Exam 70-463: Implementing a Data Warehouse with Microsoft SQL Server

Skills Measured:

Design and implement a data warehouse (10–15%)

• Design and implement dimensions
  • Design shared/conformed dimensions; determine if you need support for slowly changing dimensions; determine attributes; design hierarchies; determine whether you need star or snowflake schema; determine the granularity of relationship with fact tables; determine the need for auditing or lineage; determine keys (business transactional or your own data warehouse/surrogate keys); implement dimensions; implement data lineage of a dimension table
• Design and implement fact tables
  • Design a data warehouse that supports many to many relationships; appropriately index a fact table; using columnstore indexes; partitioning; additive measures; semi additive measures; non additive measures; implement fact tables; determining the loading method for the fact tables; implement data lineage of a fact table; design summary aggregation tables

Extract and transform data (20–25%)

• Define connection managers
  • Plan the configuration of connection managers; package level or project level connection manager; define a connection string; parameterization of connection strings
• Design data flow
  • Define data sources and destinations; distinguish blocking and non-blocking transformations; use different methods to pull out changed data from data sources; determine appropriate data flow components; determine the need for supporting Slowly Changing Dimensions (SCD); determine whether to use SQL Joins or SSIS lookup or merge join transformations; batch processing versus row by row processing; determine the appropriate transform to use for a specific task; determine the need and method for identity mapping and deduplicating; fuzzy lookup, fuzzy grouping and Data Quality Services (DQS) transformation; determine the need for custom data sources, destinations, and transforms; determine what to do with erroneous rows; determine auditing needs; trusted/authoritative data sources, including warehouse metadata; extracting only changed rows
• Implement data flow
  • Debug data flow; use the appropriate data flow components; SQL / SSIS data transformation; create SSIS packages that support slowly changing dimensions; use the lookup task in SSIS; map identities using SSIS fuzzy lookup (advanced); specify a data source and destination; use data flows; different categories of transformations; read, transform and load data; understand which transforms to use to accomplish a specific business task; data correction transformation; performance tune an SSIS dataflow; optimize Integration Services packages for speed of execution; maintain data integrity, including good data flow
• Manage SSIS package execution
  • Schedule package execution by using SQL Server Agent; execute packages by using DTEXEC; execute packages by using SQL Server Management Studio; implement package execution; plan and design package execution strategy; use PowerShell to execute script; monitor the execution using Management Studio; use DTEXECUI; ETL restartability
• Implement script tasks in SSIS
  • Determine if it is appropriate to use a script task; extending the capability of a control flow; perform a custom action as needed (not on every row) during a control flow

Load data (25–30%)

• Design control flow
  • Determine control flow; determine containers and tasks needed; determine precedence constraints; design an SSIS package strategy with rollback, staging and transaction control; decide between one package or multiple packages; determine event handlers; determine variables; determine parameters on package and project level; determine connection managers and whether they are package or project level; determine the need for custom tasks; determine how much information you need to log from a package; determine the need for checkpoints; determine security needs
• Implement package logic by using SSIS variables and parameters
  • User variables; variable scope, data type; implement parameterization of properties using variables; using variables in precedence constraints; referring to SSIS system variables; design dynamic SSIS packages; package configurations (file or SQL tables); expressions; package and project parameters; project level connection managers; variables; implement dynamic package behavior; configure packages in SSIS for different environments, package configurations (xmlconfiguration file, SQLServer table, registry entry; parent package variables, environment variable); parameters (package and project level); project connection managers; property expressions (use expressions for connection managers)
• Implement control flow
  • Checkpoints; debug control flow; implement the appropriate control flow task to solve a problem; data profiling; use sequence containers and loop containers; manage transactions in SSIS packages; managing parallelism; using precedence constraint to control task execution sequence; creating package templates; using the execute package task
• Implement data load options
  • Implement a full and incremental data load strategy; plan for an incremental update of the relational Data Mart; plan for loads into indexed tables; configure appropriate bulk load options; select an appropriate load technique (SSIS Destination versus T-SQL) and load partitioned tables
• Implement script components in SSIS
  • Create an SSIS package that handles SCD Type 2 changes without using the SCD component; work with script component in SSIS; deciding when it is appropriate to use a script component versus a built in; source, transformation, destination component; use cases: web service source and destination, getting the error message

Configure and deploy SSIS solutions (20–25%)

• Troubleshoot data integration issues
  • Performance issues; connectivity issues; execution of a task or transformation failed; logic issues; demonstrate awareness of the new SSIS logging infrastructure; troubleshoot a failed package execution to determine the root cause of failure; troubleshoot SSIS package failure from an invalid datatype; implement break points; data viewers; profile data with different tools; batch cleanup
• Install and maintain SSIS components
  • Software installation (IS, management tools); development box and server; install specifics for remote package execution; planning for installation (32- versus 64-bit); upgrade; provisioning the accounts; creating the catalog
• Implement auditing, logging, and event handling
  • Audit package execution by using system variables; propagate events; use log providers; log an SSIS execution; create alerting and notification mechanisms; use Event Handlers in SSIS to track ETL events and errors; implement custom logging
• Deploy SSIS solutions
  • Create and configure an SSIS catalog; deploy SSIS packages by using the deployment utility; deploy SSIS packages to SQL or file system locations; validate deployed packages; deploy packages on multiple servers; how to install custom components and tasks; deploy SSIS packages by using DTUTIL
• Configure SSIS security settings
  • SSIS catalog database roles; package protection levels; secure Integration Services packages that are deployed at the file system; secure Integration Services parameters, configuration

Build data quality solutions (15–20%)

• Install and maintain data quality services
  • Installation prerequisites; .msi package; adding users to the DQ roles; identity analysis, including data governance
• Implement master data management solutions
  • Install Master Data Services (MDS); implement MDS; create models, entities, hierarchies, collections, attributes; define security roles; import/export; subscriptions
• Create a data quality project to clean data
  • Profile Online Transaction Processing (OLTP) and other source systems; data quality knowledge base management; create data quality project; use data quality client; improve data quality; identity mapping and deduplicating; handle history and data quality; manage data quality/cleansing