Dkhadoop of Hadoop Big Data Platform architecture

Dkhadoop of Hadoop Big Data Platform architecture
The era of big data has come, and the explosion of information has led to a growing number of industries facing the challenge of storing and analyzing this massive amount of data. As an open-source distributed parallel processing platform, Hadoop is becoming more and more popular because of its high expansion, high efficiency and reliability. This also led to the release of the Hadoop Business Edition. This is the big fast dkhadoop for you to get a detailed introduction to the Hadoop Big Data platform architecture content.
At present, the domestic commercial distribution version of Hadoop, in addition to the big fast dkhadoop and other like Huawei Cloud. Although the issuer is different, but similar to the platform architecture, here I am familiar with the dkhadoop to introduce.

1, Big fast dkhadoop, can be said to integrate the entire Hadoop ecosystem of all components, and it was deeply optimized, recompile to a complete high-performance big Data universal computing platform, to achieve the organic coordination of the components. As a result, DKH has a very high performance boost compared to the open source Big data platform. This is also a personal feel dkhadoop than I used before the other commercial distribution of the better, most of the domestic commercial distribution of Hadoop can be said to be two times packaging, Dkhadoop do good is to dare to develop on the basis of the original ecology.

2, the big fast dkhadoop middleware technology simplifies the large data cluster configuration into three kinds of nodes, which not only simplifies the management and operation of the cluster, but also enhances the availability and stability of the cluster. Dkhadoop Middleware integrates many components of Apache, including support from file, SQL, log, message to crawler and streaming data, and heterogeneous data, integrates the fast compression algorithm, and data synchronization distribution technology, realizes the data import and reduces the mobility simultaneously realizes, For projects with real-time data requirements, there is an irreplaceable technical advantage.
3, Big fast Dkhadoop commercial release version or maintain the advantage of open source system, can be compatible with open source system 100%. Big data applications that are based on open source platforms do not need to be changed to run efficiently on Dkhadoop.
4. The Dkhadoop integrated development Framework provides more than 20 classes commonly used in big data, search, natural language processing and artificial intelligence development, with a total of more than 100 methods to achieve a significant increase in development efficiency. Dk. Hadoop integrates with NoSQL databases to simplify programming between file systems and non-relational databases, and Dk.hadoop improves the cluster synchronization system to make Hadoop data processing more efficient.
5, Dkhadoop SQL version, also provides the integration of distributed MySQL, traditional information system, can be seamlessly implemented for big data and distributed across.
6, ES: Express Dkhadoop Search system is in the open source ES system two times developed, support complete full-text search. Integrated with effective support for Chinese search and high-performance version with support for big fast data synchronization technology, DK. ES is one of the core components of DKH, with DKH integration integrated with effective support for Chinese search and high-performance versions supported by big fast data synchronization technology, DK. ES is one of the core components of Dkhadoop.
7, Chinese language processing components: Big fast Chinese language processing is currently the highest rate of domestic use of open source natural language processing development package.
The simple introduction of these, want to know more about the search query or download the Dkhadoop learning version. The following are questions about the Dkhadoop version:
DKH Standard Edition dkh-distributed SQL Edition DK. Hadoop release
DKH Standard Edition has three different sub-versions: Standalone version for development and debugging, support three-node Learning edition, support for Standard Server Edition above 5 nodes
dkh-Distributed SQL Edition has two sub-versions: Learning Edition, Server Edition

Dkhadoop of Hadoop Big Data Platform architecture