What has changed in the big data technology architecture used by big data engineers
[Guide] As a big data engineer, when performing data analysis, it is impossible to do it manually, but to use certain tools, that is, big data technology tools. In recent years, big data analysis technology has emerged, but the application of big data analysis technology to corporate brand marketing has not been implemented for long. Big data technology has had a great impact on the development of various industries, so big data engineers use What changes have taken place in China's big data technology architecture? Let's take a look.
1. From local data platform to cloud-based data platform
The cloud can be a disruptive driving force for a new data architecture approach because it provides companies with a way to rapidly expand artificial intelligence tools and functions to gain a competitive advantage.
2. From batch processing to real-time data processing
The cost of real-time data communication and streaming media functions has been greatly reduced, paving the way for its mainstream use. These technologies have enabled a series of new business applications: For example, transportation companies can provide customers with accurate second-to-second arrival time predictions when taxis arrive; insurance companies can analyze real-time behavior data from smart devices to customize tariffs In addition, manufacturers can predict infrastructure problems based on real-time sensor data.
3. From pre-integrated business solutions to modularized best-in-class platforms
In order to expand the scale of applications, companies often need to break through the limitations of the legacy data ecosystem provided by large solution providers. Now, many companies are moving towards a highly modular data architecture, this architecture uses the best, often used open source components, these components can be replaced by new technologies as needed without affecting other parts of the data architecture.
4. From point-to-point to out of data access
People can expose data through API, which can ensure that the method of directly viewing and modifying data is restricted and safe, and it also allows people to access common data sets faster. This allows data to be easily reused between teams, thereby accelerating access and achieving seamless collaboration between analysis teams, so that various artificial intelligence use cases can be developed more efficiently.
The above is the details of the changes in the big data technology architecture. I will analyze it for everyone here. I hope it will be helpful to everyone. As a big data engineer, I still hope that everyone learns some diversified big data technologies to help enterprises better perform Corporate decisions and so on.