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Implementation Data Warehouse with MS-SQL Server 2012

Implementation Data Warehouse with MS-SQL Server 2012

Exam 70-463 Implimentating Datawarehouse with MS-Sql Server 2012 is meant for the Extract Transform Load (ETL) and data warehouse developers who create Business Intelligence (BI) solutions, and whose responsibilities include data cleansing, and Extract Transform Load and data warehouse implementation. In short, the Microsoft has stated that the candidates who fulfils following responsibilities are required to take up the said examination:

• Mastering data using Master Data Services

• Cleansing data using Data Quality Services

• ETL Package development for data loading/transfer/transformation

• Data Synchronization

• One-time data or application migration

• Ad-hoc data manipulations, transformations

• Scheduling ETL jobs

• Managing, configuring and deploying SQL Server Integration Services (SSIS) packages

After passing this exam, candidate gains eligibility for the following certifications:

• MCSA: SQL Server 2012

• MCSE: Data Platform

• MCSE: Business Intelligence

Mentioned below are the topics that the Exam 70-463 Implimentating Datawarehouse with MS-Sql Server 2012 covers in relevant percentage:

Design and implement a data warehouse (11%)

• Design and implement dimensions

• Design and implement fact tables

Extract and transform data (23%)

• Define connection managers

• Design data flow

• Implement data flow

• Manage SSIS package execution

• Implement script tasks in SSIS

Load data (27%)

• Design control flow

• Implement package logic by using SSIS variables and parameters

• Implement control flow

• Implement data load options

• Implement script components in SSIS

Configure and deploy SSIS solutions (24%)

• Troubleshoot data integration issues

• Install and maintain SSIS components

• Implement auditing, logging, and event handling

• Deploy SSIS solutions

• Configure SSIS security settings

Build data quality solutions (15%)

• Install and maintain data quality services

• Implement master data management solutions

• Create a data quality project to clean data

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