什么是云计算和大数据

Big data and cloud computing are two sides of the coin. The whole world is on a way to have an online business and because of that cloud server has becomes more advanced with different technology platforms to deploy services online. The main addition to IT industry now a days is, cloud computing and big data which are by default becomes the current top issues in the IT business environment. And This trend not only becomes the reality with a far-reaching influence on business but also big data have reached its limits to deploy services to the IT infrastructure. And only the company that manages to obtain an information advantage from the data silos is one step ahead to the competition in the future.

大数据和云计算是其中的两个方面。 全世界正在寻求一种在线业务，因此，通过不同的技术平台，云服务器变得更加先进，可以在线部署服务。 如今，IT行业的主要新增功能是，默认情况下，云计算和大数据已成为IT业务环境中当前的首要问题。 而且，这种趋势不仅对业务产生了深远的影响，而且大数据已经达到了将其服务部署到IT基础架构的极限。 而且，只有设法从数据孤岛中获得信息优势的公司才能在未来的竞争中领先一步。

What is Big Data?

什么是大数据？

The different technology news websites in 1990 spoken a lot about it. Just take a look at few lines, “ the logic works behind big data is nothing a discovery term “business intelligence” is available to systematically analyze data.” But now as the time went all the people infected with big data get sued to learn about its advantages. Because as the time passes by every objects collect data with massive amounts of information every second. This includes smart phones, tablets, cars, electricity meters or cameras. There are also different areas that are not located in the immediate vicinity of a person, such as fully automated manufacturing lines, Internet providers, distribution warehouse, online organizations, aircraft and other means of transport. And of course it is we humans who nurture big data with our habits. Tweets on Twitter, comments on Facebook, Google search queries, browsing with Amazon and even the vital signs during a jogging session provide modern companies vast amounts of data today which can turn in valuable information.

1990年，不同的技术新闻网站对此发表了很多看法。 只需看几行，“大数据背后的逻辑就可以了，没有发现术语“商业智能”可用于系统地分析数据。” 但是随着时间的流逝，所有感染大数据的人都被起诉以了解其优势。 因为随着时间的流逝，每个对象每秒都会收集具有大量信息的数据。 这包括智能手机，平板电脑，汽车，电表或照相机。 还有不位于人的附近的不同区域，例如全自动生产线，互联网提供商，分销仓库，在线组织，飞机和其他运输工具。 当然，人类是根据我们的习惯来培养大数据的。 Twitter上的推文，Facebook上的评论，Google搜索查询，在亚马逊上慢跑期间的生命迹象以及当今的生命体征为当今的现代公司提供了大量数据，这些数据可以转化为有价值的信息。

Let’s understand Big Data : Managed and Un-Managed data

让我们了解大数据：托管和非托管数据

Now a days vast data sets are not a new phenomenon. From last decades, thousands of banks, government agencies, retail chains, insurance companies collect vast information on inventories, drilling data and transactions and online businesses with their client information. But the definition of Big data is that, it is a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications becomes easy with Big Data. The challenges include capture, storage, search, sharing, transfer, analysis, and visualization. The trend to larger data sets is due to the additional information derivable from analysis of a single large set of related data, as compared to separate smaller sets with the same total amount of data, allowing correlations to be found to “spot business trends, determine quality of research, prevent diseases, link legal citations, combat crime, and determine real-time roadway traffic conditions.”

如今，庞大的数据集已经不是一个新现象。 在过去的几十年中，成千上万的银行，政府机构，零售链，保险公司通过其客户信息收集了大量有关库存，钻探数据和交易以及在线业务的信息。 但是大数据的定义是，它是一个庞大而复杂的数据集的集合，以至于很难使用现有的数据库管理工具来处理，或者传统的数据处理应用程序对于大数据而言变得很容易。 挑战包括捕获，存储，搜索，共享，传输，分析和可视化。 较大数据集的趋势是由于与具有相同数据总量的单独较小数据集相比，可以从对单个较大的相关数据集进行分析得出的附加信息，从而可以发现“发现业务趋势，研究质量，预防疾病，关联法律引用，打击犯罪并确定实时道路交通状况。”

All this things are grouped together and includes projects for parallel processing of large data sets, data mining grids, distributed file systems and databases, distributed to the typical areas of what is now known as big data. All of the above areas and industries are struggling with the management and processing of all the large data volumes projects comes under the big data concept.

