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
The vast range of possibilities and outcomes in sport makes it fertile ground for number crunchers. Analysts use sophisticated algorithms to factor in everything from temperature and humidity to the materials used in the manufacturing of tournament equipment in order to predict results.
Many of these factors are prone to volatility over the course of a sporting event, making real time analysis even more challenging. A case in point is the Goldman Sachs model built on regression analysis of all team results leading up to the tournament that had to be updated mid-tournament. They predict a 2-1 victory for Brazil over the Dutch in the finals (which of course we now know is impossible after Brazil's stunning defeat against Germany in the semi-finals).
More than a Crystal Ball
Employing Big Data technologies to correlate complex and dynamic data streams to predict sporting outcomes is becoming commonplace. However, the use cases for Big Data don't stop there.
Big Data is at work everywhere at the World Cup with use cases that range from optimizing public transportation, improving social media orchestration, lowering lighting bills, and reducing traffic congestion, to smart parking, better waste management, and crowd control. Each day of the tournament, millions of sensors stream massive amounts of information that needs to be processed, analyzed and stored.
Across the globe, more and more city planners, event organizers and government agencies are investing in Big Data to streamline, secure and enrich the lives of their constituents. In the corporate world, retailers, cell phone providers, and manufacturing firms are busy retrofitting older equipment with sensors, and are looking for ways to instrument new equipment so they can understand their customers better and run their businesses more efficiently.
Operational Analytics Made Easy with Splunk and Red Hat
The growth of machine data has given rise to an entire field of Big Data analytics known as operations or operational analytics. The goal is to correlate diverse streams of machine data to provide timely, actionable insights to the end user.
Splunk is a market leader in the space and a strategic partner with Red Hat around our storage products. As the volumes of sensor data grow, enterprises and government agencies look to agile, affordable storage platforms to manage the cost and complexity of machine data. Learn how machine data is changing the business landscape and how Splunk & Red Hat can help you build a world class platform for operational analytics.
Please join us for a free webinar on July 16th at 2pm EST to speak with Red Hat and Splunk experts on how you can get started monetizing machine data today. Register here.