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MCSE SQL 2016: Data Management & Analytics inkl. MCSA BI (70-767 & 70-768 & 70-473)

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  • mar 18
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Kursusinfo

  • Dette kursus varer 11 dage
  • Der medfølger kursusmateriale til dette kursus
  • Dette kursus koster 14 klip på dit klippekort.
  • Fuld forplejning (Morgenmad, frokost, kage, kaffe og sodavand ad libitum)
  • Fuld beståelsesgaranti er inkluderet i prisen - Læs mere her
  • Eksamen er inkluderet i prisen
  • Med i prisen hører Practice Test
  • Du har i alt adgang til din kursus-pc i 3 uger

Varighed

Dette kursus varer 11 dage

Materialer

Der medfølger kursusmateriale til dette kursus

Klip på klippekort

Dette kursus koster 14 klip på dit klippekort.

Forplejning

Fuld forplejning (Morgenmad, frokost, eftermiddagskage samt kaffe og sodavand ad libitum)

Garanti for beståelse

Fuld beståelsesgaranti er inkluderet i prisen - Læs mere her

Eksamen

Alle eksamensforsøg er inkluderet i prisen

Practice Tests

Med i prisen hører Practice Test

Remote adgang

Du har i alt adgang til din kursus-pc i 3 uger

Microsoft Vouchers

11 vouchers + 9.950 kr. for Certificeringspakken.
Læs mere her

Download PDF

Hen PDF om kurset her

MCSE: Data Management and Analytics er en ny titel fra Microsoft. Titlen er frigivet i september 2016 og opnås ved at bestå i alt 3 eksamener (2 obligatoriske og 1 valgfri). 

Vi har hos Skillshouse sammensat et spændende MCSE kursusforløb baseret på de Microsoft SQL Server 2016 Business Intelligence kurser, som også giver dig titlen; Microsoft MCSA SQL Server 2016 Business Intelligence Development og som valgfri valgt det hele nye: "Designing and Implementing Cloud Data Platform Solutions" kursus, men husk at du sagtens kan vælge et af de andre valgfri kurser i stedet for - se mere længere nede i teksten. 

Opbygningen af MCSE kursusforløbet betyder, at du sparer tid i forhold til de traditionelle kurser og samtidigt foregår undervisningen via masser af hands-on og best practices, som sikrer, bedre indlærring og derved bedre brug af dine nye færdigheder. 

Kort fortalt:

  • Du sparer mere end 17.000,-
  • Garanteret beståelse og alle de eksamensforsøg du skal bruge 
  • Alt er inkluderet inkl. autoriseret Microsoft kursusmateriale.
  • Undervisningen foretages af Microsoft Certified Trainer (MCT)
  • Fuld forplejning og selvfølgelig alle de colaer og Red Bull du kan drikke 🙂

Undervisningen er fra 09.00 til 16.00

Sammensæt din egen MCSE: Data Management and Analytics
Du kan "næsten" sammensætte din MCSE: Data Management and Analytics som du ønsker, dog er der 2 obligatoriske certificeringer og herefter skal du vælge 1 valgfri certificering. Dvs. 3 certificeringer i alt.  Du kan få et nemt overblik ved at downloade vores brochure øverst på denne side. 

Obligatoriske certificeringer som begge skal bestås: 

- Implementing a SQL Data Warehouse (eksamen 70-767)
- Developing SQL Data Models (eksamen 70-768)

Når disse 2 certificeringer er bestået har du opnået titlen: Microsoft MCSA SQL Server 2016 Business Intelligence Development. 

Her efter skal du bestå 1 af nedenstående certificeringer for at opnå titlen: MCSE: Data Management and Analytics

Vi har som nævnt ved bestilling af din MCSE: Data Management and Analytics uddannelse tilvalgt kurset "Designing and Implementing Cloud Data Platform Solutions", men du kan selvfølgelig vælge en af de andre certificeringer i stedet for - oplys blot dette ved bestilling. 

 

Course Outline for Implementing a SQL Data Warehouse (70-767)

Module 1: Introduction to Data Warehousing
Describe data warehouse concepts and architecture considerations.

Lessons
Overview of Data Warehousing
Considerations for a Data Warehouse Solution

Lab : Exploring a Data Warehouse Solution
After completing this module, you will be able to:
Describe the key elements of a data warehousing solution
Describe the key considerations for a data warehousing solution

Module 2: Planning Data Warehouse Infrastructure
This module describes the main hardware considerations for building a data warehouse.

