Understanding the Data Integration Techniques

Understanding the Data Integration Techniques:-

The various techniques of data integration
vary from each other and can be used as per the requirement of the business
owner. But before doing that we need to understand, what does data integration mean and how can it
help in providing solutions for
different business needs. 
Data integration involves a combination of
data which is accumulated to a centralized system from several sources. The
stored data in disparate sources is extracted using various technologies in
order to present it in a unified view. By data integration, it has everything
to do with processing the data by way of aligning, combining and presenting it
to the end-user.

Organizations today are increasingly utilizing
data integration tools for enterprise-wide data delivery, governance, and
analytics. It allows firms to better understand and further retain their
customers. The bar of in-depth, knowledge is raised by utilizing data
integration. It supports collaboration between various, maintains security and
compliance, and reduces the overall project timelines.

If you are just beginning to search for new
integration tools, it is essential that you have the knowledge of various
delivery techniques that a tool has to offer. This can make you easily swift
through various tools and techniques as per your business requirement.
Here is the list of various big
data integration tool
techniques you need to know before you lay your hands on an integration tool.
1. Virtual Integration/ Uniform Data Access
The main benefit of virtual integration is
that there is zero latency from the source system to the consolidated view for
the data updates. It basically leaves the data in a source system and defines a
set which can provide a unified view to various customers across a platform.
Under this technique, a separate store is not required for the consolidated
There are however some drawbacks as well,
which can make this technique not quite suitable for some business types. It
has a limited possibility for accessing the data history and version
management. This technique can be applied to some kind of database types which
means the excess load on the source system are not designed to accommodate
under the Uniform Data Access type.
2. Physical Data Integration/ Common Data Storage
As the name well suggests, Common Data Storage
is a system, which tends to copy the data from the source systems to a new
system. This way a unified collected data is stored and managed by those new
systems instead of the original source. More commonly called Data Warehouse;
this technique helps in data collection from various sources, combining them to
a central space and management (Database files, mainframes, and flat files).
The vast volumes, however, requires separate data integration systems.
3. Application Based Integration
This technique can generally be used for a
very limited number of applications. This approach requires a particular
application to utilize and implement all the integration efforts that are done
by a user.
4. Manual Integration or Common User Interface
This approach is predominantly used for
accessing all the information available on the internet. This does not present
a unified view of the data and accesses all the relevant information or data
from the source system or a web page.
5. Middleware Data Integration
This technique can be used in transferring the
data from particular applications into a totally new middleware layer. Now, the
integration logic is however not implemented into the applications, still there
is a need for the applications to participate in the data integration process.
By using the following techniques data can be
stored, analyzed and viewed in a unified way. However, using which one is the
question. As a business owner you must be well aware of your business type and
various processes, only then you can make use of the one that suits your
business function and data requirements.
Retrieving data from various sources is in
itself a challenging task. From a technical viewpoint, the first thing which
includes understanding the various sources from where the big data integration is done and is sourced is in itself a task.
Secondly, designing a common structure to store all the vast information for
future reference is again a challenge.
So, to make use of a technique, it is very
important for the person to understand the data assets of an enterprise and the
source systems. This would help the organization to look ahead and plan the
long-term data integration goals of the firm.

Author Bio-

Windsor Data Services, your one-stop destination for all you need to create a data-driven organization. Based in Z├╝rich and Singapore, Windsor Data Services is a modern, cloud age data integration and advertising firm whose mission is to empower firms with data management and analysis tools. Content With me on Twitter  and Facebook.

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