Microsoft MCSA: Data Engineering with Azure - Kun 5 dage

Hvorfor

Hvad siger vores kunder?

Effektiv og fokuseret undervisning i fine lokaler og gode omgivelser.
John Wonge, Adacta A/S - Microsoft Dynamics Sure Step 2012 (2 dage), 17/12/2012 til 18/12/2012

Her er 4 gode grunde til at du skal tage MCSA Data Engineering with Azure hos Firebrand:

Se Hvordan Vi Hjælper Dig Med at Få Nye Færdigheder Hurtigt

  1. Du bliver MCSA Data Engineering with Azure uddannet og certificeret hurtigere. Du lærer mere på vores 5 dages accelererede kursus og får mindst 12 timers daglig uddannelse i et distraktionsfrit miljø.
  2. Dit MCSA Data Engineering with Azure kursus er alt-inklusiv. Du får en gennemskuelig pris, der dækker alle kursusmaterialer, eksamen, overnatning og forplejning. Du skal ikke tænke på andet end at lære.
  3. Bestå MCSA Data Engineering with Azure den første gang eller kom tilbage, gratis. Din instruktør er ekspert, og træner dig ud fra accelererede metoder, så du lærer hurtigere og har de bedste muligheder for at bestå eksamen første gang. Men hvis du mod forventning ikke består, er du dækket af vores Certificeringsgaranti.
  4. Tag MCSA Data Engineering with Azure hos en prisvindende uddannelsesleverandør. Vi har vundet adskillige udmærkelser heriblandt Microsofts "Årets Learning Partner" hele fem gange og to Børsen Gazelle priser. Firebrand er din hurtigste vej til uddannelse, og vi har sparet 72.589 deltagere for mere end en million spildte timer siden 2001.

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Dit kursus kan være langt billigere hvis din organisation har SA Vouchers. Uddannelseskuponerne kan anvendes efter eget valg til et stort udvalg af officielle Microsoft kurser hos Firebrand. Tal med en Firebrand uddannelsesrådgiver, hvis du vil vide mere Kontakt os.

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:

  • Deploying and securing multi-user HDInsight clusters
  • Implementing batch solutions with Hive and Apache Pig
  • Creating Spark streaming applications using DStream API
  • Developing big data real-time processing solutions with Apache Storm
  • Performing exploratory data analysis by using Spark SQL

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.

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20775A: Perform Data Engineering on Microsoft Azure HDInsight

Module 1: Getting Started with HDInsight

This module introduces Hadoop, the MapReduce paradigm, and 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

After completing this module, students will be able to:

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

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

  • 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
  • Customise HDInsight by using script actions
  • Delete an HDInsight cluster

After completing this module, students will be able to:

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

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

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

Lab : Authorising Users to Access Resources

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

After completing this module, students will be able to:

  • 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.

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

  • 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

After completing this module, students will be able to:

  • 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.

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

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

Lab : Troubleshooting HDInsight

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

After completing this module, students will be able to:

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

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

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

Lab : Implement Batch Solutions

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

After completing this module, students will be able to:

  • 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.

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

  • 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 analyse data in Data Lake Store
  • Analysing website logs using a custom library with Apache Spark cluster on HDInsight
  • Managing resources for Apache Spark cluster on Azure HDInsight

After completing this module, students will be able to:

  • 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.

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

  • 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

After completing this module, students will be able to:

  • Implement interactive queries.
  • Perform exploratory data analysis.

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

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

Lab : Analyse 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

After completing this module, students will be able to:

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

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

  • 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

After completing this module, students will be able to:

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

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

  • 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

After completing this module, students will be able to:

  • 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.

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

  • 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

After completing this module, students will be able to:

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

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

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

Lab : Building a Spark Streaming Application

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

After completing this module, students will be able to:

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

20776A: Performing Big Data Engineering on Microsoft Cloud Services

Module 1: Architectures for Big Data Engineering with Azure

This module describes common architectures for processing big data using Azure tools and services.

