Enterprise Data Warehouse (EDW) is an organization’s central data repository that is built to support business decisions. EDW contains data related to areas that the company wants to analyze. For a manufacturer, it might be customer, product or bill of material data. EDW is built by extracting data from a number of operational systems. As the data is fed into EDW it is converted, reformatted and summarized to present a single corporate view. Data is added into the data warehouse over time in the form of snapshots and normally an enterprise data warehouse contains data spanning 5 to 10 years. A Hadoop data warehouse architecture enables deeper analytics and advanced reporting from these diverse sets of data.
일반 EDW의 문제점
The Enterprise Data Warehouse has become a standard component of the corporate data architectures. However, the complexity and volume of data has posed some interesting challenges to the efficiency of existing EDW solutions.
Realizing the transformative potential of Big Data depends on the corporations’ ability to manage complexity while leveraging data sources of all types such as social, web, IoT and more. The integration of new data sources into the existing EDW system will empower corporations more and deeper analytics and insights. More importantly, EDW optimization using Hadoop provides a highly cost-efficient environment with optimal performance, scalability and flexibility.
Hortonworks Data Platform
Powerful open Hadoop data warehouse architecture with capabilities for data governance and integration, data management, data access, security and operations—designed for deep integration with your existing data center technology. Learn More
EDW offload to Hadoop - High-performance ETL software to access and easily onboard traditional enterprise data to HDP. Learn More
새로운 아키텍처의 역량을 빠르게 증명하고 완전한 테스트와 검증을 거친 Hortonworks 데이터 아키텍처 최적화 솔루션의 기능을 최대한 활용하도록 전문가의 안내와 지원이 제공됩니다. 자세히 알아보기
EDW optimization with Apache Hadoop ®
Data can be loaded in HDP without having a data model in place
데이터에 대해 받은 질문을 기반으로 데이터 모델을 적용할 수 있음(schema-on-read)
HDP는 사용자에게 질문이 발생할 때 답변하도록 설계됨
분석에 대해 세분화된 수준으로 100%의 데이터를 사용할 수 있음
HDP에서는 구조화된 데이터 및 비구조화된 데이터 모두 저장 및 분석할 수 있음
다양한 이용 사례를 지원하기 위해 서로 다른 방식으로 데이터를 분석할 수 있음
HDP(Hortonworks Data Platform)는 100% 개방형 플랫폼으로, 소프트웨어 라이선스 비용이 들지 않음
HDP는 범용 하드웨어에서 실행됨
새로운 데이터를 HDP에 저장하고 며칠 또는 심지어 몇 시간 만에 사용할 수 있음
EDW 최적화에 대한 이용 사례
이용 사례 1
Hadoop의 신속한 BI
신속한 BI 및 상세한 분석을 위해 채택한 전용 EDW 시스템은 엄청난 비용 부담을 전가하며 비구조 데이터 및 대규모 분석과 같은 오늘날의 빅데이터 관련 과제를 해결하기에 적절하지 않았습니다.
Hortonworks는 빠른 속도의 인메모리 SQL 엔진을 조합하여 OLAP 큐빙 엔진으로 데이터 마트를 형성함으로써 대규모 데이터 세트를 몇 초 내에 조회할 수 있도록 하고 Hadoop의 신속한 BI를 현실화합니다. 이로써 사용자는 성능을 최대화하기 위해 사전 집계된 데이터를 조회할 수 있고 세부사항이 필요한 경우 정확도 높은 양식을 조회할 수 있습니다. 또한, ODBC, JDBC 또는 MDX를 지원하는 주요 BI 도구에 액세스할 수 있습니다.
A typical EDW spends between 45 to 65 percent of its CPU cycles on ETL processing.These lower-value ETL jobs compete for resources with more business-critical workloads and can cause SLA misses. Hadoop can EDW offload these ETL jobs with minimal porting effort and at substantially lower cost, saving money and freeing up capacity on your EDW for higher-value analytical workloads. Hortonworks makes it easy by providing high-performance ETL tools, a powerful SQL engine and integration with all major BI vendors.
데이터 볼륨 및 비용 증가에 따른 부담으로 인해 많은 회사에서는 불가피하게 오래된 데이터를 테이프에 보관하지만, 이는 분석이 불가능하거나 검색에 막대한 비용이 소요되는 방식입니다.
A Hadoop data warehouse architecture offers cost per terabyte on par with tape backup solutions. Because of the appealing cost, you can store years of data rather than months. All of your enterprise data remains available for retrieval, query and deep analytics with the same tools you use on existing EDW systems.
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Apache Hadoop과 엔터프라이즈 데이터 웨어하우스의 새 데이터 아키텍처를 통합하여 만들어지는 새로운 데이터는 이제 단순한 빅데이터 수준을 뛰어넘는 규모에서 관리되어야 합니다. 또한 상세 수준의 관리도 필요합니다. 데이터 증가의 속도와 다양한 새로운 데이터 소스 출현에 대응하기 위해 미래 지향적 조직에서는 데이터 아키텍처 최적화에 투자하고 있으며, Hadoop을 사용하여 EDW(Enterprise Data Warehouse) 환경을 증강하고 있습니다. 왜 Hadoop을 선택해야 할까요?
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