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What Is a Data Hub?

What Is a Data Hub?

Data-hub refers to a “hub-and-spoke” system whereby all data inserted from different sources is reconfigured for efficient storage, access and delivery of information. Instead of isolating the data, such systems transfer disparate silos into one new system. However, centralising data does not automatically help companies to make sense of if they are not made compatible. Hence, it retains the value of data by de-duplication, meeting quality standards, strengthening security, and incorporating a standardised set of query services. The point of data hubs aims to ensure the capabilities of data-centric companies are unlocked efficiently. It is a form of architecture a company must have to be truly data literature. 

According to Gartner, from 2018 to 2019, there has been a surge of enquiries by clients requesting data hubs by 20%. These statistics have consistently been on the rise since then, indicating the corporate’s interest in ensuring a robust data hub is incorporated into its system. This article will explain the benefits of having a data hub system.

How Are Data Hubs Different From Other Forms of Data-Centric Architectures?

There are many types of data architecture, two of which are particularly associated with data hubs. This includes data lakes and warehouses. Choosing which architecture a company requires will depend on its type of corporation and its needs. However, larger corporations may require a combination of all three. If an organisation is keen to choose one, they need to know the differences between the three architectures. Here are two important points that make data hubs different from lakes and warehouses. 

Not Focused On Analysing Data

Data processed via a data hub helps companies to manage data directly involved in line with the business processors. Data lake and warehouse, in contrast, are primarily associated with an analytical component. Hence, while data lake focuses on analytics and reporting, data warehouses focus on analytics, reporting, and machine learning. While some may regard this to be a disadvantage, it is noted that data hubs assure data quality at a higher level in comparison to the other two. 

Cannot Store Data for Long Periods

A data hub acts as a point of mediation in contrast to data lake and data warehouses, which focus on the data collection endpoint. It, therefore, moves data between endpoints and applies governance to the data that flows across a company’s infrastructure. As a result, data in a hub architecture stores data only for a limited period since it is specifically used for presentation purposes. On the other hand, data lakes and warehouses are long-term data storage mediums.

Benefits of Having A Data Hub

While analysing data is essential, there are certain other features a data architecture should have to ensure other aspects that can help companies utilise data seamlessly is present. This is where the data hub comes in. Here are four benefits a data hub plays in a data-centric organisation.

Better Data Viaibility

A company that has multiple data entry points requires organisation. A data hub can keep track of all such points, allowing organisations to track, filter and retrieve any information they need in a matter of seconds under one single console. Modern data hubs often rely on data semantics, including business, technical and operational metadata, domain glossaries, search indices and more. As a result, varying views are produced to understand a 360-degree view of businesses. It can also find connections between different points and unify data where required, making an extremely complex process much more manageable. By providing better data visibility, data scientist or industry experts can filter out data they require to analyse, share and make decisions.

Speed of Data

The amount of data the architecture of a data hub can handle is immense. The great thing about data hubs is that it does not sacrifice their ability to store and access enormous amounts of data with time. Thus, data speed is much faster and processed at a much higher rate than in data lakes and warehouses. Modern data hubs especially require data pipelining, which will handle real-time data movements on a terabyte-scale bulk. Additionally, features such as filtering and indexing help this process. 

Data Security

One advantage that a data hub guarantees are an assurance of data security. As an architecture that centralises data and helps companies make sense of it, it can ensure the security policy of a business is intact every step of the way. This makes monitoring regulator requirements easy and updates new security protocols in one go. Having robust security measure also help detect any errors during the data pipeline flow and rectify them speedily. This is also an efficient way to ensure data quality remains a priority in an organisation. It, for instance, will have a set of complex validation rules which will consistently run in the back of the program, resulting in high data quality at all times.

Cost Effective

A company must monitor data individually from multiple systems without a data hub. Not only will such a company have to make multiple investments in storage and integration separately, but it will not make valuable connections or relationships between different data sources to provide insightful reports. This is similar to a point-to-point integration system where individual connections are required for all data to share and communicate with one another. Hence, a company may not receive their ROI correctly. However, a business with a data hub architecture needs only to invest once. Once such an architecture is incorporated into business operations, it will be responsible for storing the data individually and integrating everything into one central local whilst providing the organisation with the flexibility to retrieve information easily and gain expert insights. This means it only requires one for each source system making it easy for companies to scale up in the future as well. Thus, a data hub architecture helps corporates cut unnecessary costs and save overall finances.

Cerexio Data-Hub Multi-Cloud Platform: A Powerful Data Hub That Offers Ample Flexibility

Cerexio Data Hub Multi-Cloud Platform is a robust solution, ideal for data-dependent organisations.  You can now easily transition from traditional on-prem approaches to cloud platforms optimally. It is compatible with all cloud systems, whether public, private or hybrid. It distinguishes itself from traditional Data Hub or Universal cloud integration by integrating features of both solutions and other innovative data technologies. It accommodates Edge-to-AI data, core and cloud analytical technologies. The solution transforms your architecture whereby your data infrastructure can integrate and produce analytical knowledge even when there is an enormous and consistent flow of data, convoluted data streams, complex insights and entwined information events.  

While the solution builds a data infrastructure strong enough to process data speedily, it also assures security. Cerexio has incorporated advanced security protocols to ensure the integrity and manages your data intelligently. Connect with us to learn how this can help improve your operations.

Integrate a Data Architecture That Complements All

A data hub is ideal for a business if the central aim of the organisation is to make the retrieval of data much faster and more accurate. However, as is made clear, sometimes a data hub is not the perfect answer. The lack of analytical power and the temporary storing power of data requires a corporation to integrate features present in a data lake and/or data warehouse system. A solution like Cerexio that centralises data and provides analytical capabilities may then be the answer you have been looking for. 

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