Due to the rapid development and progress of big data, more and more talents are devoted to the industry of big data, but for now, there is also a shortage of big data talents. In the process of learning big data, Hadoop is important as a core module of big data development. So what are the techniques of big data storage in Hadoop environment?
There are several techniques for big data storage, and it is important to understand the techniques for learning big data development, including distributed storage, virtualization, and so on, which need to focus on understanding.
Hadoop is designed to bring computing closer to the data nodes, while using the massive horizontal scaling capabilities of the HDFS file system.
Although, the usual solution for Hadoop to manage its own data inefficiencies is to store Hadoop data on a SAN. But this also creates its own performance and scale bottlenecks. Now, if you run all your data through a centralized SAN processor, it runs counter to the distributed and parallelized nature of Hadoop. You either have to manage multiple SANs for different data nodes or centralize all the data nodes into one SAN.
But Hadoop is a distributed application and should run on distributed storage so that storage retains the same flexibility as Hadoop itself, though it also requires embracing a software-defined storage solution and running on commercial servers, which is naturally more efficient compared to bottlenecked Hadoop.
Virtualized Hadoop is already widely used in the enterprise market, and many places are using virtualization, with more than 80% of physical servers now virtualized. However, there are still many enterprises that avoid virtualized Hadoop because of performance and data localization issues.
Many people think analytics is a new feature, but it is not, it has been in the traditional RDBMS environment for many years. The difference is based on the emergence of open source applications, and the ability to integrate database forms and social media, unstructured data sources (for example, Wikipedia). The key is the ability to integrate multiple data types and formats into a single standard, facilitating easier and more consistent visualization and report production. The right tools are also critical to the success of an analytics/business intelligence project.