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CERTIFICATS

ETL Development with SQL Server and SSIS
OVERVIEW

Our certificate provides students with the knowledge and skills to provision a Microsoft SQL Server database. The course covers SQL Server provision both on-premise and in Azure that covers installing from new and migrating from an existing install.

Class Schedule: Tuesday and Thursday (6:00pm - 9:00pm EST)

KEY FEATURES

  • ETL Development with SQL Server and SSIS

    Get trained by industry Experts

  • ETL Development with SQL Server and SSIS

    Project Based Learning

  • ETL Development with SQL Server and SSIS

    Learn while you Work

  • ETL Development with SQL Server and SSIS

    State of the Art Infrastructure

  • ETL Development with SQL Server and SSIS

    24/7 Lab access

PLAN DE COURS

ETL Development with SQL Server and SSIS

This module describes data warehouse concepts and architecture consideration.

Lessons
Overview of Data Warehousing
Considerations for a Data Warehouse Solution
Lab : Exploring a Data Warehouse Solution
Exploring data sources
Exploring an ETL process
Exploring a data warehouse
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

This module describes the main hardware considerations for building a data warehouse.

Lessons
Considerations for data warehouse infrastructure.
Planning data warehouse hardware.
Lab : Planning Data Warehouse Infrastructure
Planning data warehouse hardware
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

This module describes how you go about designing and implementing a schema for a data warehouse.

Lessons
Data warehouse design overview
Designing dimension tables
Designing fact tables
Physical Design for a Data Warehouse
Lab : Implementing a Data Warehouse Schema
Implementing a star schema
Implementing a snowflake schema
Implementing a time dimension table
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

This module introduces Columnstore Indexes.

Lessons
Introduction to Columnstore Indexes
Creating Columnstore Indexes
Working with Columnstore Indexes
Lab : Using Columnstore Indexes
Create a Columnstore index on the FactProductInventory table
Create a Columnstore index on the FactInternetSales table
Create a memory optimized Columnstore table
After completing this module, you will be able to:

Create Columnstore indexes
Work with Columnstore Indexes

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
Copying data with the Azure data factory
Lab : Implementing an Azure SQL Data Warehouse
Create an Azure SQL data warehouse database
Migrate to an Azure SQL Data warehouse database
Copy data with the Azure data factory
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
WarehousePlan for migrating to Azure SQL Data Warehouse

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
Exploring source data
Transferring data by using a data row task
Using transformation components in a data row
After completing this module, you will be able to:

Describe ETL with SSIS
Explore Source Data
Implement a Data Flow

This module describes implementing control flow in an SSIS package.

Lessons
Introduction to Control Flow
Creating Dynamic Packages
Using Containers
Managing consistency.
Lab : Implementing Control Flow in an SSIS Package
Using tasks and precedence in a control flow
Using variables and parameters
Using containers
Lab : Using Transactions and Checkpoints
Using transactions
Using checkpoints
After completing this module, you will be able to:

Describe control flow
Create dynamic packages

Use containers

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
Debugging an SSIS package
Logging SSIS package execution
Implementing an event handler
Handling errors in data flow
After completing this module, you will be able to:

Debug an SSIS package
Log SSIS package events
Handle errors in an SSIS package

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
Loading modified data
Temporal Tables
Lab : Extracting Modified Data
Using a datetime column to incrementally extract data
Using change data capture
Using the CDC control task
Using change tracking
Lab : Loading a data warehouse
Loading data from CDC output tables
Using a lookup transformation to insert or update dimension data
Implementing a slowly changing dimension
Using the merge statement
After completing this module, you will be able to:

Describe incremental ETL
Extract modified data
Load modified data
Describe temporal tables

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
Creating a DQS knowledge base
Using a DQS project to cleanse data
Using DQS in an SSIS package
Lab : De-duplicating Data
Creating a matching policy
Using a DS project to match 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

This module describes how to implement master data services to enforce data integrity at source.

Lessons
Introduction to Master Data Services
Implementing a Master Data Services Model
Hierarchies and collections
Creating a Master Data Hub
Lab : Implementing Master Data Services
Creating a master data services model
Using the master data services add-in for Excel
Enforcing business rules
Loading data into a model
Consuming master data services data
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

This module describes how to extend SSIS with custom scripts and components.

Lessons
Using scripting in SSIS
Using custom components in SSIS
Lab : Using scripts
Using a script task
After completing this module, you will be able to:

Use custom components in SSIS
Use scripting in SSIS

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
Creating an SSIS catalog
Deploying an SSIS project
Creating environments for an SSIS solution
Running an SSIS package in SQL server management studio
Scheduling SSIS packages with SQL server agent
After completing this module, you will be able to:

Describe an SSIS deployment
Deploy an SSIS package
Plan SSIS package execution

This module describes how to debug and troubleshoot SSIS packages.

Lessons
Introduction to Business Intelligence
An Introduction to Data Analysis
Introduction to reporting
Analyzing Data with Azure SQL Data Warehouse
Lab : Using a data warehouse
Exploring a reporting services report
Exploring a PowerPivot workbook
Exploring a power view report
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

COMPÉTENCES ACQUISES

QUI DOIT POSTULER ?

This certificate is ideal for database professionals who are looking to fulfil a Business Intelligence Developer role looking to focus on creating BI solutions, including Data Warehouse implementation, ETL, and data cleansing.
The certificate is for data professionals and business intelligence professionals who want to learn how to perform data analysis using Power BI accurately.
This certificate is also for individuals who develop reports that visualize data from the data platform technologies in the cloud and on-premises. In addition, the certificate is also great for recent graduates who are looking to expand their skillset into Power BI.
This certificate is also for individuals who develop reports that visualize data from the data platform technologies in the cloud and on-premises. In addition, the certificate is also great for recent graduates who are looking to expand their skillset into Power BI.

ELIGIBILITY AND REQUIREMENTS

In addition to their professional experience, students who attend this training should already have the following technical knowledge:
Basic knowledge of the Microsoft Windows operating system and its core functionality. Working knowledge of relational databases. Some experience with database design.

Upon completing this course, you will receive an industry-recognized certificate from MCIT. Once you complete this exam, you will earn your official Implementing a SQL Data Warehouse certification.

INSTRUCTOR SPOTLIGHT

CALENDRIER

— FAQ —

Oui. Chaque participant reçoit un certificat d'achèvement à la fin.
Pour vous inscrire à un cours, vous pouvez soit nous appeler, soit saisir votre demande en ligne, et notre personnel administratif vous appellera pour vous inscrire.
De façon générale, MCIT exige des aptitudes d’écriture et de lecture en anglais de niveau secondaire 5. Prière de vérifier la description des cours pour connaître les préalables requis.