From the perspective of data center, Internet of Things and big data projects almost all emphasize network and storage infrastructure. Before starting to implement such a large-scale data-intensive project in an organization, planners need to carefully evaluate the infrastructure requirements.

Traditional business intelligence projects are based on different needs and understandings from big data projects. Typical business intelligence starts with clear ideas and must stand scrutiny. What data is needed or collected to answer these questions, what results need to be reported, and who needs these results in the organization. For decades, such projects have been the foundation of enterprise IT. The Internet of Things (IoT) and big data have different emphases. They will ask questions: how to ask the right questions; What are the problems, how to solve these problems to better serve customers, what products must be provided to retain existing customers, and how to persuade new customers to buy the company's products and services? This usually shows that the Internet of Things and big data projects each need different professional knowledge, different experience levels and different kinds of tools. Therefore, IT will be more difficult for IT teams to run such projects. Take a solid first step in the field of Internet of Things and big data. When powerful new technologies or methods in the IT field gain a certain momentum, some people may take urgent measures-sometimes few people can understand how to get a successful first practice. The Internet of Things and big data clearly fall into this category. This understanding may induce organizations to invest heavily in very disappointing or useless data. Failure may come from choosing the wrong tool, not configuring the tool correctly to support the system, lacking the necessary professional knowledge, or working with the wrong partner. Once it fails, many decision makers will blame the method or technology. The potential of big data is an indisputable topic, and the report also advocates the Internet of Things, pointing out that it will connect everything from our mobile phones and our cars to our household appliances. Suppliers of hardware, software and professional services have joined in, and everyone wants to share a big cake in the potential benefits of these technical methods of the Internet of Things. Almost all vendors, including those in the fields of systems, storage, networks, operating systems, data management tools and development tools, have proposed a set of products and services related to big data. These homogeneous suppliers have also begun to provide methods for data conversion and data collection from smart devices. Integrating the Internet of Things and Big Data Before starting the Internet of Things and big data projects, wise leaders will slow down and evaluate what the enterprise really needs. Evaluate the capabilities and expertise of the IT team. Realistically consider what may go wrong and what information you can learn from it. Organizations usually design big data projects to determine which questions to ask, rather than tracking specific, previously known requirements. This means that decision makers and developers must first decide what questions to ask based on operational, mechanical and other types of collected data, because it is likely that no one will take the time to analyze these data. Internet of things projects are likely to become the data source needed for the implementation of big data. Both the Internet of Things and big data usually rely on the NoSQL database. On the contrary, the software cluster that relies on the system to perform data management, the extensive use of network capacity and the * * * memory sharing or complex data caching technology will accelerate the application of existing storage media. Internet of things projects are likely to have a huge impact on data center network and storage. Most organizations have a wealth of raw data, which comes from information automatically collected at the point of sale of operating systems, database management products, application frameworks, applications and service equipment. Organizations can use data to gain a clearer overall understanding of the advantages and disadvantages of plans, products and training. The Internet of Things is mixed into big data to help companies learn more about their customers. Analyzing these huge and growing data can often provide clues for enterprises to better grasp customer needs. Enterprises can also find out which information corresponding to their problems has not been collected correctly, and seek their own unique solutions to the problems. Rejecting the quick method of aiming-shooting-hitting is particularly important in the Internet of Things project. Few organizations have the courage to postpone the project because it will irritate or offend customers. The IT team must clearly understand its own purpose, and the tools used by the team and the suppliers selected will be an important part of this attempt. Only such a team can capture and tame the big data "beast" or promote the effective practice of the Internet of Things. This requires an organization to correctly configure and provide its infrastructure, including the deployment of necessary processing power, memory, storage and network capabilities, as well as appropriate software development, continuous operation, monitoring, management and security. Each of these elements must be carefully selected and configured. However, this process is not necessarily getting better and better. As customers face the Internet of Things or other projects, it is wise to consider how customers will deal with business in always online. Performance, privacy and functionality are all important. Internet of Things and Big Data Development Tools Each set of big data methods has its own series of development and deployment tools. It is also applicable to the Internet of Things platform. In order to build the most effective platform, the company's developers must understand these tools, know how to use them and know how to build an optimal system. People working on big data projects may choose to use tools different from those of the Internet of Things development team. However, the two teams must keep in touch. The IOT team needs to collect appropriate data to support the implementation of big data. For enterprises that are new to these new technologies, it is wise to choose smaller projects to start with, and then set foot in large projects with the improvement of team development experience and professional knowledge. Organizations must treat big data projects when evaluating them, which requires far-sighted operational activities of IT management teams. It is very important to choose monitoring and management tools suitable for enterprise management framework, which can provide easy-to-understand and useful data. Internet of things projects need to be lightweight, responsive and manageable because they directly face customers. If these tools are too heavy, customers will complain that your company consumes too many expensive data plans. Finding the right balance between information collection and function provision, overall performance and the ability to send data back and forth will be a thorny issue. Many organizations have found real prospects in big data. Best practices of the Internet of Things are still emerging, so standards cannot be widely used. However, in these two cases, the correct selection and configuration of components combined with professional technical knowledge is the key factor for the success of the project. Appropriate configuration selection, including system drivers, supported operating systems and systems, network and storage configuration deployment. However, usually the most important factor is to find the right attitude towards the project. In the case of big data, the goal should be to understand what kind of questions are right, rather than treating the project as another business intelligence plan. In the case of the Internet of Things, the project must be able to provide useful services in exchange for the authorization of customers to collect data to meet the sales activities, support and business intelligence systems based on big data.