* Desktop: Project Design
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Created by:
jontbrooks on December 13, 2010
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57 terms
Terms | Definitions |
|---|---|
 The ____ is the repository that stores all of the project information. MSTR apps use it to translate user requests into SQL queries and then translate the results of the queries back into MSTR objects like reports and documents. | Metadata |
| True or False: Any time an object is created, modified, or removed the changes are stored in the metadata repository. | TRUE |
| True or False: Project definitions, parameters, and schema objects become the foundation for all other types of objects created in a project. | TRUE |
| True or False: Project definitions, parameters, and schema objects become the foundation for all other types of objects created in a project. | TRUE |
| Name the 4 basic Schema objects that form the foundation of a MSTR project. | a) Table (logical objects that correspond to physical tables stored in the DW) b) Facts (logical objects that relate to aggregatable data stored in the DW. Usually numeric) c) Attributes (logical objects that relate descriptive data in the DW to the MSTR reporting environment - provide the context for reporting, and define level of details at which to analyze facts) d) Hierarchies (logical objects that represent relationships for Attributes - facilitates drilling and/or browsing paths) |
| MSTR Architect plays an important role in support 3 project functions - name them. | Reporting, Drilling, Browsing |
| True or False: Basic schema objects that form the basis of a MSTR project can be created in Architect. | TRUE |
| True or False: Attributes determine the level of detail at which Metrics are reported. | TRUE |
| Relationships between Attributes and Hierarchies ad their drilling configuration is defined in which MSTR Desktop component? | Architect |
| Hierarchies are used to determine browsing paths for Attributes. What are the 2 kinds of Hierarchies? | User (aka "custom groupings" defined by the architect) System (defined by the MSTR logic) |
| Describe Fact tables | Store fact data and Attribute ID columns that desc the level at which the fact values a recorded; Atts in Fact table comprise the fact table lvl |
| 2 types of Fact tables | Base (table stores a fact or set of facts at the lowest level of detail); Aggregate (stores a fact or set of facts at a higher, summarized lvl)- these can reduce query time; can have multiple agg tables for the same fact/set of facts |
| The Project Design process is characterized by 4 primary steps. DLDM, DDWS, CPMA, MtPS | Design the Logical Data Model; Design the Data Warehouse Schema, Create the Project with MSTR Architect, Manage the Project Source |
| Designing the Logical Data Model consists of what primary tasks? | 1) Determine info users want to see in reports 2) Determine what info is available to include in reports 3) Design a logical model that incorporates the info users want to see and shows how it's related |
| Designing the Data Warehouse Schema consists of what primary tasks? | 1) Consider advantages/disadvantages of various structures for storing data in the DW 2) Determine optimal schema design that balances user and performance requirements, and maintenance overhead 3) Create the DW using the design or modify the DW to use the schema design |
| Creating the Project in MSTR Architect consists of what primary tasks? | 1) Configure project metadata if necessary 2) Configure connectivity to project metadata and DW if necessary 3) Create the project object in MSTR Architect 4) Create necessary schema objects for the project based on logical model, mapping them to appropriate structures in the DW schema |
| Managing the Project Schema consists of what primary tasks? | 1) Creating new Schema objects as needed 2) Modifying definitions of Schema objects as needed 3) Removing Schema objects as appropriate 4) Creating advanced Schema objects for more complex functions |
| A ____ data ____ is a diagram that shows the information you want to analyze in a project and how that information is related. It does NOT show the physical structure of how the info is stored in the DW. | logical, model |
| True or False: The Logical Data Model should include any info users want to see on reports. | TRUE |
| True or False: The schema objects created (via Architect) serve as a link between the logical and physical structures of the data. The schema objects can then be used to create application objects. | TRUE |
| A Logical Data Model includes 3 components. Name them. | Facts, Hierarchies, Attributes |
| True or False: Facts are measures used to analyze business performance. | TRUE |
| In the Data Warehouse, ___ exist as columns in the ___ tables. | Facts, Fact |
