Databases Reference
In-Depth Information
allowed scalability of each layer, but the overall connectedness of the different layers limited
the performance of the overall system. This is predominantly the architecture of analytical and
business intelligence applications.
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n-
tier architecture
.
n
-tier or multitier architecture is where clients, middleware, applications,
and servers are isolated into tiers. By this architecture any tier can be scaled independent of the
others. Web applications use this type of architecture approach.
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Cluster architecture
. Refers to machines that are connected in a network architecture (software
or hardware) to closely work together to process data or compute requirements in parallel.
Each machine in a cluster is associated with a task that is processed locally and the result sets
are collected to a master server that returns it back to the user.
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Peer-to-peer architecture
. This is a type of architecture where there are no dedicated servers
and clients; instead, all the processing responsibilities are allocated among all machines,
known as peers. Each machine can perform the role of a client or server or just process data.
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Distributed processing has a lot of advantages and disadvantages.
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Advantages:
- Scalability of systems and resources can be achieved based on isolated needs.
- Processing and management of information can be architected based on desired unit of
operation.
- Parallel processing of data reducing time latencies.
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Disadvantages:
- Data redundancy
- Process redundancy
- Resource overhead
- Volumes
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The most popular distributed processing implementations in the data world are:
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Peer to peer
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Hub and spoke
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Federated
Processing data in either the centralized processing or distributed processing style has a lot of
infrastructure challenges that continue to dominate the space. The next section discusses the key areas
of challenge in terms of data processing.
Data processing infrastructure challenges
Basic data processing architecture (computational units) as shown in
Figure 3.2
has remained the
same from the days of punch card to modern computing architectures. The following sections outline
the four distinct areas that have evolved and yet prove challenging.
Storage
The first problem to manifest itself has been storage. With an increase in the volume of data, the
amount of storage needed to process and store it increases by 1.5 times. You need the additional 0.5
times storage for intermediate result set processing and storage.
