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Your 5-day accelerated MCSA: Data Engineering with Azure course will develop the skills to design and implement big data engineering workflows with the Microsoft cloud ecosystem and Microsoft HD Insight to extract strategic value from your data.
Your expert Microsoft Certified Trainer (MCT) will immerse you in Microsoft Official Curriculum (MOC) at our distraction-free training centre, allowing you to focus 100% on learning. You'll experience Firebrand's Lecture | Lab | Review technique, combining hands-on practical labs, theory and review sessions to reinforce learning and develop skills and knowledge faster.
You'll cover a range of exciting topics including:
You'll be prepared for exams 70-775: Perform Data Engineering on Microsoft HD Insight and 70-776: Engineering Data with Microsoft Cloud Services. You'll sit these on-site during the course, covered by your Certification Guarantee.
Your MCSA: Data Engineering with Azure certification will validate skills in implementing big data engineering workflows with Microsoft Cloud Services and Microsoft HDInsight. Ideal if you're a data engineer, data architect, data scientist or data developer. Earning this certifcation acts as your first step towards achieving the MCSE: Data Management and Analytics credential.
Module 1: Getting Started with HDInsight
This module introduces Hadoop, the MapReduce paradigm, and HDInsight.
Lessons
Lab : Working with HDInsight
After completing this module, students will be able to:
Module 2: Deploying HDInsight Clusters
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 customise 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.
Lessons
Lab : Managing HDInsight clusters with the Azure Portal
After completing this module, students will be able to:
Module 3: Authorising Users to Access Resources
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.
Lessons
Lab : Authorising Users to Access Resources
After completing this module, students will be able to:
Module 4: Loading data into HDInsight
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.
Lessons
Lab : Loading Data into your Azure account
After completing this module, students will be able to:
Module 5: Troubleshooting HDInsight
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.
Lessons
Lab : Troubleshooting HDInsight
After completing this module, students will be able to:
Module 6: Implementing Batch Solutions
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 operationalisation that are available for big data workloads on an HDInsight stack.
Lessons
Lab : Implement Batch Solutions
After completing this module, students will be able to:
Module 7: Design Batch ETL solutions for big data with Spark
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.
Lessons
Lab : Design Batch ETL solutions for big data with Spark.
After completing this module, students will be able to:
Module 8: Analyse Data with Spark SQL
This module describes how to analyse 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.
Lessons
Lab : Performing exploratory data analysis by using iterative and interactive queries
After completing this module, students will be able to:
Module 9: Analyse Data with Hive and Phoenix
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.
Lessons
Lab : Analyse data with Hive and Phoenix
After completing this module, students will be able to:
Module 10: Stream Analytics
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.
Lessons
Lab : Implement Stream Analytics
After completing this module, students will be able to:
Module 11: Implementing Streaming Solutions with Kafka and HBase
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.
Lessons
Lab : Implementing Streaming Solutions with Kafka and HBase
After completing this module, students will be able to:
Module 12: Develop big data real-time processing solutions with Apache Storm
This module explains how to develop big data real-time processing solutions with Apache Storm.
Lessons
Lab : Developing big data real-time processing solutions with Apache Storm
After completing this module, students will be able to:
Module 13: Create Spark Streaming Applications
This module describes Spark Streaming; explains how to use discretised streams (DStreams); and explains how to apply the concepts to develop Spark Streaming applications.
Lessons
Lab : Building a Spark Streaming Application
After completing this module, students will be able to:
Module 1: Architectures for Big Data Engineering with Azure
This module describes common architectures for processing big data using Azure tools and services.
Lessons
Lab : Designing a Big Data Architecture
After completing this module, students will be able to:
Module 2: Processing Event Streams using Azure Stream Analytics
This module describes how to use Azure Stream Analytics to design and implement stream processing over large-scale data.
Lessons
Lab : Processing Event Streams with Azure Stream Analytics
After completing this module, students will be able to:
Module 3: Performing custom processing in Azure Stream Analytics
This module describes how to include custom functions and incorporate machine learning activities into an Azure Stream Analytics job.
Lessons
Lab : Performing Custom Processing with Azure Stream Analytics
After completing this module, students will be able to:
Module 4: Managing Big Data in Azure Data Lake Store
This module describes how to use Azure Data Lake Store as a large-scale repository of data files.
Lessons
Lab : Managing Big Data in Azure Data Lake Store
After completing this module, students will be able to:
Module 5: Processing Big Data using Azure Data Lake Analytics
This module describes how to use Azure Data Lake Analytics to examine and process data held in Azure Data Lake Store.
Lessons
Lab : Processing Big Data using Azure Data Lake Analytics
After completing this module, students will be able to:
Module 6: Implementing custom operations and monitoring performance in Azure Data Lake Analytics
This module describes how to create and deploy custom functions and operations, integrate with Python and R, and protect and optimise jobs.
Lessons
Lab : Implementing custom operations and monitoring performance in Azure Data Lake Analytics
After completing this module, students will be able to:
Module 7: Implementing Azure SQL Data Warehouse
This module describes how to use Azure SQL Data Warehouse to create a repository that can support large-scale analytical processing over data at rest.
Lessons
Lab : Implementing Azure SQL Data Warehouse
After completing this module, students will be able to:
Module 8: Performing Analytics with Azure SQL Data Warehouse
This module describes how to import data in Azure SQL Data Warehouse, and how to protect this data.
Lessons
Lab : Performing Analytics with Azure SQL Data Warehouse
After completing this module, students will be able to:
Module 9: Automating the Data Flow with Azure Data Factory
This module describes how to use Azure Data Factory to import, transform, and transfer data between repositories and services.
Lessons
Lab : Automating the Data Flow with Azure Data Factory
After completing this module, students will be able to:
You'll sit the following exams at the Firebrand training centre during the course, covered by your Certification Guarantee:
70-775: Perform Data Engineering on Microsoft HD Insight
Skills measured:
70-776: Engineering Data with Microsoft Cloud Services - currently in beta
Skills measured:
This exam is currently in beta. If you pass it, you will receive full credit toward applicable certifications, but you will not receive a score report or pass/fail notification until 8-12 weeks following the conclusion of the beta period.
Your accelerated course includes:
Before attending this course, you must meet the following prerequisite knowledge and skills of:
Unsure whether you meet the prerequisites? Don’t worry. Your training consultant will discuss your background with you to understand if this course is right for you.
Here's the Firebrand Training review section. Since 2001 we've trained exactly 134561 students and asked them all to review our Accelerated Learning. Currently, 95.05% have said Firebrand exceeded their expectations.
Read reviews from recent accelerated courses below or visit Firebrand Stories for written and video interviews from our alumni.
"I think Firebrand offers a very good and efficient concept to prepare for an exam in a very short period of time."
Anonymous (3.9.2018 (Monday) to 5.9.2018 (Wednesday))
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"Firebrand is a great bootcamp, where you can learn a lot and get very useful certifications. "
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"Perfect training!"
P. E.. (4.12.2017 (Monday) to 6.12.2017 (Wednesday))
"A high tempo and lots to learn from a great instructor. Thanks for helping me pass the exam in a quick tempo."
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