| ___ can come from different source systems and stored at different levels of detail. | Facts |
| ___ are descriptive data that provide context for analyzing facts. | Attributes |
| In the DW, ___ exist as columns in Lookup, Relationship, and Fact tables. | Attributes |
| Attribute relationships are the key to joining data from different attributes and aggregating fact data to different levels. What are the 2 different kinds of relationships? | Direct and Indirect |
| What kind of relationship is this: a parent-child relationship exists between two or more attributes. In the DW, these relationships are explicitly defined using Lookup tables. | Direct |
| What kind of relationship is this: two or more attributes are related only through a fact or set of facts. In the DW, these relationships are stored only in fact tables. | Indirect |
| What are the 3 different Direct relationship types? | 1 to 1; 1 to many; many to many |
| In a logical data model ____ are also sometimes referred to as dimensions. | hierarchies |
| True or False: Hierarchies only contain Attributes that are directly related to each other. Attributes in one hierarchy are indirectly related to Attributes in other hierarchies. | TRUE |
| User reporting requirements, existing source data, & technical and performance considerations are factgors to consider when creating a ___ ___ model. | Logical Data |
| 2 Primary Components of a Physical Schema | Tables and Columns |
| Characteristics of simple keys with regards to mapping and degree of complexity | Maps to one column - less complex |
| Relationship tables | Resolve many to many relationships |
| Characteristics of compound keys with regards to mapping and degree of complexity | Maps to 2 or more columns - more complex and SQL-intensive |
| True or False: the MSTR Metadata Repository has 23 predefined tables | TRUE |
| True or False: it is possible for the same Fact to be stored at different Attribute levels within a Hierarchy | TRUE |
| A ___ ___ is a detailed, graphical representation of the physical structure of a database. | Physical Schema |
| True or False: You can create various physical schema designs from a single logical model, depending on how you want to store the data representing logical objects in the DW. | TRUE |
| True or False: the Physical Schema shows you how the underlying data for Facts and Attributes are stored in the DW | TRUE |
| What are the 3 different kinds of Column types with regards to a Physical Model? | ID, Description (store the text description of Attributes), Fact |
| True or False: Lookup tables can only store information for one Attribute | FALSE |
| How does Data Volume contribute to deciding whether or not to denormalize warehouse tables? | Atts & hierarchies with greater volume are better candidates for denormalization, while using more normalized design for lower data volumes |
| What are the concerns with denormalizing tables with regards to database maintenance? | With more denormalization comes greater maintenance (more tables, more aggregation, more ETL) |
| Describe how Normalized vs Denormalized schemas differ with regards to storing data. | Normalization occurs when you have a schema design that does not store data redundantly; Denormalization does store data redundantly (best for OLAP; analyses) |
| What is the primary benefit of denormalizing tables in a data warehouse environment? | It typically yields better query performance |
| What are some disadvantages of denormalization? | More maintenance; more storage |
| ___ ___ store information about the relationship between 2 or more attributes. They enable you to join data for related Attributes. | Relationship tables |
| True or False: to map a relationship between 2 or more Attributes, their respective IDs do not need to exist together in a relationship table. | FALSE |
| True or False: when working with 2 Attributes that share a many-to-many Direct Relationship, you must create a separate relationship table to map the parent-child relationship | TRUE |
| ____ fact tables are tables that store a Fact or set of Facts at the lowest possible level of detail. | Base |
| ____ fact tables are tables that store a Fact or set of Facts at the lowest possible level of detail. | Aggregate |
| What are some benefits in keeping higher-level Attribute Lookup tables? | More efficiently browse higher-level Attributes; Take advantage of Aggregate Fact tables |
| What are 3 most commonly used schema designs? | Completely normalized schema, Moderately denormalized schema, Completely denormalized schema |
| What are the factors to consider when creating a DW schema? | User reporting requirements; query performance; data volume; db maintenance |
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