Implementing a Data Warehouse with Microsoft SQL Server (M20463) | MSP Training

ABOUT THE PROGRAM

The course provides the essential knowledge to implement a data warehouse platform to support a business intelligence solution. The 5-day course covers the crucial capabilities across the SQL Server data platform and discusses new components of SQL Server 2014. During the course, the delegates will learn how to create a data warehouse, extract and transform data, configure SQL Server Integration Services and deploy SQL Server Master Data Services and Data Quality Services.

The course is specifically designed for database administrators and professionals who are responsible for creating and supporting data warehousing solution within an organization. The course teaches delegates to support incremental data extraction and data loading by implementing extracting, transforming and loading (ETL) process. After the completion of the course, the delegates will be able to create business intelligence solutions, implement data warehouse and data cleansing and validating effectively.

Deploy and Configure SSIS packages effectively
Enforce data integrity using SQL Server Data Quality Services
Managing consistency by implementing ETL solutions
Manage and monitor the execution of deployed packages
Courses led by highly certified and well experienced Microsoft Server Instructors

WHAT'S INCLUDED ?

Find out what's included in the training programme.

Tutor Support

A dedicated tutor will be at your disposal throughout the training to guide you through any issues.

Key Learning Points

Clear and concise objectives to guide delegates through the course.

Exam(s) included

Exams are provided, as part of the course. Obtaining certification is dependant on passing these exams

Certificate

Delegates will get certification of completion at the end of the course.

PREREQUISITES

The delegates must have at least two years of working experience with relational databases. These include:

Designing tables and relationships
Querying with Transact-SQL
Creating a normalized database
Having knowledge of key business priorities and basic programming constructs

TARGET AUDIENCE

The course is intended for database administrators and professionals responsible for:

Designing SQL Server integration service package for extracting, loading and transforming data
Apply data quality services for cleansing data
Implementing a data warehouse
Use master data services for administering data integrity

WHAT WILL YOU LEARN?

Understand concepts and basic architecture of data warehouse
Able to design and implement a data warehouse
Describe an appropriate hardware platform for a data warehouse
Get to know the configuration of SSIS packages
Learn how to implement data flow and control flow in SSIS Package
Explain debugging and troubleshooting of SSIS packages
Describe how Microsoft data quality services use for data cleansing and validating
Understand SSIS expansion with custom scripts and components
Learn to implement a SSIS solution supporting extracting data and incremental data warehouse loads
Learn to enforce data integrity by implementing master data services
Describe how business intelligence solutions consume data in a data warehouse

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PROGRAM OVERVIEW

The course enables the delegates to implement an ETL solution that supports incremental data extraction and data loading. The course provides an opportunity to learn the key components and considerations of a data warehousing project and describes common approaches for handling errors in data flow and control flow. Throughout the course, the delegates will learn how to design and implement a data warehouse, deploy and configure SSIS packages and implement control flow and data flow in server integration packages.

Important information on implementing a Data Warehouse with Microsoft SQL Server exam

The course prepares the delegates to enable them to sit in the examination and cover the following aspects:

Design and Implement a Data Warehouse
Data loading
Extract and transform data
Deploy and configure SSIS Solutions
Building data quality solutions

PROGRAM CONTENT

An Introduction to Data Warehousing

In this module, while undertaking data warehousing projects one must take into account the key components of data warehousing and other high-level considerations of SQL Server.

Overview of data warehousing
Understand its basic architecture
Understand the key elements of a data warehousing solution
Describe considerations for data warehousing solution

Planning Data Warehouse Infrastructure

In this module, the delegates will identify the parameters to consider for selecting data warehouse hardware and distributing SQL Server facilities across servers.

Considerations for Data Warehousing Infrastructure
Data Warehouse Reference Architectures and Appliances
Planning Data Warehouse Hardware

Design and implement Data Warehouse

The topic covers understanding about key factors for the logical design of data warehouse and then discuss best practices for physical implementation.

Overview of Data Warehouse design
Design dimension tables
Design fact tables
Physical design for data warehouse
Design and implement effective physical data structures

Create an ETL solution with SSIS

This topic elaborates considerations for implementing ETL process and then focus on Microsoft SQL Server Integration Services (SSIS) for building ETL solutions.

Understand process of ETL with SSIS
Describe key features of SSIS
Use SSIS for implementation of data flow
Explore data sources

Implementing Control Flow in a SSIS package

In this module, the delegates learn how to implement ETL solutions that consolidate multiple tasks and workflow logic.

Introduction to control flow
Use of task constraints to execute control flow
Develop dynamic packages including parameters and variables
Use containers in a package control flow
Techniques to manage consistency with checkpoints and transactions

Debugging and troubleshooting SSIS packages

This module discusses how you can debug packages to find the reason of errors occur during execution and also understand the logging functionality built into SSIS that can be used to log events for troubleshooting purposes. The delegates also get familiar with the basic approaches for handling errors in control flow and data flow.

