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[悬赏]Hadoop和企业信息管理:利用大数据的解决方案 (已翻译43%)

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英文原文:Hadoop and Enterprise Information Management: Leveraging Big Data Solutions
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admin 发布于 2017-06-26 11:08:11 (共 7 段, 本文赏金: 23元)
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The past few years have firmly established the importance of Big Data in the global business environment. 2017 looks to be the the year of greater Apache™ Hadoopimplementation (both in terms of its open source development and more available commercial options) at the enterprise level, as Enterprise Information Management (EIM) continues to need more improved Big Data solutions.


The earlier (and continuing) trends of Data Warehouse modernization, Hadoop project-level adoption, and use of Data Lakes will likely continue forward at a greater pace. Between Versions 1 and 2, Hadoop has evolved from a primarily batch-oriented processor to a powerful, real-time data cruncher that can handle enterprise grade Big Data applications as well as more traditional, legacy datasets.


Today, Hadoop can deliver a data processing infrastructure that can accommodate large and complex business applications. With Big Data at the core of the processing model, typical business systems running on Hadoop include three distinct layers: the infrastructure layer, the data layer, and the analytics layer. Thus, commercial platform vendors such as MapR or Cloudera may find it easy to position Hadoop architecture as an omni-utility platform meeting most enterprise needs.



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The Data Explosion in Modern Enterprises


 The Forbes blog post titled 5 Reasons Hadoop Is Ready for Enterprise Prime Time explains how the data explosion has forced organizations to scale up their business applications through third-party, managed services without making large investments. In the managed service scenario, businesses do not have to worry about infrastructure, in-house Data Centers, or expert manpower – thus devoting the entire time and effort to speed of delivery.


The latest “icing on the cake” is the steady supply of open source solutions for Hadoop, which extend the power and capability of this unique data platform by several times. For supply chain systems, the story is a little different. The article What is Hadoop and What Does It Mean for Supply Chain Management argues that as the basis of supply chain, risk-assessment applications is vast troves of “unstructured data,” Hadoop with MapReduce and HDFS make a formidable combination for risk assessments and mitigation in supply chain programs.



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Hadoop for Enterprise Information Management


 Business datasets have gone beyond databases to web trails, GPS data, sensor data, and social data. The new “data environment” requires advanced technologies and tools to take advantage of vast amounts of multi-structured data, which can yield profitable intelligence and sights if processed with the right tools. The article also stresses that the huge data volumes have made it necessary to find cost-friendly technological solutions for storing and processing such data. Hadoop is a wonderful solution for Big-Data enabled technologies for delivering real benefits to business users.


The Seed Analytics Group explores the Big Data Challenges for EIM, where Big Data Analytics proves to be the core differentiator for success among stiff competition. Companies like LinkedIn have leveraged Big Data Analytics to move ahead of competition. The interesting observation mad here is that many leading software vendors have embraced Hadoop as their preferred platform for Big Data applications.


Globally, businesses are encouraged to start planning for Big Data on Hadoop, and Big Data Analytics, if they have not done it already. Here, the enterprise data framework has been clearly defined in four consecutive steps of: Data Acquisition, Data Cleansing, Data Processing, and Intelligence Gathering. An industry whitepaper titled Evolving Role of the Enterprise Data Warehouse in the Era of Big Data Analytics attempts to explain that Big Data technologies need to be adapted in the traditional Enterprise Information Management model.


The Database Trends and Applications magazine reports in Trend-Setting Products in Data and Information Management for 2017, that in recent times, the Cloud has emerged as a top data storage platform among organizations. Most of the organizations who participated in this 2016 survey conducted by DBTA Magazine have more than 100TB data.



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Hadoop上的大数据

Apache的Hadoop最流行的开源版本需要高级的技术技能,而订阅Hadoop -as-a- service可以减轻客户机的维护负担。HP与HortonWorks合作,在Hadoop和它自己的大数据技术之间建立了一个坚实的技术联盟。

在这一广谱的另一端,IBM提供了在云中的基于前提和托管的Hadoop版本。到目前为止,许多想要管理多结构的大数据的组织可能会依赖Hadoop来交付理想的结果。真正的挑战在于为Hadoop数据库和它们的内部应用程序选择合适的分析解决方案。

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数据湖:独特的Hadoop仓库

数据湖有能力以不同的格式摄取原始数据,并且可以很容易地扩展到pb级。在数据湖中存储原始数据的最大好处是,数据可以反复地重新定义业务需求和需求。这允许以最灵活的格式保存数据以适应任何新的应用程序。

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Building Big Data Use Cases on Hadoop


An effective way to build the Hadoop infrastructure is through Big Data use cases. In order to build the best use case, an organization first needs manpower – a team of able Data Architects and Data Scientists who can visualize and build solutions from available data. Along with these experts, organizations also need Data Analysts and Business Intelligence experts to extract insights from the data. In an ideal situation, it is a multi-effort exercise requiring a wide variety of skills and experience.


The article titled Data Management Trends to Watch in 2017  suggests that the massive cost advantage of Hadoop storage facilities makes it the preferred choice for data storage in modern enterprises. The immense power of a Data Lake to retain data in its raw format makes it possible to repeatedly utilize that data for disparate applications.


Gartner published a helpful infographic to aid in understanding why Hadoop can deliver most of the data demands made by an Enterprise Information Management system, which requires a suitable integration of domains, road maps, processes, workflows driving desirable outcomes with full attention to data governance.


This graphic also attempts to describe the role of a Chief Data Officer, who can ideally lead the Data Governance and Data Stewardship efforts in large enterprise information networks.



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 展望未来

随着企业数据量在战略上的重要性不断提高,传统的企业数据仓库将继续演化为更大更复杂的数据架构。从高层管理人员到车间经理,每一个商业用户都可能开始利用大数据应用程序来审查、分析和报告日常业务操作中的关键任务信息。

此外,如机器学习和深度学习等先进技术包含在企业大数据应用程序中,用于预测建模、针对客户、产品定价或建议,像Hadoop这样的开源平台可能是成本高效的企业信息管理解决方案的完美答案。这些趋势将持续到2017年(及以后),并将通过Hadoop的sql化以及物联网(物联网)的增长而得到加强。

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