To do big data, first of all, you should understand what is the core of your own enterprise or industry. We often find that many enterprises are defeated not by their current competitors, but by many competitors who are not your competitors. For a simple example, everyone thinks that Amazon is an e-commerce company, but this is wrong. Its main revenue now comes from the cloud (cloud service). That is to say, enterprises need to find their own core data (value).
Big Data
As the world continues to urbanize and the amount of data generated by cities grows, the importance of big data analytics in shaping the future of urban life will only increase.
2013 is called the first year of big data, and all walks of life are gradually opening the era of big data applications. Until now, big data is still talked about.
Data Lake is a term that has emerged in the past decade to describe an important part of the data analysis pipeline in the big data world.
The application of big data is just like the use of credit cards. The better you use it, the greater the income. On the contrary, can enterprises bear the cost of mistakes in big data? This article describes 6 major mistakes and solutions.
The data grid can overcome many challenges inherent in big data by driving higher levels of autonomy and data engineering alliances among a wider range of stakeholders. However, big data is not a panacea, it brings a series of risks for enterprises to manage.
In the digital age, the emergence of disruptive technologies has changed the nature of lending. Thanks to big data, the lending process is now less about the bank and more about the customer.
The digital twin is a technology for real-time virtual modeling of objects, from buildings to entire cities, an emerging concept that could transform the built environment and real estate industry in many ways.
Low-latency analytics is a technology that enables processing and analyzing big data in real time or near real time. It is critical in big data processing because it allows organizations to extract insights from data faster.
Companies tend to make their Big Data projects large in size and scope when implementing them, but the truth is that most Big Data projects usually end up in failure.