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Data Manager

overview

IBM Cognos Data Manager Build Data Marts with Enterprise Data (V10.1) is a five-day, instructor-led course that teaches participants how to move, merge, consolidate, and transform data from a range of data sources to build and maintain subject-area data marts.
prerequisties
  • Experience using basic Windows functionality
  • Knowledge of database concepts
  • Knowledge of dimensional analysis concepts
  • Working knowledge of SQL
Duration
Online
  • It is a 16 days program and extends up to 2hrs each.
  • The format is 40% theory, 60% Hands-on.

Corporate
  • It is a 4 days program and extends up to 8hrs each.
  • The format is 40% theory, 60% Hands-on.
Classroom
    Private Classroom arranged on request and minimum attendies for batch is 4.
course content
  • Getting Started
    • Identify the purpose of IBM Cognos Data Manager
    • Define data warehousing and its key underlying concepts
    • Identify how Data Manager creates data warehouses
    • Examine the Data Manager architecture and user interface
  • Create a Catalog
    • Examine the purpose and contents of Data Manager catalogs
    • Create a catalog
    • Define connections to source and target data
    • Access data using SQLTerm
    • Configure flat data source files using SQLTXT
  • Create Hierarchies
    • Examine the role of the dimensional framework in Data Manager
    • Examine hierarchies and their data sources
    • Identify how to create hierarchies from the columns of one table, the rows of one table, and from multiple tables
    • Test and view hierarchies
    • Create a hierarchy of static date values
    • Handle weeks in a date hierarchy
  • Create Basic Builds
    • Examine Data Manager builds and build-related terminology
    • Create a dimension build using the Dimension Build wizard
    • Create a fact build using the Fact Build wizard
    • Test and execute a fact build
    • Document a catalog
    • Create catalog schema
  • Create Derivations
    • Examine derivations
    • Apply operators and functions to derivations
    • Examine the derivation timing model
    • Add derivations to a fact build
  • Create Conformed Dimensions
    • Examine conformed dimensions and their advantages
    • Design conformed dimensions
    • Create conformed dimensions
    • Create data integrity lookups that use conformed dimensions
  • Customize Reference Structures
    • Create hierarchies manually using different approaches
    • Examine the features of a hierarchy
    • Examine literals
    • Set data access for hierarchy levels
    • Examine static and dynamic members
    • Examine fostering
    • Use derivations in a hierarchy
  • Process Dimensional History and Late Arriving Facts
    • Examine slowly changing dimensions (SCDs)
    • Use surrogate keys in SCDs
    • Manage type 1 and type 2 changes to dimensional data
    • Load historical data for a dimension
    • Examine late arriving facts
    • Process late arriving facts in a fact build
  • Transform Data Using Lookups and Derived Dimensions
    • Identify when to use lookups
    • Identify the requirements for a lookup
    • Create a translation lookup
    • Create an optional lookup
    • Add derived dimensions to fact builds
  • Customize Data Delivery
    • Configure fact and dimension delivery modules
    • Create indexes on fact and dimension tables
    • Update fact data using keys
  • Customize Fact Data Processing
    • Filter fact data
    • Merge duplicate fact data
    • Examine fact data integrity checking
    • Reject fact data
  • Aggregate, Filter, and Partition Fact Data
    • Aggregate fact data
    • Examine aggregate rules
    • Vertically restrict fact data
    • Horizontally restrict fact data
    • Partition fact data
  • Implement Job Control
    • Examine where job control fits into the data warehouse lifecycle
    • Create a JobStream
    • Add, link, and reposition nodes
    • Execute a JobStream and view the results
  • Automate Functionality Using Commands
    • Differentiate between the Command Line Interface (CLI) and Data Manager Designer
    • Identify common commands
    • Use commands in a batch file
    • Examine variables
  • Customize Functionality with User-Defined Functions and Variables
    • Examine user defined functions (UDFs)
    • Create an internal UDF
    • Create a user-defined variable
  • Process Unbalanced Hierarchical Data
    • Examine balanced, unbalanced, and ragged hierarchies
    • Add a recursive level to a hierarchy
    • Identify ways to balance a hierarchy and delivered flattened data
    • Examine circular references
  • Pivot Fact Data
    • Examine pivoting
    • Use the single pivot technique
    • Use the advanced pivot technique
    • Examine reverse pivoting
  • Resolve Data Quality Issues
    • Identify data quality and cleansing issues
    • Handle fostered and unmatched members
    • Perform debugging using SQLTerm and functions
    • Assess the quality of output data
  • Troubleshoot and Tune the Data Manager Environment
    • Use build logging to ensure that data marts are being loaded properly
    • Perform dimension breaking
    • Manage memory and resources
    • Export DDL statements
  • Organize and Package Data Manager Components
    • Export and import components using packages
    • Search for components in a catalog using Navigator
  • Integrate with IBM Cognos BI
    • Examine IBM Cognos BI
    • Identify the role of metadata dimensions, metadata collections, and metadata stars
    • Export Data Manager metadata to XML
    • Import Data Manager XML into Framework Manager
    • Use Data Manager metadata with IBM Cognos BI
    • Publish a data movement task to IBM Cognos Connection
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