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Database Structure
About Database Fleet Management
About Database Fleet Maintenance
Database Fleet Maintenance allows administrators to maintain sets of Oracle Homes and
associated databases by applying database updates that include interim one-off patches
including quarterly security patch updates (SPUs/CPUs), Patchset Updates (PSUs), Release
Updates (RUs) and Monthly Recommended Patches (MRPs).
Any new database patches and updates are made available in the form of images. A Gold
image represents the software binary that is patched to the latest required level. Each newly
created image from a new patch for a specific database version is a new version.
For example, a user may create an Image with the first version containing 19.18RU. Later,
they may create a new version containing 19.18RU+MRP1. Then they can create a new
version containing 19.19RU and so on. Once they create a version and they decide to roll it
, they should mark it as Current. This version is now the gold image for the databases
subscribed to this target.
There are three main variations of patching:
Patch a specific (non-container) Oracle Database.
Patch a Container Database (CDB) and it's Pluggable Databases (PDB) together.
Patch PDBs independently by relocating them to a new CDB running from a patched
Oracle home.
Database fleet maintenance can be used for:
Single instance, RAC, and RAC One Node databases.
Note:Support is limited to: Single instance database on Windows, Standby databases
(Single instance and RAC), Grid Infrastructure for Clusters, Oracle Restart
(Grid Infrastructure for Standalone Server), and associated databases.
RAC Oracle Homes and associated databases.
Grid Infrastructure Homes and associated Grid Instances.
Oracle Restart Homes (Grid Infrastructure for Standalone Server) and associated databases.
Standby databases (Single Instance and RAC).
RAC One Node.
Fleet maintenance involves the following steps:
Figure 17-2 Fleet Maintenance Roles
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SSID: JEIMAH-2.4GHz@unifi
Protocol: Wi-Fi 4 (802.11n)
Security type: WPA2-Personal
Manufacturer: Intel Corporation
Description: Intel(R) Wi-Fi 6 AX201 160MHz
Driver version: 22.220.0.4
Network band: 2.4 GHz
Network channel: 11
Link speed (Receive/Transmit): 300/300 (Mbps)
IPv6 address: 2001:e68:541f:3b9:c8f7:1234:9fe1:7fbb
Link-local IPv6 address: fe80::5022:b8ce:864d:a2ad%16
IPv6 DNS servers: 2001:e68::b:68 (Unencrypted)
2001:e68::b:69 (Unencrypted)
IPv4 address: 192.168.0.106
IPv4 DNS servers: 192.168.0.1 (Unencrypted)
Physical address (MAC): 20-1E-88-EF-E0-25
Pipeline Schema
6 Types of Database Schemas
Flat model: A flat model database schema organizes data in a single, two-dimensional
display—think of a Microsoft Excel spreadsheet or a
CSV file. This schema is best for simple tables and databases without complex relationships
between different entities.
Hierarchical model: Database schemas in a hierarchical model have a “tree-like” structure,
with child nodes branching out from a root data node.
This schema is ideal for storing nested data—for example, family trees or biological taxonomies.
Network model: The network model, like the hierarchical model, treats data as nodes connected
to one other; however, it allows
for more complex connections, such as many-to-many relationships and cycles. This schema
can model the movement of goods
and materials between locations or the workflows required to accomplish a particular task.
Relational model: As discussed above, this model organizes data in a series of tables, rows,
and columns, creating relationships between different
entities. The next section and the rest of this guide will focus on the relational model.
Star schema: The star schema is an evolution of the relational model that organizes data into
facts and dimensions. Fact data is numerical (for example,
the number of sales of a product), while dimensional data is descriptive (for example,
a product’s price, color, weight, etc.).
Snowflake schema: The snowflake schema is a further abstraction on top of the star schema.
It contains a fact table that connects to a dimensional table,
expanding the descriptiveness possible within a database. As you might have guessed,
the snowflake schema gets its name from the intricate patterns of a
snowflake, where smaller structures radiate off of the central arms of the flake.
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