Lessons

  • Understanding Big Data
  • Architectures for Processing Big Data
  • Considerations for designing Big Data solutions

Lab : Designing a Big Data Architecture

  • Design a big data architecture

After completing this module, students will be able to:

  • Explain the concept of Big Data.
  • Describe the Lambda and Kappa architectures.
  • Describe design considerations for building Big Data Solutions with Azure.

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

  • 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

After completing this module, students will be able to:

  • Describe the purpose and structure of Azure Stream Analytics.
  • Configure Azure Stream Analytics jobs for scalability, reliability and security.

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

  • 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

After completing this module, students will be able to:

  • Describe how to create and use custom functions in Azure Stream Analytics.
  • Describe how to use Azure Machine Learning models in an Azure Stream Analytics job.

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

  • 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

After completing this module, students will be able to:

  • Describe how to create an Azure Data Lake Store, create folders, and upload data.
  • Explain how to monitor an Azure Data Lake account, and protect the data that it contains.

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

  • Introduction to Azure Data Lake Analytics
  • Analysing 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

After completing this module, students will be able to:

  • Describe the purpose of Azure Data Lake Analytics, and how to create and run jobs.
  • Describe how to use USQL to process and analyse data.
  • Describe how to use windowing to sort data and perform aggregated operations, and how to join data from multiple sources.

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

  • Incorporating custom functionality into Analytics jobs
  • Managing and Optimising jobs

Lab : Implementing custom operations and monitoring performance in Azure Data Lake Analytics

  • Custom extractor
  • Custom processor
  • Integration with R/Python
  • Monitor and optimise a job

After completing this module, students will be able to:

  • Describe how to incorporate custom features and assemblies into USQL.
  • Describe how to implement security to protect jobs, and how to monitor and optimise jobs to ensure efficient operations.

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

  • 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.

After completing this module, students will be able to:

  • Describe the purpose and structure of Azure SQL Data Warehouse.
  • Describe how to design table to optimise the processing performed by the data warehouse.
  • Describe tools and techniques for importing data into a warehouse at scale.

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

  • 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 analysing threats

After completing this module, students will be able to:

  • Describe how to perform queries and use the data held in a data warehouse to perform analytics and generate reports.
  • Describe how to configure and monitor a data warehouse to maintain good performance.
  • Describe how to protect data and manage security in a data warehouse.

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

  • 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

After completing this module, students will be able to:

  • Describe the purpose of Azure Data Factory, and explain how it works.
  • Describe how to create Azure Data Factory pipelines that can transfer data efficiently.
  • Describe how to perform transformations using an Azure Data Factory pipeline.
  • Describe how to monitor Azure Data Factory pipelines, and how to protect the data flowing through these pipelines.

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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:

  • Administer and Provision HDInsight Clusters
  • Implement Big Data Batch Processing Solutions
  • Implement Big Data Interactive Processing Solutions
  • Implement Big Data Real-Time Processing Solutions

70-776: Engineering Data with Microsoft Cloud Services - currently in beta

Skills measured:

  • Design and Implement Complex Event Processing By Using Azure Stream Analytics (15-20%)
  • Design and Implement Analytics by Using Azure Data Lake (25-30%)
  • Design and Implement Azure SQL Data Warehouse Solutions (15-20%)
  • Design and Implement Cloud-Based Integration by using Azure Data Factory (15-20%)
  • Manage and Maintain Azure SQL Data Warehouse, Azure Data Lake, Azure Data Factory, and Azure Stream Analytics (20-25%)

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.

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Microsoft Official Curriculum

  • MOC 20775A: Perform Data Engineering on Microsoft HD Insight
  • MOC 20776A: Engineering Data with Microsoft Cloud Services

Det hele er inkluderet! Du får en alt-inklusiv kursuspakke, som er målrettet til dine behov. Vi tager os af enhver detalje, så det eneste du skal fokusere på er dine lærings- og certificeringsmål.

