What about Big value when the infrastructure is blocked

In the big data conversation, there is a lack of attention to the infrastructure necessary to support its operation-especially for real-time applications.

For many enterprises, big data means they have the right to use the data warehouse they have been using. The data warehouse has been and will continue to be a key component of enterprise-level organizations.

Such a system provides data sets by organization and can split and package the data so that business analysts can perform in-depth analysis on the business situation.

It is this form of data (analyzed and processed into actionable information) that will be integrated into the data center for applications and infrastructure, enter numerous systems and programs as input information for near-real-time decision-making.

However, the data warehouse is not designed for these forms of massive integration and access, nor for a variety of business intelligence systems that assist in data processing.

Sometimes a large amount of input data will cause the support system to crash, coupled with a large number of systems that attempt to access accurate data, such applications are unlikely to support such a large amount of pressure.

If big data in an enterprise becomes a successful platform for business development and operations, it must be considered a more important data center asset. This approach requires a long and arduous effort to build infrastructure and architecture to ensure access to the system and scalability to meet more needs.

Similar to the architecture used in public-oriented applications, we will almost certainly be used to implement the model. Big Data in this model can be as much as traditional (daily or even weekly) and near real-time. This is the development direction of the next-generation data center model we are looking forward to, and it is also the most valuable.

Data Retrieval congestion

Reliability is the most important, especially when it comes to infrastructure. The integration of infrastructure and application services often leads to obstacles, which causes the system to wait for real-time data retrieval. It cannot be processed continuously until the process is completed or otherwise succeeded.

When the service runs well, blocking is no longer a problem. Data can be retrieved almost immediately and processed. However, when the service is overwhelmed, the dependent system will be waiting for response.

This delay affects the data supply chain, from the service itself to dependent systems and end users. These users do not understand why the system is slow, because it is deeply hidden under several architecture layers, there is no way to notify them through the system in a timely manner.

Therefore, the reliability and performance of big data systems must be ensured. An appropriate design architecture focuses on scalability and internal relevance, which are characteristic of big data-driven organizations.

It will take a long time for us to invest in the construction of an architecture to achieve scalability. This will allow the entire data center to use big data extensively.