Database Architecture

Centralized database system (CDB)

• Data is stored in one location, this location is most often a central computer or database system.
• System may contain several processors
• Example: Cloud computing/Data farms/Data centres

Pros:

• Data integrity is maximized and data redundancy is minimized. This aids in the maintaining of data as accurate and as consistent as possible and enhances data reliability.
• Data kept in the same location is easier to be changed, re-organized, mirrored, or analyzed.

Cons:

• Highly dependent on network connectivity, the slower the internet connection is , the longer the database access time will be
• Bottlenecks can occur as a result of high traffic
• No data redundancy makes it very hard to retrieve the data that is unexpectedly lost

Distributed database system (DBAs):

• Data is distributed across several nodes
• It may be stored in multiple computers, located in the same physical location; or may be dispersed over a network of interconnected computers.
• The nodes are connected by communication network
• System provides necessary concurrency, recovery and transaction processing

Pros:

• Increase the reliability and availability
• Increase the scalability
• Protection of valuable data
• Distributed query processing cam improved performance

Cons:

• DBAs may have to do extra work to ensure that the distributed mature of the system is transparent
• Difficult to maintain integrity, which require too much of the network's resources to be feasible

World Wide Web

• Data is stored in many locations
• Several owners of data and therefore no certainly of availability or consistency of data
• No standards have been developed except in case of XML/http and some protocols for accessing data in WWW
• Convenient to use
• Notions of transactions is much more difficult to enforce

P2P Database

• Data and Processing are shared among a group of computer systems which may be geographically separated
• Computer nodes can join and leave the network at will, thus need duplication of data for availability and reliability
• Reliability and security of such systems can be worse than grid database

Grid Computing

• Data and Processing are shared among a group of computer systems which may be geographically separated
• The grid database system is designed for particular purpose
• Administration of such systems are done locally by each owner of the system
• Reliability and security of such system are not well-developed or studied.

Cluster, Grid and Cloud Computing

• Cluster differs from Cloud and Grid in that a cluster is a group of computers connected by a local area network (LAN), whereas cloud and grid are more wide scale and can be geographically distributed. Another way to put it is to say that a cluster is tightly coupled, whereas a Grid or a cloud is loosely coupled.
• Clusters are made up of machines with similar hardware, whereas clouds and grids are made up of machines with possibly very different hardware configurations.
• Cloud services offered in several forms:
  • IaaS Infrastructure as a service(provide virtual machines)
  • PaaS Platform as a service(provide environment like Linux)
  • SaaS Software as a service (Specific application like RDB)
    the datacenter hardware and software is what we call a cloud
• Cloud computing is a centralized model whereas grid computing is a decentralized model where the computation could occur over many administrative domains.
• A grid is a collection of computers which is owned by multiple parties in multiple locations and connected together so that users can share the combined power of resources. Whereas a cloud is a collection of computers usually owned by a single party.

Examples of Clouds: Amazon Web Services (AWS), Google App Engine.

Examples of Grids: FutureGrid.

Examples of cloud computing services: Dropbox, Gmail, Facebook, Youtube, RapidShare