  • Transport til/fra specifikke afhentningssteder
  • Overnatninger, samtlige måltider samt adgang til forfriskninger, snacks, kaffe og the.
  • Intensiv Hands-on uddannelse med vores unikke (Lecture | Lab | Review)TM metode
  • Omfattende kursusmaterialer og labmanualer
  • Et helt igennem instruktørstyret program
  • 24 timers adgang til både undervisningslokale og instruktøren
  • Samtlige måltider samt adgang til forfriskninger, snacks, kaffe og the.
  • On site eksaminering som en del af dit kursus
  • Certificeringsgaranti

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Before attending this course, you must meet the following prerequisite knowledge and skills of:

  • 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.

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Firebrand Training tilbyder et ambitiøst uddannelsesmiljø, som forudsætter at du dedikerer dig til kurset. Ovenstående forkundskaber er vejledende; mange deltagere med mindre erfaring, men med en anden baggrund eller færdigheder, har haft succes med accelereret uddannelse hos Firebrand Training.

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Hvornår

Hvornår vil du tage dit accelererede kursus?

Starter

Slutter

Tilgængelighed

Sted

Tilmelding

27/5/2019 (Mandag)

31/5/2019 (Fredag)

Afsluttet

-

 

30/9/2019 (Mandag)

4/10/2019 (Fredag)

Venteliste

Landsdækkende

 

11/11/2019 (Mandag)

15/11/2019 (Fredag)

Begrænsede pladser

Landsdækkende

 

3/2/2020 (Mandag)

7/2/2020 (Fredag)

Tilgængelige pladser

Landsdækkende

 

16/3/2020 (Mandag)

20/3/2020 (Fredag)

Tilgængelige pladser

Landsdækkende

 

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A.H. - Microsoft Dynamics Sure Step 2012 (2 dage) (29/10/2015 til 30/10/2015)

"Det er intenst og meget godt"
Anonym - Microsoft Dynamics Sure Step 2012 (2 dage) (29/10/2015 til 30/10/2015)

"Excellent !! Super træning, super instruktør der hele vejen igennem holdt et godt tempo med mange informationer"
Anonym, EG A/S - Microsoft Dynamics CRM Developer 2015 (5 dage) (19/10/2015 til 23/10/2015)

"Fine vaerelsesforhold. Middel bespisning. Gode undervisnings forhold. Fantastisk god underviser, let at forstaa og godt inde i stoffet. "
Anonym, Atos - Microsoft MCTS BizTalk Server 2010 (4 dage) (19/10/2015 til 22/10/2015)

"Super kursus, alle kom til Firebrand med samme vilje til at lære tingene! "
Danny Henriksen , Jansson Kommunikation A/S, Odense, Denmark. - Microsoft MCSA: Windows Server 2012 R2 (9 dage) (19/9/2015 til 27/9/2015)

"God og intensiv undervisning med en velforberedt og kompetent instruktør "
Winnie Reinert , INUIT. - Microsoft Dynamics NAV 2015 Financials (6 dage) (7/9/2015 til 12/9/2015)

"Alt gik som smurt selvom jeg var sent ude."
Steen Bjørn , ProActive. - Microsoft Dynamics Sure Step 2012 (2 dage) (16/6/2015 til 17/6/2015)

"Meget professionelt - undervisning og ophold"
Bettina Toft , MW Data A/S. - Microsoft Dynamics AX 2012 R3 Trade & Logistics (5 dage) (20/4/2015 til 24/4/2015)

"Man får ro til at få yderligere indsigt I løsningen. Også selvom man arbejder med det I hverdagen. Det giver en mulighed for at se yderligere løsningsmuligheder, som man ikke havde haft øjnene op for før. "
Anonym - Microsoft Dynamics CRM Online 2015 (7 dage) (13/4/2015 til 19/4/2015)

