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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 2012 tools and features. Some of the tasks involved in implementing a data warehouse are:



  • Create and configure a SQL Server 2012 database for the data warehouse using SQL Server Management Studio (SSMS) or Transact-SQL (T-SQL). This includes setting up the database name, size, recovery model, collation, compatibility level, etc.



  • Create and populate the fact and dimension tables for the data warehouse using SSMS or T-SQL. This includes defining the table name, columns, data types, constraints, indexes, etc.



  • Implement partitions on large fact tables to divide them into smaller subsets based on a partitioning column or function. This improves the performance and manageability of fact tables by allowing parallel processing, incremental loading, partition switching, etc.



  • Implement indexes on fact and dimension tables to speed up query execution by creating sorted structures that point to table rows. This includes choosing between clustered or nonclustered indexes, columnstore or rowstore indexes, filtered or unfiltered indexes, etc.



  • Implement compression on fact tables to reduce their storage space and increase their query speed by applying algorithms that remove redundancy or store data more efficiently. This includes choosing between row or page compression, columnstore or rowstore compression, etc.



  • Implement statistics on fact and dimension tables to provide information about their distribution and cardinality to the query optimizer. This helps the query optimizer choose the best execution plan for queries. Statistics can be created and updated automatically or manually using SSMS or T-SQL.



  • Implement security on the data warehouse database and tables to protect them from unauthorized access or modification. This includes creating and assigning logins, users, roles, permissions, etc. using SSMS or T-SQL.



  • Implement auditing on the data warehouse database and tables to track and record the events and actions that occur on them. This helps to monitor and troubleshoot the data warehouse activities and ensure compliance with regulations. Auditing can be implemented using SQL Server Audit feature or triggers.



  • Implement backup and restore strategies for the data warehouse database and tables to ensure their availability and recoverability in case of failure or disaster. This includes choosing between full, differential, or transaction log backups, simple or full recovery model, backup compression, encryption, etc.



  • Implement high availability solutions for the data warehouse database and tables to minimize downtime and data loss in case of failure or disaster. This includes choosing between failover clustering, database mirroring, log shipping, AlwaysOn availability groups, etc.



Extracting and Transforming Data




ETL Concepts




ETL stands for extract, transform, and load. It is the process of moving data from one or more data sources to a data warehouse or a data mart. ETL is one of the most critical and complex aspects of a data warehouse project. ETL involves the following components:



  • 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 sources can be structured or unstructured, relational or non-relational, internal or external, 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 extraction can be done using various tools and techniques, such as SQL queries, flat files, web services, APIs, 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 transformation can be done using various tools and techniques, such as SQL Server Integration Services (SSIS), T-SQL scripts, stored procedures, functions, 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 loading can be done using various tools and techniques, such as SSIS destinations, T-SQL statements, bulk insert commands, 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.



The benefits of ETL are:



  • It improves the quality and consistency of the data by applying various transformations and validations.



  • It reduces the complexity and redundancy of the data by integrating and consolidating data from multiple sources.



  • It enhances the performance and scalability of the data warehouse by optimizing and parallelizing the data movement and processing.



  • It enables the delivery and analysis of timely and accurate data for business intelligence purposes.



ETL Design




parameters, and configurations using SQL Server Integration Services (SSIS). SSIS is a powerful and flexible tool that allows you to create and manage ETL packages that can perform various tasks and transformations on data. Some of the steps involved in designing an ETL solution using SSIS are:


  • Create an SSIS project using Visual Studio or SQL Server Data Tools (SSDT). This is where you can create, edit, debug, and deploy your SSIS packages.



  • Choose an SSIS project template that suits your ETL scenario. This can be a blank project, an import/export wizard project, a basic package project, or a custom project.



  • Define the SSIS project parameters that can be used to pass values to your SSIS packages at runtime. These can be project-level parameters or package-level parameters.



  • Define the SSIS variables and expressions that can be used to store and manipulate values within your SSIS packages. These can be system variables or user-defined variables.



