For a lot of business users, the data integration architecture sounds
like an oxymoron. That's because they fail to take a possibility into account:
data integration may have its own architecture. The reason for this lack of
sight is that several data analysts still rely on practices of the 1990s that
have become obsolete and limited in capacity with time.
Truth is, currently, a multitude of data warehouse professionals or
integration specialists even now follows the practice of building one
independent interface at a time, a poor way to approach a data integration
project. And a huge misconception is that switching to a vendor product for
data integration automatically assures architecture.
Here's the catch: If companies fail to embrace the existence of data
integration architecture, they can't find out how architecture affects data
integration's various aspects: staffing, cost, scalability, and ability to aid
real-time, SOA, master data management, and interoperability with integration
solutions.
In this blog, you'll find why data integration needs architecture to
make it more independent, future-facing, productive, scalable, and
interoperable. Find why hub and spoke architecture is preferred for
most integration tools to boot.
Complexity: Reason Why Data Integration Needs an Architecture
In places where complex data integrations are implemented, the flow of
data from myriad source systems (including CRM, supply chain, ERP etc.) through
multiple data transformation steps is impacted. Both data sources and targets
are heterogeneous in nature. Plus, they have diverse data models. Then
particular interfaces uniformly connects these equally diverse pieces. And the
data doesn't flow continuously, so companies need data staging areas. These are
various reasons why firms need to organize a data integration solution using an
architecture.
Role of Data Integration Architecture
Data integration architecture allows companies impose order on the chaos
generated by complexity to accomplish certain goals:
Architectural patterns as development standards. Data integration
solutions include several components that can be categorized into three broad
categories: servers, interfaces, and data transformations. Keeping this in mind,
let's explore data integration architecture integration in detail.
Data integration architecture is a pattern that is constructed when
servers relate through interfaces.
Data integration architecture provides a holistic view of infrastructure
and implementations to bolster users by helping them wrap their heads around
these with ease.
Promotes reuse and consistency. As architectural
patterns and development standards are implemented in a plethora of data
integration projects, the end result is simplicity (as compared to ad hoc
methods), which enables the reuse of data integration artifacts (such as
interfaces, jobs, routines, and more) - this increases consistency in the handling
of data.
Harmony present within the common infrastructure and individual
tools. For a data flow or project to be organized in an
architecture, the particular infrastructure encompassing integration server and
interface must support that architecture.
Hub and Spoke Architecture Fits the Bill
Hub and spoke architecture is one of the best architectural patterns for
data integration. In the hub and spoke model, data transfer and inter-server
communication travel through a centralized hub. In this hub, communications are
managed and data transformations are performed. This allows it to be cognizant
of every transaction, every business activity, every data entry, and more. All
this data is compatible as the hub has a job to translate everything into a
canonical language. Hence, the hub not only serves as a facilitator of
transactions but also companies' window into your business.
Hub and spoke architecture offers a lot of benefits. Here are a
few.
- Enhanced Flexibility: Hub and spoke model includes a flexible architectural pattern that's
easy to comprehend and do business with. Not to mention, it can be expressed in
a multitude of variations despite its flexibility.
- Increased Reuse: Users typically foster an interface, preferably called spoke, from the
hub to a specific system. This interface can be reused for establishing
communications among other systems.
- Reduced Interfaces: The ability to reuse interfaces helps users decrease the number of
interfaces to be built or maintained. As a result, complexity decreases to a
minimum - and agility increases by leaps and bounds.
- Improved Communication: Hub and spoke architecture fosters streamlined communication within the
system, allowing users create more sales and ultimately revenue.
To conclude, hub and spoke architecture allows businesses handle
complexity in data integration, promote flexibility and reuse, and improve
communication across the business ecosystem for maximized outcomes.
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About the Author
Chandra Shekhar is a product marketing enthusiast who likes to talk about business integration and how enterprises can gain a competitive edge by better customer data exchange. He has 8 years of experience in product marketing for SaaS companies.