Microsoft/Implementing a SQL 2016 Data Warehouse (SSIS)


Implementing a SQL 2016 Data Warehouse (SSIS) Certification Training Course Overview

Implementing SQL Data Warehouse (SSIS) training course imparts core skills on the implementation of data warehouse platform for supporting a BI solution. Participants enrolled for this SQL Data Warehouse certification will learn to create the data warehouse with Microsoft SQL Server 2016 and Azure SQL Data Warehouse. The course also covers the Implementation of ETL using Integration Services, validation and cleansing of data using Data Quality Services and Master Data Services.

Implementing SQL Data Warehouse course is ideal for database professionals, BI developers, and professionals who are responsible for creating BI solutions in an enterprise environment.

SSIS Tasks

SSIS is an ETL tool that performs various operations like loading the data based on need, executes data related calculations and defines a workflow for the process and tasks.

Types of Data Warehouse

There are three types of data warehouse:

  • Operational Data Store – An Operational Data Store (ODS) is a type of database that processes data from multiple sources and sends it to the corresponding operational system and data warehouse. Since an ODS is used to store short term data, it serves as an intermediate database.
  • Enterprise Data Warehouse – An Enterprise Data Warehouse is a common database that holds all the business data and makes it available across the company for analysis and planning purposes.
  • Data Mart – A data mart is responsible for serving a particular group by making the specific set of data available to that group so that the users don’t have to waste their precious time and efforts in searching for the data they need.
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Implementing a SQL 2016 Data Warehouse (SSIS) (40 Hours) Download Course Contents

Instructor - Led Online Training Fee For Both Group Training & 1-on-1 Training 1700
Group Training

03,04,05,06,07 May
09:00 AM - 05:00 PM CST
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07,08,09,10,11 Jun GTR
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Course Modules

  • Lesson 1: Overview of Data Warehousing
  • Lesson 2: Considerations for a Data Warehouse Solution
  • Lab: Exploring a Data Warehousing Solution
  • Lesson 1: Considerations for Data Warehouse Infrastructure
  • Lesson 2: Planning Data Warehouse Hardware
  • Lab: Planning Data Warehouse Infrastructure
  • Lesson 1: Data Warehouse Design Overview
  • Lesson 2: Designing Dimension Tables
  • Lesson 3: Designing Fact Tables
  • Lesson 4: Physical Design for a Data Warehouse
  • Lab: Implementing a Data Warehouse
  • Lesson 1: Introduction to Columnstore Indexes
  • Lesson 2: Creating Columnstore Indexes
  • Lesson 3: Working with Columnstore Indexes
  • Lab: Using Columnstore Indexes
  • Lesson 1: Advantages of Azure SQL Data Warehouse
  • Lesson 2: Implementing an Azure SQL Data Warehouse Database
  • Lesson 3: Developing an Azure SQL Data Warehouse
  • Lesson 4: Migrating to an Azure SQL Data Warehouse
  • Lesson 5: Copying Data with the Azure Data Factory
  • Lab: Implement an Azure SQL Data Warehouse
  • Lesson 1: Introduction to ETL with SSIS
  • Lesson 2: Exploring Source Data
  • Lesson 3: Implementing Data Flow
  • Lab: Implementing Data Flow in an SSIS Package
  • Lesson 1: Introduction to Control Flow
  • Lesson 2: Creating Dynamic Packages
  • Lesson 3: Using Containers
  • Lab A: Implementing Control Flow in an SSIS Package
  • Lesson 4: Managing Consistency
  • Lab B: Using Transactions and Checkpoints
  • Lesson 1: Debugging an SSIS Package
  • Lesson 2: Logging SSIS Package Events
  • Lesson 3: Handling Errors in an SSIS Package
  • Lab: Debugging and Troubleshooting an SSIS Package
  • Lesson 1: Introduction to Incremental ETL
  • Lesson 2: Extracting Modified Data
  • Lab A: Extracting Modified Data
  • Lesson 3: Loading Modified Data
  • Lesson 4: Temporal Tables
  • Lab B: Loading a Data Warehouse
  • Lesson 1: Introduction to Data Quality
  • Lesson 2: Using Data Quality Services to Cleanse Data
  • Lab A: Cleansing Data
  • Lesson 3: Using Data Quality Services to Match Data
  • Lab B: Deduplicating Data
  • Lesson 1: Introduction to Master Data Services
  • Lesson 2: Implementing a Master Data Services Mode
  • Lesson 3: Hierarchies and Collections
  • Lesson 4: Creating a Master Data Hub
  • Lab: Implementing Master Data Services Model
  • Lesson 1: Using Scripts in SSIS
  • Lesson 2: Using Custom Components in SSIS
  • Lab: Using Custom Scripts
  • Lesson 1: Overview of SSIS Development 13-2
  • Lesson 2: Deploying SSIS Projects 13-5
  • Lesson 3: Planning SSIS Package Execution 13-14
  • Lab: Deploying and Configuring SSIS Packages
  • Lesson 1: Introduction to Business Intelligence
  • Lesson 2: Introduction to Data Analysis
  • Lesson 3: Introduction to Reporting
  • Lesson 4: Analyzing Data with Azure SQL Data Warehouse
  • Lab: Using a Data Warehouse
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Course Prerequisites