"Godt kursus, god instruktør. Er klar til at skulle hjem og implementere i virksomheden."
Christina Brandt , KRM AG. - Microsoft Dynamics AX 2012 HRM (3 dage) (23/3/2015 til 25/3/2015)

"Supergod og intensiv træning"
Karin Jepsen, MW Data. - Microsoft Dynamics Sure Step 2012 (2 dage) (16/3/2015 til 17/3/2015)

"Rigtig godt kursus, og forplejningen er helt i orden så man bare kan koncentrere sig om materialet."
J.K.C. - Microsoft Dynamics AX 2012 R3 Financials (5 dage) (16/2/2015 til 20/2/2015)

"Top koncept!!! Men man skal indstille sig på at det er hårdt. Men alt i alt en super lærerig, sjov og fornøjelig oplevelse, som jeg meget gerne gentager - flere gange!!!"
Anonym, Koncern IT - Microsoft MCSA: Windows Server 2012 R2 (9 dage) (7/2/2015 til 15/2/2015)

"Fin gennemgang af materiale"
Michael Nielsen , FSR - danske revisorer. - Microsoft MCSE: Messaging (Exchange 2013) (6 dage) (2/2/2015 til 7/2/2015)

"Firebrand konceptet virkede rigtig godt for mig. Koncentreret undervisning fra morgen til aften - med en god blanding af undervisning, opgave løsning og spørgerunder mv. og efterfølgende selvstudium mellem dagene. Jeg ville ikke på egen hånd have lært så meget på så kort tid og været ligeså klar til at tage certificeringerne. Ser frem til mit næste firebrand kursus."
Jonas Brock , Netcompany. - Microsoft MCSE: Business Intelligence (SQL Server) (6 dage) (15/12/2014 til 20/12/2014)

"Underviseren er rigtig god til at forklare stoffet på flere måder og god til at få alle til at følge med."
Flemming Lauest , EG A/S. - Microsoft MCSA: SQL Server (9 dage) (6/12/2014 til 14/12/2014)

"Super kursus"
Anonym - Microsoft Dynamics AX 2012 R3 Financials (5 dage) (8/12/2014 til 12/12/2014)

"Det er en hård proces, men det man får ud af det, er helt klart det værd. Alle faciliteterne er gode, man har alt det man har brug for, maden er god og der er mange flinke mennesker omkring."
Bilal Arslan , 2ndC. - Microsoft MCSD: SharePoint 2013 Applications – Developing Server Solutions (6 dage) (30/11/2014 til 5/12/2014)

"Jeg kan godt lide kursus formen, væk fra hverdagen uden forstyrrelser. Her er tid til fordybelse og gå ture hvis tiden er til det. Afslappet stemming, god mad og fine værelser."
Robert Tastesen , Inceptio. - Microsoft MCSE: Messaging (Exchange 2013) (6 dage) (17/11/2014 til 22/11/2014)

"Det er hårdt, men det er absolut det værd!"
Julie Andersen, MicroPartner. - Microsoft Dynamics CRM 2013 Online (6 dage) (14/10/2014 til 19/10/2014)

"Fantastisk kursus, faglig meget kompetent instruktør, kurset foregik på en meget behaglig måde, meget rutineret planlagt, alt fungerede optimalt. Tak for det "
Jan Larsen, KMD A/S. - Microsoft Dynamics CRM 2013 Online (6 dage) (14/10/2014 til 19/10/2014)

"Det her var min første gang på kursus hos firebrand og det bliver ikke min sidste gang, fantastisk instruktør og fantastisk personale der alle er søde og venlige. Bedste kursus oplevelse jeg har haft."
Mikkel Schakmann Sørensen, Prophix EU. - Microsoft MCSA: SQL Server (9 dage) (20/9/2014 til 28/9/2014)

