Red Hat 계정으로 회원 프로필, 기본 설정 및 고객 상태에 따라 다음의 서비스에 액세스할 수 있습니다.
아직 등록하지 않으셨습니까? 등록해야 하는 이유:
- 한 곳에서 기술 자료 문서를 탐색하고, 지원 사례와 서브스크립션을 관리하고, 업데이트를 다운로드 할 수 있습니다.
- 조직 내의 사용자를 보고, 계정 정보, 기본 설정 및 권한을 편집할 수 있습니다.
- Red Hat 자격증을 관리하고 시험 내역을 조회하며 자격증 관련 로고 및 문서를 다운로드할 수 있습니다.
Red Hat 계정으로 회원 프로필, 기본 설정 및 자신의 고객 상태에 따른 기타 서비스에 액세스할 수 있습니다.
보안을 위해, 공용 컴퓨터 사용 중에 Red Hat 서비스 이용이 끝난 경우 로그아웃하는 것을 잊지 마십시오.로그아웃
We are pleased to announce the availability of the Hortonworks Data Platform (HDP) 2.1 on Red Hat Storage 3.0.2. This joint solution between Red Hat and Hortonworks allows you to bring the full analytical power of the Hadoop Ecosystem to the data within your Red Hat Storage cluster.
Red Hat Storage (RHS) is a great choice for a central storage repository. The fact that it is a software-defined, scale out architecture and runs on commodity hardware helps reduce your total cost of ownership, but it also simplifies managing storage capacity and allows you to grow your cluster at your own pace. For these reasons we think that eventually you'll end up with a significant amount of data within your RHS cluster and you'll likely want to begin analyzing it.
It is common for RHS users to start with Python and R analysis on your RHS data because they perform well and work intuitively with a distributed filesystem that is POSIX compliant and written in C. However, it is also common to want to explore using Hadoop because of the analytical power it brings with parallel processing, its ability to simplify ETL and the opportunity it offers to be used as a lower cost data warehouse. Rather than procuring new software and hardware infrastructure for an additional Hadoop cluster, one can actually use Hadoop directly on the data within the RHS cluster by installing the Hortonworks Hadoop Distribution directly on the RHS cluster. This solution configures RHS as the storage tier for Hadoop in lieu of HDFS. The solution works in a similar manner to how HDP would normally work with HDFS due to the fact that the HDP components are collocated on the same servers as the RHS components. This allows Hadoop to maintain data locality when scheduling analytical workloads.
Given that we initially shipped HDP 2.0.6 support with the GA release of RHS 3.0 in September, HDP 2.1 support marks the 2nd release of our joint solution with Hortonworks. In this release we've added support for Tez and HBase, we've augmented the security capabilities by validating the solution with RHEL Identity Management (LDAP and Kerberos integration) and we have also improved the user experience by simplifying the installation and configuration process.
If you're interested in trying this out, simply follow the instructions in Chapter 7 of the RHS Installation Guide.