  • Designing a normalized database.
  • Creating tables and relationships.
  • Querying with Transact-SQL.
  • Some exposure to basic programming constructs (such as looping and branching).


Upon Completion of this Course, you will accomplish following:-

  • Understand the components of a data warehousing solution
  • Implement a logical and physical design to create a data warehouse
  • Implement a physical design for a data warehouse
  • Understand SSIS for implementing data flows
  • Create dynamic packages using parameters and variables
  • Implement Data Quality Services and Master Data Services
  • Implement custom components for extending SSIS
  • Implement Business Intelligence and its common scenarios in this SQL Data Warehouse certification training

Give an edge to your career with Microsoft certification training courses. Students can join the classes for 20767: SSIS Training for Implementing a SQL 2016 Data Warehouse (SSIS) at Koenig Campus located at New Delhi, Bengaluru, Shimla, Goa, Dehradun, Dubai & Instructor-Led Online.

FAQ's

No, the published fee includes all applicable taxes.

Microsoft SSIS (SQL Server Integration Services) is a business intelligence tool that is designed to make data migration easier. The main objective of SSIS is to analyze and cleanse data and run extract, transform and load (ETL) processes for data warehousing.

SSIS package configuration is referred to the settings that help us modify a property without opening the package. This is possible due to the fact that the configuration is stored somewhere outside the code that makes up the SSIS package.

Some of the prominent differences between SSIS and SSRS are:

  • SSIS refers to SQL Server Integration Services while SSRS stands for SQL Server Reporting Services.
  • The main feature of SSIS is data holding which includes components such as Import and Export Wizard, SSIS API programming and SSIS Designer. On the other hand, SSRS is mainly used for reporting with components such as Report Designer, Report Builder, Report Server and Report Manager.

An SQL Server Data Warehouse is a kind of central repository used for storing heterogeneous data related to the purposes of analysis and reporting. It consolidates, standardizes and organizes the data for better decision making.

There are 7 important steps that one follow to build a robust data warehouse:

  • Determining the objectives of the company
  • Collecting and analyzing information
  • Identifying major or core business processes
  • Constructing a conceptual data model
  • Locating data sources and planning data transformations
  • Tracking the warehouse storage duration
  • Implementing the plan

A database is an organized collection of data. On the other hand, a data warehouse is a kind of database (or group of databases) created to store, filter, retrieve and analyze huge volumes of data. The basic approach these days is to store data from all the databases into a data warehouse which enables one time analysis and visualization on the complete bulk of data.

A data warehouse is a repository of large volume of operational and customer-related data imported from other databases. This ensures analysis of data in a more holistic way. For example, from a business point of few, a data warehouse might include customer information, website details, mailing lists, comment cards and employee information including time cards, demographic data and salary information.

The major benefits of a data warehouse include:

  • Enhanced system performance
  • Timely data access
  • Increased consistency and quality
  • High Return on Investment (ROI)
  • Access to historical data
  • Cost-effective decision making

The features of a data warehouse include:

  • Subject Oriented
  • Integrated
  • Time Variant
  • Non-Volatile
  • Data Granuality

Data warehousing is applicable to a whole lot of sectors including banking, finance, consumer goods, government and education, healthcare, hospitality, insurance, services, telephone and transportation, among many others.

The data warehouse tools help build enterprise data solutions to fetch information from the data easily and quickly in the cloud. Here is a list of the most popular data warehouse tools:

  • Amazon Redshift
  • Teradata
  • Oracle 12c
  • Informatica
  • IBM Infosphere
  • Ab Initio Software
  • ParAccel
  • Cloudera
  • AnalytiX DS
  • MarkLogic