Lessons
Considerations for Building a Data Warehouse
Data Warehouse Reference Architectures and Appliances

Lab : Planning Data Warehouse Infrastructure
After completing this module, you will be able to:
Describe the main hardware considerations for building a data warehouse
Explain how to use reference architectures and data warehouse appliances to create a data warehouse

Module 3: Designing and Implementing a Data Warehouse
This module describes how you go about designing and implementing a schema for a data warehouse.

Lessons
Logical Design for a Data Warehouse
Physical Design for a Data Warehouse

Lab : Implementing a Data Warehouse Schema
After completing this module, you will be able to:
Implement a logical design for a data warehouse
Implement a physical design for a data warehouse

Module 4: Columnstore Indexes
This module introduces Columnstore Indexes.

Lessons
Introduction to Columnstore Indexes
Creating Columnstore Indexes
Working with Columnstore Indexes

Lab : Using Columnstore Indexes
After completing this module, you will be able to:
Create Columnstore indexes
Work with Columnstore Indexes

Module 5: Implementing an Azure SQL Data Warehouse
This module describes Azure SQL Data Warehouses and how to implement them.

Lessons
Advantages of Azure SQL Data Warehouse
Implementing an Azure SQL Data Warehouse
Developing an Azure SQL Data Warehouse
Migrating to an Azure SQ Data Warehouse

Lab : Implementing an Azure SQL Data Warehouse
After completing this module, you will be able to:
Describe the advantages of Azure SQL Data Warehouse
Implement an Azure SQL Data Warehouse
Describe the considerations for developing an Azure SQL Data Warehouse
Plan for migrating to Azure SQL Data Warehouse

Module 6: Creating an ETL Solution
At the end of this module you will be able to implement data flow in a SSIS package.

Lessons
Introduction to ETL with SSIS
Exploring Source Data
Implementing Data Flow

Lab : Implementing Data Flow in an SSIS Package
After completing this module, you will be able to:
Describe ETL with SSIS
Explore Source Data
Implement a Data Flow

Module 7: Implementing Control Flow in an SSIS Package
This module describes implementing control flow in an SSIS package.

Lessons
Introduction to Control Flow
Creating Dynamic Packages
Using Containers

Lab : Implementing Control Flow in an SSIS Package
Lab : Using Transactions and Checkpoints
After completing this module, you will be able to:
Describe control flow
Create dynamic packages
Use containers

Module 8: Debugging and Troubleshooting SSIS Packages
This module describes how to debug and troubleshoot SSIS packages.

Lessons
Debugging an SSIS Package
Logging SSIS Package Events
Handling Errors in an SSIS Package

Lab : Debugging and Troubleshooting an SSIS Package
After completing this module, you will be able to:
Debug an SSIS package
Log SSIS package events
Handle errors in an SSIS package

Module 9: Implementing an Incremental ETL Process
This module describes how to implement an SSIS solution that supports incremental DW loads and changing data.

Lessons
Introduction to Incremental ETL
Extracting Modified Data
Temporal Tables

Lab : Extracting Modified Data
Lab : Loading Incremental Changes
After completing this module, you will be able to:
Describe incremental ETL
Extract modified data
Describe temporal tables

Module 10: Enforcing Data Quality
This module describes how to implement data cleansing by using Microsoft Data Quality services.

Lessons
Introduction to Data Quality
Using Data Quality Services to Cleanse Data
Using Data Quality Services to Match Data

Lab : Cleansing Data
Lab : De-duplicating Data
After completing this module, you will be able to:
Describe data quality services
Cleanse data using data quality services
Match data using data quality services
De-duplicate data using data quality services

Module 11: Using Master Data Services
This module describes how to implement master data services to enforce data integrity at source.

Lessons
Master Data Services Concepts
Implementing a Master Data Services Model
Managing Master Data
Creating a Master Data Hub

Lab : Implementing Master Data Services
After completing this module, you will be able to:
Describe the key concepts of master data services
Implement a master data service model
Manage master data
Create a master data hub

Module 12: Extending SQL Server Integration Services (SSIS)
This module describes how to extend SSIS with custom scripts and components.

Lessons
Using Custom Components in SSIS
Using Scripting in SSIS

Lab : Using Scripts and Custom Components
After completing this module, you will be able to:
Use custom components in SSIS
Use scripting in SSIS

Module 13: Deploying and Configuring SSIS Packages
This module describes how to deploy and configure SSIS packages.