Describe Debugging of a SSIS package
Logging SSIS package events
Techniques to handle errors in SSIS package

Implementing a Data Extraction Solution

This module covers the techniques that can be used to perform an incremental data warehouse refresh process.

Overview of Incremental ETL
Plan data extraction
Loading modified data

Loading data into Data Warehouse

In this module, the techniques used to implement data warehouse load process is covered.

Plan data loads
Use SSIS for incremental loads
Use Transact-SQL loading techniques

Enforce Data Quality

This module covers Microsoft SQL Server Data Quality Services (DQS) and describes how it can be used to cleanse and de-duplicate data.

Understand importance of Data Quality
Overview of Microsoft SQL Server Data Quality Services (DQS)
Using data quality services for cleansing and validating

Master data Services

This module describes how Master Data Services provides a way for organizations to standardize data and improve its quality, consistency, and reliability that helps in key business decisions. It also explains Master Data Services and its advantages of applying it.

Introduction to Master Data Services concepts
Implement Master Data Services model
Manage Master data
Create Master data hub

Extending SQL Server Integration services

This module covers the techniques to extend SSIS and also provide awareness of major steps required to use scripts in an ETL process and custom components based on SSIS.

Include custom scripts in SSIS package
Use custom components in SSIS to extend SSIS

Deploy and configure SSIS packages

The topics cover how to implement packages and their dependencies to the server.

Describe key elements of SSIS deployment
Deploy SSIS projects
Plan SSIS package execution
Monitor the implementation of deployed packages

Consuming Data in Data Warehouse

This module describes Business Intelligence solutions that data warehouse can use as a basis for enterprise and self-service BI lessons.

Understand BI and common scenarios of BI
Enterprise Business Intelligence
Describe reporting
Overview of data analysis
Big data and Self-Service BI

Implementing a Data Warehouse with Microsoft SQL Server (M20463) Enquiry

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ABOUT Swansea

Swansea is a coastal city lies on the south coast of Wales with a population of around 241,300 in 2014. The county of Swansea is the second largest and most populous city in the Wales followed by Cardiff. The city is situated between the ancient Welsh and Glamorgan‘s historic county boundaries and surrounded by Bristol Channel and Swansea Bay. The city was also known as Copperopolis due to the presence of copper industry in the 19^th century. The town got the city status in 1969 during the Prince Charles’s tour to Wales. The city comprises four physical areas include Gower Peninsula, urban and suburban zone stretching to the towns of Gorseinon, Lliw uplands and coastal strip around Swansea Bay.

History

The name of the city is derived from Sveinn’s island, referring settlement is developed on the raised ground in marshes. The early evidence of the area demonstrated the Bronze Age, Stone Age and Iron Age. It has been suggested that the city has developed as a Viking trading post. The first charter was granted to the city in the 11^th century and it gained the status of the borough. A marcher lordship was formed in the city after the Norman Empire and incorporated land around the manor of Kilvey, Swansea Bay and the peninsula. The borough charter was granted between 1158 and 1184 and gained the status of chief lordship town.

During the Industrial Revolution, the city served as the key logical base for copper smelting works. The wool, cloth, wine and coal was traded through the Swansea port. The population of the city expanded significantly in the 18^th century. The lower Swansea valley was filled with waste products and derelict works due to decline in heavy industries in the town in the 20^th century. The Lower Swansea Valley Scheme was introduced in the city to bring the wasteland into the usable condition. This resulted in the construction of Enterprise Zone over the wasteland and north and south dock became Parc Tawe and Marina respectively. The city was the prime target of German bombers during the Second World War due to the presence of the heavy industries in the town and the centre of the town was significantly affected.

Economy

The economy of the city was based on metals, copper and mining industry in the 18^th century. These heavy industries declined and the majority of the copper ores were melted in the lower Swansea valley. The trend of the economy shifted to light industries and service sector. More than 90% of the workforce is engaged in the service sector including banking, finance, education, public administration and insurance. Headquarter of the Driver and Vehicle Licensing Agency is based in the city and provided employment to around 6000 people in the city while other major employers in the city include Virgin Media, Amazon, Admiral Group, BT and HSBC. The city is home to the worldwide customer care centre of the Virgin Atlantic, handling baggage claims, customer relations, reservations and sales.

Education

The city has six infants, one nursery, five junior, fifteen comprehensive and seventy-seven primary schools in the city. The campus of Swansea University is located in the Singleton Park for higher education. The engineering department of the university has done an outstanding work for solving engineering design problems and acknowledged as a centre of excellence. The other departments of the university such as Physics, Computer Science and History were also renowned for their achievements. The third best medical school in the country after Cambridge and Oxford ‘Swansea University Medical School’ is located in the city. The other higher educational institutes are Gower College and University of Wales Trinity Saint David.

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