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Master the Skills of Data Warehouse Implementation with SQL Server 2012 and CBT Nuggets 70-463 Certification


CBT Nuggets 70-463: Implementing a Data Warehouse with Microsoft SQL Server 2012




If you want to learn how to design and implement a data warehouse using Microsoft SQL Server 2012, then you should check out CBT Nuggets 70-463 course. This course will help you prepare for the 70-463 exam, which is one of the requirements for earning the MCSA: SQL Server 2012 certification. In this article, we will give you an overview of what CBT Nuggets is, what the 70-463 exam covers, and what you will learn in this course. We will also provide you with some tips and resources for passing the exam and becoming a certified data warehouse professional.




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Introduction




What is CBT Nuggets and what are its benefits?




CBT Nuggets is an online learning platform that offers high-quality video training courses for IT professionals. CBT Nuggets courses are taught by experienced and certified instructors who explain complex concepts in a simple and engaging way. CBT Nuggets courses also include quizzes, practice exams, virtual labs, and flashcards to help you test your knowledge and skills. CBT Nuggets courses are accessible anytime, anywhere, and on any device, so you can learn at your own pace and convenience. CBT Nuggets also offers a 7-day free trial, so you can try it out before you buy it.


What is the 70-463 exam and what are its objectives?




The 70-463 exam is one of the three exams that you need to pass to earn the MCSA: SQL Server 2012 certification. The other two exams are 70-461: Querying Microsoft SQL Server 2012 and 70-462: Administering Microsoft SQL Server 2012 Databases. The MCSA: SQL Server 2012 certification validates your skills and knowledge in working with SQL Server 2012 databases and data warehouses. The 70-463 exam focuses on implementing a data warehouse with SQL Server 2012. The exam objectives are:



  • Design and implement a data warehouse (10-15%)



  • Extract and transform data (20-25%)



  • Load data (25-30%)



  • Configure and deploy SSIS solutions (20-25%)



  • Build data quality solutions (15-20%)



The exam consists of 40-60 multiple-choice, drag-and-drop, and simulation questions. You have 120 minutes to complete the exam. You need to score at least 700 out of 1000 points to pass the exam. The exam costs $165 USD.


Who should take this course and exam?




This course and exam are suitable for anyone who wants to learn how to design and implement a data warehouse using SQL Server 2012. This includes database developers, database administrators, business intelligence developers, ETL developers, data analysts, and data architects. To take this course and exam, you should have at least two years of experience working with relational databases, including designing, creating, and maintaining databases using SQL Server. You should also have some basic knowledge of data warehouse concepts, such as star schemas, dimensions, facts, ETL processes, etc.


Designing and Implementing a Data Warehouse




Data Warehouse Concepts




A data warehouse is a centralized repository of integrated data from one or more disparate sources. A data warehouse is used for reporting and analysis purposes, such as business intelligence (BI), decision support systems (DSS), data mining, etc. A data warehouse enables users to access historical, current, and consistent data across the organization.


The main components of a data warehouse are:



  • Data sources: These are the original systems or applications that generate or store the operational data, such as ERP systems, CRM systems, web logs, etc.



  • Data extraction: This is the process of extracting the relevant data from the data sources using various methods, such as full extraction, incremental extraction, delta extraction, etc.



  • Data transformation: This is the process of transforming the extracted data into a consistent format that is suitable for loading into the data warehouse. This may include cleansing, filtering, aggregating, sorting, joining, splitting, etc.



  • Data loading: This is the process of loading the transformed data into the data warehouse using various methods, such as bulk loading, batch loading, real-time loading, etc.



  • Data warehouse: This is the database that stores the transformed and loaded data in a structured way that supports efficient querying and analysis. The data warehouse may use different architectures and schemas to organize the data, such as relational model, dimensional model, snowflake schema, star schema, etc.



  • Data marts: These are subsets of the data warehouse that are tailored for specific business units or functions. Data marts may use different levels of granularity or aggregation to suit the needs of the users.



Data Warehouse Design




Designing a data warehouse is a complex and iterative process that involves understanding the business requirements, analyzing the data sources, defining the data model, choosing the data warehouse architecture, and designing the ETL process. Some of the steps involved in designing a data warehouse are:



  • Identify the business objectives and scope of the data warehouse. This includes defining the key performance indicators (KPIs), metrics, dimensions, facts, and measures that the data warehouse should support.



  • Analyze the data sources and assess their quality, availability, and compatibility. This includes identifying the data entities, attributes, relationships, and constraints that exist in the data sources.



  • Define the data model for the data warehouse. This includes choosing between a relational model or a dimensional model, and selecting a schema design, such as star schema or snowflake schema. A relational model uses normalized tables to store the data, while a dimensional model uses denormalized tables that consist of facts and dimensions. A star schema has a single fact table that references multiple dimension tables, while a snowflake schema has multiple levels of dimension tables that are normalized.



  • Choose the data warehouse architecture that best suits the business needs and technical constraints. This includes deciding between a single-tier, two-tier, or three-tier architecture, and selecting a suitable platform, such as SQL Server 2012. A single-tier architecture has only one layer of data storage and processing, while a two-tier architecture has a separate layer for data staging and transformation. A three-tier architecture has an additional layer for data presentation and access.



  • Design the ETL process that will extract, transform, and load the data from the data sources to the data warehouse. This includes defining the ETL logic, workflow, schedule, frequency, and error handling mechanisms.



Some of the best practices for data warehouse design are:



  • Use a top-down approach to design the data warehouse based on the business objectives and user requirements.



  • Use a bottom-up approach to implement the data warehouse based on the available data sources and technical resources.



  • Use a hybrid approach to combine the top-down and bottom-up approaches to balance between business needs and technical feasibility.



  • Use a dimensional model to facilitate easy and fast querying and analysis of the data.



  • Use a star schema to simplify the data structure and reduce the number of joins.



  • Use surrogate keys to identify the rows in the fact and dimension tables.



  • Use conformed dimensions to ensure consistency and integration across different data marts.



  • Use slowly changing dimensions to handle changes in dimension attributes over time.



  • Use partitioning to improve the performance and manageability of large fact tables.



  • Use compression to reduce the storage space and increase the query speed of fact tables.



Data Warehouse Implementation




Implementing a data warehouse involves creating and managing the data warehouse database and tables using SQL Server 20