Data Warehousing 11g Essentials

Explanation:
In a data warehouse, a dimension is a data element that categorizes each item in a data set into non-overlapping regions. A data warehouse dimension provides the means to "slice and dice" data in a data warehouse. Dimensions provide structured labeling information to otherwise unordered numeric measures.
For example, "Customer", "Date", and "Product" are all dimensions that could be applied meaningfully to a sales receipt. A dimensional data element is similar to a categorical variable in statistics.
The primary function of dimensions is threefold: to provide filtering, grouping and labeling. For example, in a data warehouse where each person is categorized as having a gender of male, female or unknown, a user of the data warehouse would then be able to filter or categorize each presentation or report by either filtering based on the gender dimension or displaying results broken out by the gender.

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Explanation:
The Metadata Dependency Manager (MDM) enables you to plan your project by previewing the impact of the changes or future changes for "what-if" analysis. When you plan to introduce changes to your source systems, you can gauge the impact of that change on your warehouse design. If changes have already been introduced, then you can plan the time required to update your ETL design and rebuild your data warehouse.

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Explanation:
All metadata for cubes and dimensions is stored in the Oracle database.

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Explanation:
Your applications sometime send repetitive queries to the database. To improve the response time of repetitive queries, results of queries, query fragments, and PL/SQL functions can be cached in memory. A result cache stores the results of queries shared across all sessions. When these queries are executed repeatedly, the results are retrieved directly from the cache memory.

You must annotate a query or query fragment with a result cache hint to indicate that results are to be stored in the query result cache.
The query result set can be cached in the following ways:

* Server-side Cache
* Client Result Cache

Oracle Database 11g Release 1 (11.1) provides support for server-side Result Set caching for both JDBC types. The server-side result cache is used to cache the results of the current queries, query fragments, and PL/SQL functions in memory and then to use the cached results in future executions of the query, query fragment, or PL/SQL function. The cached results reside in the result cache memory portion of the SGA. A cached result is automatically invalidated whenever a database object used in its creation is successfully modified. The server-side caching can be of the following two types:
* SQL Query Result Cache (A)
* PL/SQL Function Result Cache (B)

Explanation:

Note:
Partition-wise joins reduce query response time by minimizing the amount of data exchanged among parallel execution servers when joins execute in parallel. This significantly reduces response time and improves the use of both CPU and memory resources. In Oracle Real Application Clusters (RAC) environments, partition-wise joins also avoid or at least limit the data traffic over the interconnect, which is the key to achieving good scalability for massive join operations. Partition-wise joins can be full or partial.

Oracle decides which type of join to use.

Explanation:
Partitioning enhances the performance, manageability, and availability of a wide variety of applications and helps reduce the total cost of ownership for storing large amounts of data. Partitioning allows tables, indexes, and index-organized tables to be subdivided into smaller pieces, enabling these database objects to be managed and accessed at a finer level of granularity. Oracle provides a rich variety of partitioning strategies and extensions to address every business requirement. Moreover, since it is entirely transparent, partitioning can be applied to almost any application without the need for potentially expensive and time consuming application changes.

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Explanation:
Oracle Advanced Security is an option to the Oracle Database 11g Enterprise Edition that helps address privacy and regulatory requirements including the Payment Card Industry Data Security Standard (PCI-DSS), Health Insurance Portability and Accountability Act (HIPAA), and numerous breach notification laws.
Oracle Advanced Security provides data encryption and strong authentication services to the Oracle database, safeguarding sensitive data against unauthorized access from the network and the operating system. It also protects against theft, loss, and improper decommissioning of storage media and database backups.

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Explanation:
Automatic Storage Management (ASM) is a feature provided by Oracle Corporation within the Oracle Database from release Oracle 10g (revision 1) onwards. ASM aims to simplify the management of database files. To do so, it provides tools to manage file systems and volumes directly inside the database, allowing database administrators (DBAs) to control volumes and disks with familiar SQL statements in standard Oracle environments. Thus DBAs do not need extra skills in specific file systems or volume managers (which usually operate at the level of the operating system).

With ASM:
* IO channels can take advantage of data striping and software mirroring
* DBAs can automate online redistribution of data, along with the addition and removal of disks/storage
* the system maintains redundant copies and provides 3rd-party[citation needed] RAID functionality
* Oracle supports third-party multipathing IO technologies (such as failover or load balancing to SAN access) the need for hot spares diminishes

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