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Big data integration tools .pdf

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Understanding the Data Integration Techniques
The various techniques of data integration differ 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 means
and how can it help in providing ​solutions​ for different businesses.
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 understand and further retain their customers. The bar of
in-depth, knowledge is raised by utilizing data integration. It supports collaboration between various
sources, 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 different tools
and techniques as per your business requirement.
Here is the list of various ​big data integration tools​ ​and techniques you need to know.
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 data.


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.
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​.
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.
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.
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 aforementioned 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.


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