MCSA: Data Engineering with Azure

Description

During this five-day course, the student will develop the skills to design and implement big data engineering workflows with the Microsoft Cloud Ecosystem and Microsoft HD Insight to extract the greatest amount of value from Data.

The MCSA: Data Engineering with Azure Certification will give validation to the skills learned in implementing big data engineering workflows with Microsoft Cloud services and Microsoft HD Insight.

This course is ideal for:

Data Engineer
Data Architect
Data Scientist
Data Developer
After completing this course, students will be able to:

To describe the purpose of Azure Data Factory, and explain how it works
To describe how to create Azure Data Factory pipelines that can transfer data efficiently
To describe how to perform transformations using an Azure Data Factory pipeline
To describe how to monitor Azure Data Factory pipeline, and how to protect the data flowing through these pipelines
20755: Perform data engineering on Microsoft HD Insight

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 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

Perform Big Data Engineering on Microsoft Cloud Services (beta)

Describe common architectures for processing Big Data using Azure tools and services
Use Azure Stream Analytics to design and implement stream processing over large-scale data
How to include custom functions and incorporate machine learning activities into an Azure Stream Analytics job
How to use Azure Data Lake Store as a large-scale repository of data files
How to use Azure Data Lake Analytics to examine and process data held in Azure Data Lake Store
How to create and deploy custom functions and operations, integrate with Python and R, and protect and optimize jobs
How to use Azure SQL Data Warehouse to create a repository that can support large-scale analytical processing over data at rest
How to use Azure SQL Data Warehouse to perform analytical processing, how to maintain performance, and how to protect the data
How to use Azure Data Factory to import, transform, and transfer data between repositories and services
The purpose of Azure Data factory, and explain how it works
How to create Azure Data Factory pipelines that can transfer data efficiently
How to perform transformations using an Azure Data Factory pipeline
How to monitor Azure Data Factory pipelines and how to protect the data flowing through these pipelines

Prerequisites

It is recommended that students interested in this course have previous knowledge or experience with:

Azure Data Services
Microsoft Windows Operating system and its core functionality
Relational databases
Programming using R, and familiarity with common R packages
Common statistical methods and data analysis best practices

What’s included?

  • Authorized Courseware
  • Intensive Hands on Skills Development with an Experienced Subject Matter Expert
  • Hands on practice on real Servers and extended lab support 1.800.482.3172
  • Examination Vouchers & Onsite Certification Testing- (excluding Adobe and PMP Boot Camps)
  • Academy Code of Honor: Test Pass Guarantee
  • Optional: Package for Hotel Accommodations, Lunch and Transportation

With several convenient training delivery methods offered, The Academy makes getting the training you need easy. Whether you prefer to learn in a classroom or an online live learning virtual environment, training videos hosted online, and private group classes hosted at your site. We offer expert instruction to individuals, government agencies, non-profits, and corporations. Our live classes, on-sites, and online training videos all feature certified instructors who teach a detailed curriculum and share their expertise and insights with trainees. No matter how you prefer to receive the training, you can count on The Academy for an engaging and effective learning experience.

Methods

  • Instructor Led (the best training format we offer)
  • Live Online Classroom – Online Instructor Led
  • Self-Paced Video

Speak to an Admissions Representative for complete details

Curriculum

20775: Perform Data Engineering on Microsoft HDInsight

Module 1: Getting Started with HDInsight
Lessons

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
Lessons

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
Lessons

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
Lessons

Storing data for HDInsight processing
Using data loading tools
Maximizing value from stored data
Lab: Loading Data into your Azure account
Load data for use with HDInsight

Module 5: Troubleshooting HDInsight
Lessons

Analyze HDInsight logs
YARN logs
Heap Dumps
Operations management suite
Lab: Troubleshooting HDInsight

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

Module 6: Implementing Batch Solutions
Lessons

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
Lessons

What is Spark?
ETL with Spark
Spark performance
Lab: Design Batch ETL solutions for big data with Spark

Create an 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
Lessons

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
Lessons

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

Module 10: Stream Analytics
Lessons

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
Lessons

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
Lessons

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
Lessons

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

20776A: Performing Big Data Engineering on Microsoft Cloud Services

Module 1: Architectures for Big Data Engineering with Azure

Lessons

Understanding Big Data
Architectures for Processing Big Data
Considerations for designing Big Data solutions
Lab: Designing a Big Data Architecture

Design big data architecture
Module 2: Processing Event Streams using Azure Stream Analytics

Lessons

Introduction to Azure Stream Analytics
Configuring Azure Stream Analytics jobs
Lab: Processing Event Streams with Azure Stream Analytics

Create an Azure Stream Analytics job
Create another Azure Stream job
Add an Input
Edit the ASA job
Determine the nearest Patrol Car
Module 3: Performing custom processing in Azure Stream Analytics

Lessons

Implementing Custom Functions
Incorporating Machine Learning into an Azure Stream Analytics Job
Lab: Performing Custom Processing with Azure Stream Analytics

Add logic to the analytics
Detect consistent anomalies
Determine consistencies using machine learning and ASA
Module 4: Managing Big Data in Azure Data Lake Store

Lessons

Using Azure Data Lake Store
Monitoring and protecting data in Azure Data Lake Store
Lab: Managing Big Data in Azure Data Lake Store

Update the ASA Job
Upload details to ADLS
Module 5: Processing Big Data using Azure Data Lake Analytics

Lessons

Introduction to Azure Data Lake Analytics
Analyzing Data with U-SQL
Sorting, grouping, and joining data
Lab: Processing Big Data using Azure Data Lake Analytics

Add functionality
Query against Database
Calculate average speed
Module 6: Implementing custom operations and monitoring performance in Azure Data Lake Analytics

Lessons

Incorporating custom functionality into Analytics jobs
Managing and Optimizing jobs
Lab: Implementing custom operations and monitoring performance in Azure Data Lake Analytics

Custom extractor
Custom processor
Integration with R/Python
Monitor and optimize a job
Module 7: Implementing Azure SQL Data Warehouse

Lessons

Introduction to Azure SQL Data Warehouse
Designing tables for efficient queries
Importing Data into Azure SQL Data Warehouse
Lab: Implementing Azure SQL Data Warehouse

Create a new data warehouse
Design and create tables and indexes
Import data into the warehouse.
Module 8: Performing Analytics with Azure SQL Data Warehouse

Lessons

Querying Data in Azure SQL Data Warehouse
Maintaining Performance
Protecting Data in Azure SQL Data Warehouse
Lab: Performing Analytics with Azure SQL Data Warehouse

Performing queries and tuning performance
Integrating with Power BI and Azure Machine Learning
Configuring security and analyzing threats
Lessons

Introduction to Azure Data Factory
Transferring Data
Transforming Data
Monitoring Performance and Protecting Data
Lab: Automating the Data Flow with Azure Data Factory

Automate the Data Flow with Azure Data Factory

Enrolled