  • Design the SSIS control flow that defines the workflow and logic of your ETL process. This includes adding and configuring control flow tasks, containers, precedence constraints, and event handlers.



  • Design the SSIS data flow that defines the data movement and transformation of your ETL process. This includes adding and configuring data flow sources, transformations, and destinations.



  • Configure the SSIS package logging, debugging, and error handling features that can help you monitor and troubleshoot your ETL process. This includes choosing a logging provider, setting breakpoints, using data viewers, redirecting errors and warnings, etc.



  • Choose an SSIS deployment model that determines how you deploy and execute your SSIS packages. This can be a project deployment model or a package deployment model.



  • Use SSIS environments, parameters, and project connections to configure your SSIS packages for different execution environments, such as development, testing, or production.



  • Use SSIS package execution options to specify how you run your SSIS packages. This can be done using SQL Server Agent jobs, command-line utilities, PowerShell scripts, or SSIS catalog stored procedures.



ETL Implementation




Implementing an ETL solution involves executing and managing the ETL process using SQL Server Integration Services (SSIS) tools and features. Some of the tasks involved in implementing an ETL solution using SSIS are:


  • Implement data extraction using SSIS data flow sources that can connect to various types of data sources and extract data from them. These include OLE DB source, flat file source, Excel source, XML source, etc.



  • Implement data transformation using SSIS data flow transformations that can perform various operations on data as it flows through the data pipeline. These include aggregate transformation, lookup transformation, merge join transformation, derived column transformation, etc.



  • Implement data loading using SSIS data flow destinations that can load data into various types of data destinations. These include OLE DB destination, SQL Server destination, flat file destination, Excel destination, etc.



  • Implement data cleansing using SSIS fuzzy lookup transformation that can match input data with reference data based on fuzzy logic and similarity scores. This helps to identify and correct misspelled or inaccurate data.



  • Implement data quality using SSIS data quality services (DQS) that can perform various tasks to improve the quality of data. These include profiling data, cleansing data, matching data, enriching data, etc.



  • Implement incremental data loading using SSIS change data capture (CDC) feature that can capture and track changes in the source data over time. This helps to load only the changed or new data into the data warehouse instead of loading all the data every time.



  • Implement slowly changing dimension (SCD) loading using SSIS slowly changing dimension transformation that can handle changes in dimension attributes over time. This helps to preserve the historical values of dimension attributes in the data warehouse.



  • Implement checkpoint loading using SSIS checkpoint feature that can restart an interrupted package execution from the point of failure instead of starting from the beginning. This helps to save time and resources in case of failure or error.



  • Implement parallel loading using SSIS balanced data distributor transformation that can split a single input into multiple outputs that can be processed in parallel by different transformations or destinations. This helps to improve the performance and scalability of the ETL process.



  • Implement partition switching loading using SSIS partition switching technique that can switch partitions between tables without physically moving any data. This helps to load data into partitioned tables quickly and efficiently.



Loading Data




Data Loading Concepts




Data loading is the process of loading the transformed data into the data warehouse or data marts. Data loading can be done using various techniques and scenarios, depending on the type, volume, and frequency of the data. Some of the data loading concepts are:



  • Full load: This is the technique of loading all the data from the source to the destination at once. This is usually done when the data warehouse is first created or when the source data is small or static.



  • Incremental load: This is the technique of loading only the new or changed data from the source to the destination since the last load. This is usually done when the source data is large or dynamic and when the data warehouse needs to be updated frequently.



  • Delta load: This is the technique of loading only the difference between the source and destination data. This is usually done when the source and destination data are synchronized and when the data warehouse needs to be updated in real-time.



Full load vs incremental load vs delta load: The advantages and disadvantages of these techniques are:



Technique


Advantages


Disadvantages


Full load


Simple and easy to implementNo need to track changes in source dataNo risk of data inconsistency or duplication


Slow and resource-intensi


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