Lessons
Overview of SSIS Deployment
Deploying SSIS Projects
Planning SSIS Package Execution

Lab : Deploying and Configuring SSIS Packages
After completing this module, you will be able to:
Describe an SSIS deployment
Deploy an SSIS package
Plan SSIS package execution

Module 14: Consuming Data in a Data Warehouse
This module describes how to debug and troubleshoot SSIS packages.

Lessons
Introduction to Business Intelligence
Introduction to Reporting
An Introduction to Data Analysis
Analyzing Data with Azure SQL Data Warehouse

Lab : Using Business Intelligence Tools
After completing this module, you will be able to:
Describe at a high level business intelligence
Show an understanding of reporting
Show an understanding of data analysis
Analyze data with Azure SQL data warehouse

Course Outline for Developing SQL Data Models (70-768)

Module 1: Introduction to Business Intelligence and Data Modeling
This module introduces key BI concepts and the Microsoft BI product suite.

Lessons
Introduction to Business Intelligence
The Microsoft business intelligence platform

Lab : Exploring a Data Warehouse
After completing this module, you will be able to:
Describe the concept of business intelligence
Describe the Microsoft business intelligence platform

Module 2: Creating Multidimensional Databases
This module describes the steps required to create a multidimensional database with analysis services.

Lessons
Introduction to multidimensional analysis
Creating data sources and data source views
Creating a cube
Overview of cube security

Lab : Creating a multidimensional database
After completing this module, you will be able to:
Use multidimensional analysis
Create data sources and data source views
Create a cube
Describe cube security

Module 3: Working with Cubes and Dimensions
This module describes how to implement dimensions in a cube.

Lessons
Configuring dimensions
Define attribute hierarchies
Sorting and grouping attributes

Lab : Working with Cubes and Dimensions
After completing this module, you will be able to:
Configure dimensions
Define attribute hierarchies.
Sort and group attributes

Module 4: Working with Measures and Measure Groups
This module describes how to implement measures and measure groups in a cube.

Lessons
Working with measures
Working with measure groups

Lab : Configuring Measures and Measure Groups
After completing this module, you will be able to:
Work with measures
Work with measure groups

Module 5: Introduction to MDX
This module describes the MDX syntax and how to use MDX.

Lessons
MDX fundamentals
Adding calculations to a cube
Using MDX to query a cube

Lab : Using MDX
After completing this module, you will be able to:
Describe the fundamentals of MDX
Add calculations to a cube
Query a cube using MDX

Module 6: Customizing Cube Functionality
This module describes how to customize a cube.

Lessons
Implementing key performance indicators
Implementing actions
Implementing perspectives
Implementing translations

Lab : Customizing a Cube
After completing this module, you will be able to:
Implement key performance indicators
Implement actions
Implement perspectives
Implement translations

Module 7: Implementing a Tabular Data Model by Using Analysis Services
This module describes how to implement a tabular data model in PowerPivot.

Lessons
Introduction to tabular data models
Creating a tabular data model
Using an analysis services tabular model in an enterprise BI solution

Lab : Working with an Analysis services tabular data model
After completing this module, you will be able to:
Describe tabular data models
Create a tabular data model
Be able to use an analysis services tabular data model in an enterprise BI solution

Module 8: Introduction to Data Analysis Expression (DAX)
This module describes how to use DAX to create measures and calculated columns in a tabular data model.

Lessons
DAX fundamentals
Using DAX to create calculated columns and measures in a tabular data model

Lab : Creating Calculated Columns and Measures by using DAX
After completing this module, you will be able to:
Describe the fundamentals of DAX
Use DAX to create calculated columns and measures in a tabular data model

Module 9: Performing Predictive Analysis with Data Mining
This module describes how to use data mining for predictive analysis.

Lessons
Overview of data mining
Using the data mining add-in for Excel
Creating a custom data mining solution
Validating a data mining model
Connecting to and consuming a data mining model

Lab : Perform Predictive Analysis with Data Mining
After completing this module, you will be able to:
Describe data mining
Use the data mining add-in for Excel
Create a custom data mining solution
Validate a data mining solution
Connect to and consume a data mining solution

Course outline for Designing and Implementing Cloud Data Platform Solutions (70-473)

The focus of this three-day instructor-led Microsoft Training course is on designing and implementing cloud data platform solutions with the Microsoft Data Platform by using SQL Server on-premises, hybrid and cloud data platform solutions. It describes how to design and implement and optimize workloads in hybrid scenarios with both on-premises and Microsoft Azure cloud-based solutions, and how to implement high availability and disaster recovery solutions.

