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Apache Hadoop Data Warehouse Architecture for EDW Optimization

Reduce Costs by Moving Data and Processing to Hadoop®

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什么是 EDW?

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.

EDW 优化

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 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.

解决方案

Hortonworks Data Platform

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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

Syncsort

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EDW offload to Hadoop - High-performance ETL software to access and easily onboard traditional enterprise data to HDP. Learn More
 
 

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 optimization with Apache Hadoop ®

灵活

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Data can be loaded in HDP without having a data model in place

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可以根据对数据提出的问题来应用数据模型(读时模式)

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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 优化用例

用例 1
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Hadoop 的快速商务智能

企业针对快速商务智能和深度细微分析采用专用 EDW 系统,不过这些系统成本高且不具持续性,尚无法应对现代化大数据(如非结构化数据和大规模分析)的挑战。

通过组合用于创建数据集市的快速内存 SQL 引擎和允许您在短时间内查询大型数据集的 OLAP Cubing 引擎,Hortonworks 使 Hadoop 的快速商务智能得以实现。这样,您就可以查询最佳性能的预聚集数据,当需要精细组件的详情时,也可以 full-fidelity 形式进行查询,从而允许从支持 ODBC、JDBC 或 MDX 的主要商务智能工具进行访问。

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使用案例 2
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将 ETL 进程连接至 HADOOP

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.

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使用案例 3
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将数据归档至 HADOOP

与日俱增的数据数量以及随之而来的成本压力迫使很多企业将旧数据归档于磁带中,但是磁带中的数据不仅无法分析,而且重新恢复会耗费大量成本。

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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