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EDW Optimization with Hadoop

通过把数据和处理工作转移到 Hadoop® 来降低成本

Read the analyst white paper on EDW optimization.

下载报告

典型 EDW 的问题

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 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 further empower corporations and deeper analytics and insights. More importantly, EDW optimization using Hadoop provides a cost-efficient environment with optimal performance, scalability and flexibility.

使用 Apache Hadoop ® 优化 EDW

灵活

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可以在 HDP 中加载数据,无需实施数据模型

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Data model can be applied based on the questions being asked of data (schema-on-read)

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HDP 设计为在用户遇到问题时解答问题

高效

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100% 的数据以颗粒级别提供,可供分析

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HDP可以存储和分析结构化数据和非结构化数据

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可以按不同方式来分析数据以支持不同用例

经济高效

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HDP (Hortonworks Data Platform) 完全开放,没有任何软件许可费用

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HDP 在商用硬件上运行

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新数据在几天甚至几小时内便可以存储在 HDP 中可供使用

EDW 优化

解决方案

Hortonworks Data Platform

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强劲的开放式 Hadoop 数据仓库架构,包含针对数据监管和集成、数据管理、数据访问及安全和运营的能力,专为利用现有数据中心技术深度集成数据而设计。了解更多

Syncsort

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EDW 卸载到 Hadoop - 访问高性能 ETL 软件,可轻松地将传统企业数据连接至 HDP。了解更多
 
 

JethroData

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高性能分析引擎,适用于 Hadoop 大数据上的交互式 BI。了解更多

专业服务

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我们提供专家指导和支持,可以快速地证明您的新架构的价值,并最大化经过全面测试和验证的 Hortonworks 数据架构优化解决方案的价值。了解更多

EDW 优化用例

用例 1
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将 ETL 进程连接至 HADOOP

ETL 进程会占用标准的 EDW 系统的 45% 至 65% CPU 周期。这些价值很低的 ETL 作业不仅会抢占更重要的业务工作负载的资源,而且可能会导致 SLA 丢失。Hadoop 通过少量移植工作,以低得多的成本就可完成这些 ETL 作业,不仅可以节省成本,而且可以为价值较高的分析工作负载释放 EDW 系统的空间。通过提供高性能 ETL 工具,强劲的 SQL 引擎,并与所有主要商务智能供应商集成,Hortonworks 将这一操作变得非常轻松。

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使用案例 2
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ACTIVE ARCHIVE & ENRICHMENT IN HADOOP

Increasing data volumes and cost pressures force many companies to archive old data to tape where it can’t be analyzed or must be retrieved at great expense. 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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使用案例 3
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Hadoop 上的交互式 BI

Proprietary EDW systems were adopted for fast BI and deep slice-and-dice analytics, but EDW prices are unsustainably high and these systems have not adapted to modern big data challenges like unstructured data and large-scale analytics. Hortonworks and JethroData makes fast BI on Hadoop a reality, with the combination of a fast in-memory SQL engine to create data marts with a high performance analytical engine for interactive BI that lets you support 1,000’s of concurrent users and query huge datasets in seconds. JethroData dynamically will create indexes and cubes, lowering the upfront setup cost and time. Standard access to all your Hadoop data is supported with your existing BI tools like Tableau, Qlik or Microstrategy.

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