Deltager profil
The primary audience for this course are database professionals who want to enhance their existing SQL Server skills with hybrid solutions and cloud solution architecture using the Microsoft Data Platform.

Efter endt kursus
Efter endt kursus vil du have følgende kompetencer:

• Position Microsoft Cloud Data Platform Solutions
• Design and implement solutions on Azure using SQL Server
• Design and implement solutions on Azure using SQL Database
• Design and implement security, access and auditing for cloud data platform solutions
• Design highly scalable mission critical solutions using the Microsoft Data Platform
• Design and implement cloud data platform solutions.

Kursusbeskrivelse for Designing and Implementing Cloud Data Platform Solutions

Module 1: Microsoft Cloud Data Platform Overview
This module provides an overview of the Microsoft Data Platform Solutions in Hybrid and Cloud based scenarios. The module provides an overview of offerings and capabilities.

Lessons
Microsoft Cloud Data Platform
Microsoft SQL Server
Common Tools
Cloud Data Offerings
Data Visualizations
Cortana Analytics Suite

Module 2: Implement SQL Server on Azure VM
This module with a hands-on-lab describes the core capabilities of running SQL Server in a virtual machine environment (IAAS) on Azure.

Lessons
Virtual machine components
Storage Architecture
Azure deployment models
Virtual machine tiers
Virtual machine deployments
Deployment scenarios
Move existing workloads
Connecting to Azure VMs
Domain joining VMs
VM best practices

Lab : SQL Server in Azure VMs

Module 3: Implement SQL Database
This module with a hands-on-lab describes the core capabilities of implementing data platform solutions using SQL Database on Azure. The module provides details in service tiers and offerings.

Lessons
What is SQL Database
Data tiers/ Elastic data tiers
Scalability Options
Elastic Databases
Elastic Database Tools
SQL Database performance
Service Tier Advisor

Lab : Implementing SQL Database

Module 4: SQL Database High Availability and disaster recovery
This module with a hands-on lab describes high availability and disaster recovery scenarios with SQL Database.

Lessons
Business Continuity with SQL Database
SQL Database HA/DR
SQL Database Backup Solutions
SQL Database Replication Scenarios

Lab : SQL Database HA/DR

Module 5: Hybrid HA/DR scenarios with SQL Server
This module with a hands-on lab exercise describes the design of high available, scalable data platform solutions on-premises, in the cloud and in hybrid cloud scenarios using SQL Server on premises and with Azure VMs.

Lessons
HA/DR deployment architectures
AlwaysOn availability groups
AlwaysOn Failover Clustering
Database Mirroring
Log Shipping
Backup to Azure
Managed Backups
SQL Server data files on Azure

Lab : Implement HA/DR with SQL Server in Azure VMs

Module 6: Design and Implement Security
This module with a hands-on-lab describes the security features and capabilities in SQL Server in Azure VMs and SQL Database including firewalls andauditing.

Lessons
SQL Database Security Approach
SQL Database Firewall Security
SQL Database Active Directory Integration
Encryption
Row Level Security
Dynamic Data Masking
Auditing and Threat Detection

Lab : Design and Implement SQL Database Security

Module 7: Monitor and manage implementations on Azure
This module with a hands-on-lab focuses on management and deployment, monitoring for performance of SQL Database and SQL Server in Azure VMs.

Lessons
Azure Automation Capabilities
SQL Monitoring and Management
Operational Insights

Lab : Monitoring and manage implementations

Module 8: Design and implement database solutions for SQL Server and SQL Database
This module covers design and architecture scenarios for implementation of database solutions using the Microsoft Cloud Data Platform. This module introduces key scenarios to discuss with classroom attendees in order to build out the right architecture and infrastructure based on architecture and business requirements.

Lessons
Case Study – SQL Server Hybrid Scenario
Case Study – Lift and Shift

Lignende kurser

Microsoft Azure Architect Technologies (AZ-300) 21 jan 19
SQL og DataAnalyse 18 dec
Performance Tuning and Optimizing SQL Databases (10987) 07 jan 19
Perform Cloud Data Science with Azure Machine Learning 20774 (70-774) 17 dec
Developing SQL Databases MOC 20762 (70-762) 11 feb 19