Microsoft SQL Server 2012 Implementing a Data Warehouse: Enhancing Packages
Data Warehouse Developers and Database Administrators who create and manage business intelligence solution as part of their daily tasks, which include implementing data warehouse databases, extracting, transforming and loading data as part of an ETL solution, and data cleansing.
SSIS in SQL Server 2012 allows for dynamic packages and to enable values within the package to be manually set depending on your requirements at execution time. SQL Server 2012 allows for the use of parameters, variables, and expressions to create dynamic packages. In SQL Server 2012 you can also perform data mining and text mining to find patterns and rules within the data being analysed. This course discusses and shows how to use parameters, variables, expressions, and event handlers to create dynamic SSIS packages. It also discusses and shows how to use the Data Mining Query, Term Extraction, Term Lookup, and Percentage Sampling transformations. This course is one of a series in the SkillSoft learning path that covers the objectives for the Microsoft SQL Server 2012 exam 70-463:Implementing a Data Warehouse with Microsoft SQL Server 2012. This exam is one of the requirements for the MCSA: SQL Server 2012, MCSE: Data Platform, and MCSE: Business Intelligence certification paths.
Using Parameters to enhance SSIS packages 2012
- recognize how to create dynamic SSIS packages using parameters, variables, and expressions, and explain what event handlers can be used for
- recognize how to implement dynamic SSIS packages using parameters, variables, expressions, and event handlers
Data Mining Techniques
- recognize how to use a Data Mining prediction model in an SSIS package using the Data Mining Query Transformation
- differentiate between the Term Extraction Transformation and Term Lookup Transformation in SSIS packages
- recognize how to use the term extraction and term lookup transformations within an SSIS package in a given scenario
- create SSIS packages using parameters, variables, and expressions, and sample text mining and data mining for predictions