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The Six Different Styles of Data Hubs Today

February 28th, 2017 / / categories: 31 /

In the information architecture of many companies and businesses today, Data Hubs are essential. They provide an automated way of storing and managing the data of the company and be able to easily access the data. Data Hubs have become a great option for companies who want to have a more organization in the data in their systems. Simple data warehouses and data silos are only good for storing data that the company inputs. This makes it a challenge when trying to access the data having to navigate through a plethora of files. Thankfully, with Data Hubs, this very grueling tasks can be automated and make much more efficient for the company.

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Companies that use data hubs have a much better way of storing their data and when needed, being able to access it. The data hub acts like a digital organizer taking in the inputted data and converting it something that can easily be stored and then convert it back for usage when a person needs it. However, a “data hub” can be quite a general term. You might not know but there are different architectural styles of data hubs, six to be exact and we are going to take a look into these today.

1.) Publish-Subscribe – The hub acts as a facilitator between the publisher and the subscriber. The publisher creates and inputs data which it can push into the hub or the hub pulls from the publisher. The subscriber on the other hand, pulls the data from the hub or the hub can push the data to the subscriber.

2.) ODS for Integrated Reporting – This style was created when reports coming out from the hub degraded the performance of the system. Through this style different apps have been integrated into the Data Hub allowing for integrated reporting.

3.) ODS for Data Warehouses – Just like the previous style, certain apps are being integrated into the data hub. Through the data hub, data from the apps are being sent to the data warehouses and it certain cases there may be further integration as the data goes into the layer of the data warehouses.

4.) MDM Hub – In a Master Data Management Hub, integration still happens but there are some apps that produce the data and are not integrated and instead are external. The management of the data content is also important resulting in the permit for a human user to be able to do analysis and updating on the data.

5.) Message Hub – This style of data hub facilitates how the data is being integrated which is in the containment of messages in real time. To put it basically, it follows a “command and control” approach by taking in the command which is the message and processes them to the control.

6.) Integration Hub – This style of Data Hub functions by integrating all the data that flows in through batch movement as well as through messages. Through this, the hub will supply the warehouse layer without the warehouse needing to do further integration done by the hub.

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