"Jeg har fået fuld valuta på mit kursus. Havde ekstremt høje forventningar, som alle blev indfriet. "
Maria Have , Leasy. - Microsoft Dynamics AX 2012 R3 Financials (5 dage) (22/9/2014 til 26/9/2014)

"Kurset var paa ingen made kedeligt. Indholdet var komprimeret med en masse information paa kort tid."
Jens Olesen , Region Sjaelland.dk. - Microsoft MCTS System Center 2012 Configuration Manager (4 dage) (16/6/2014 til 19/6/2014)

"Aldrig har jeg lært så meget på så kort tid, hårdt men udbytterigt"
Niels Kristian Risager , Airgreenland A/S. - Microsoft MCSD: Web Applications (9 dage) (28/4/2014 til 6/5/2014)

"Firebrand er stedet for absolut læring , det er ben hårdt men er helt klart det værd!"
Danny Christiansen , Forenede A/S. - Microsoft MCSE: SharePoint 2013 (6 dage) (22/4/2014 til 27/4/2014)

"Der manglede intet. Det var utroligt dejligt at man slet ikke skulle bekymre sig om andet end sit kursus."
Nicolai Christensen , Fujitsu A/S. - Microsoft MCSE: SharePoint 2013 (6 dage) (22/4/2014 til 27/4/2014)

"Rigtig meget laering paa rigtig kort tid - hvilket lever op til forventningerne. Gode omgivelser og en dygtig underviser"
Martin Guldborg . - Microsoft Dynamics NAV 2013 Developer (6 dage) (31/3/2014 til 5/4/2014)

"Gode kursusforhold. God underviser. Gode faciliteter på kursusstedet. Alt i alt en god oplevelse, der kan anbefales til andre."
Kristian Ellebæk Frandsen , CORNATOR A/S. - Microsoft Dynamics Sure Step 2012 (2 dage) (31/3/2014 til 1/4/2014)

"Kan klart anbefales. "
J.J. - Microsoft MCSE: Communication (Lync Server) (6 dage) (17/3/2014 til 22/3/2014)

"Som sædvanlig er de fysiske rammer super, værelser, forplejning, personale o.s.v. er helt i top og står altid på pinde for at gøre det til en god oplevelse at være her. "
, MedCom. - Microsoft MCSE: Communication (Lync Server) (6 dage) (17/3/2014 til 22/3/2014)

"Et hårdt og intenst program, men klart værd at tage. Gode instruktører, der kender deres område."
Søren Bjørn-Fallesen , Aka IT. - Microsoft MCSA: SQL Server (9 dage) (8/3/2014 til 16/3/2014)

"Et SUPER kursus. Med en fremragende instruktør. Servicen hos Firebrand er i TOP klasse, Lokalerne er SUPER. "
Kenneth Liedtke, NI - Qaqortoq. - Microsoft MCSA: Windows Server 2012 R2 (9 dage) (12/1/2014 til 19/1/2014)

"Meget hårdt kursus - SUPER instruktør."
T.S.R, Fujitsu Denmark A/S. - Microsoft MCSD: SharePoint 2013 Applications – Developing Server Solutions (6 dage) (16/2/2014 til 21/2/2014)

"Professionelt"
Michael Nielsen , ColumbusIT. - Microsoft MCSD: SharePoint 2013 Applications – Developing Server Solutions (6 dage) (4/11/2013 til 9/11/2013)

"Vi kom hele vejen rundt om SharePoint 2013. Underviseren var blandt de bedste, jeg har oplevet: professional, betydelig viden om SharePoint - baade omkring Administration og indenfor udvikling - og han var en respektfuld og humoristisk underviser. Selvom det er anstrengende, er internat-formen glimrende til hurtigt at tilegne sig et omfattende materiale. Kursusstedet er beliggende, hvor der er fred og ro, personalet er venligt og imoedekommende, og maden er glimrende."
Peter Malling, Red Barnet. - Microsoft MCSE: SharePoint 2013 (6 dage) (28/10/2013 til 2/11/2013)

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