analysis by using Hive
• Perform interactive processing by using Apache Phoenix

• Implement interactive queries for big data with interactive Hive
• Perform exploratory data analysis by using Hive
• Perform interactive processing by using Apache Phoenix

• Implement interactive queries with interactive Hive.
• Perform exploratory data analysis using Hive.
• Perform interactive processing by using Apache Phoenix.

The Microsoft Azure Stream Analytics service has some built-in features and capabilities that make it as easy to use as a flexible stream processing service in the cloud. You will see that there are a number of advantages to using Stream Analytics for your streaming solutions, which you will discuss in more detail. You will also compare features of Stream Analytics to other services available within the Microsoft Azure HDInsight stack, such as Apache Storm. You will learn how to deploy a Stream Analytics job, connect it to the Microsoft Azure Event Hub to ingest real-time data, and execute a Stream Analytics query to gain low-latency insights. After that, you will learn how Stream Analytics jobs can be monitored when deployed and used in production settings.

• Stream analytics
• Process streaming data from stream analytics
• Managing stream analytics jobs

• Process streaming data with stream analytics
• Managing stream analytics jobs

• Describe stream analytics and its capabilities.
• Process streaming data with stream analytics.
• Manage stream analytics jobs.

In this module, you will learn how to use Kafka to build streaming solutions. You will also see how to use Kafka to persist data to HDFS by using Apache HBase, and then query this data.

• Building and Deploying a Kafka Cluster
• Publishing, Consuming, and Processing data using the Kafka Cluster
• Using HBase to store and Query Data

• 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

• Build and deploy a Kafka Cluster.
• Publish data to a Kafka Cluster, consume data from a Kafka Cluster, and perform stream processing using the Kafka Cluster.
• Save streamed data to HBase, and perform queries using the HBase API.

This module explains how to develop big data real-time processing solutions with Apache Storm.

• Persist long term data
• Stream data with Storm
• Create Storm topologies
• Configure Apache Storm

• Stream data with Storm
• Create Storm Topologies

• Persist long term data.
• Stream data with Storm.
• Create Storm topologies.
• Configure Apache Storm.

This module describes Spark Streaming; explains how to use discretized streams (DStreams); and explains how to apply the concepts to develop Spark Streaming applications.

• Working with Spark Streaming
• Creating Spark Structured Streaming Applications
• Persistence and Visualization

• Installing Required Software
• Building the Azure Infrastructure
• Building a Spark Streaming Pipeline

• Describe Spark Streaming and how it works.
• Use discretized streams (DStreams).
• Work with sliding window operations.
• Apply the concepts to develop Spark Streaming applications.
• Describe Structured Streaming.