所有这些东西都组合在一起，包括用于并行处理大数据集，数据挖掘网格，分布式文件系统和数据库的项目，这些项目分布到现在所谓的大数据的典型区域。 上述所有领域和行业都在努力管理和处理所有大数据概念下的大数据量项目。

All above things are related to large database required industries but what about “normal” industries. Today’s challenges are that data arise from many different sources and sometimes fast, unpredictable and, lest unstructured. Big data is to help in places where a lot of different data sources may be combined. Examples are tweeting on Twitter, browsing behavior or information about clearance sales and to this understanding to develop new products and services. New regulations in the financial sector lead to higher volumes of data and require better analysis.

以上所有内容都与大型数据库所需的行业有关，但与“正常”行业有关。 当今的挑战是数据来自许多不同的来源，有时是快速，不可预测的，以至于没有结构。 大数据可在可能组合许多不同数据源的地方提供帮助。 例如在Twitter上发布推文，浏览有关清仓销售的行为或信息，并以此了解开发新产品和服务。 金融部门的新法规导致大量数据，需要更好的分析。

In addition to that most online web portals like Bing, yahoo, Google and Facebook collect an enormous amount of daily data which they also associate with users and to understand how the user moves to the side and behaves. Big data becomes a general problem. According to Gartner, enterprise data could grow in the next five years by up to 650%. This large unstructured data or big data that have already shown that they are difficult to manage. In addition, The premier global market intelligence firm estimates that the average company has to manage 50 times more information by 2020, while the number of IT staff will increase by only 1.5%. A challenge companies must respond to in an efficient manner if they try to remain competitive.

除了Bing，Yahoo，Google和Facebook之类的大多数在线Web门户网站，它们还收集了大量的每日数据，它们还与用户相关联，以了解用户如何向一侧移动并表现。 大数据成为普遍问题。 根据Gartner的说法，企业数据在未来五年内可能会增长650％。 如此庞大的非结构化数据或已经表明难以管理的大数据。 此外，全球领先的市场情报公司估计，到2020年，普通公司必须管理的信息量将增加50倍，而IT员工人数将仅增加1.5％。 如果公司试图保持竞争力，就必须以有效的方式应对挑战。

But why companies choose big data

但是为什么公司选择大数据

Big data helps large organizations to manage the databases in more advanced and parallel way within the defined time.

大数据可帮助大型组织在定义的时间内以更高级和并行的方式管理数据库。

Big data is nothing but cloud technology

大数据不过是云技术

Cloud computing is a new technology where a defined cloud infrastructures help to reduce costs for the IT infrastructure. This allows company to be able to focus more effectively on their core business and gain greater flexibility and agility for the implementation of new solutions. Cloud has many measures so, one can easily get a cloud related to its infrastructure to earn unlimited advantages in managing data. Thus a foundation is laid, to adapt to the ever-changing amounts of data and to provide the necessary scalability. Cloud computing providers are capable based on investments in their infrastructure, to develop a big data usable and friendly environment and maintain these. Whereas a single company can’t provide the adequate resources for scalability and also does not have the necessary expertise.

云计算是一项新技术，其中定义的云基础架构可帮助降低IT基础架构的成本。 这使公司能够更有效地专注于其核心业务，并在实施新解决方案方面获得更大的灵活性和敏捷性。 云具有许多措施，因此，人们可以轻松获得与其基础架构相关的云，从而获得管理数据的无限优势。 这样就奠定了基础，以适应不断变化的数据量并提供必要的可伸缩性。 云计算提供商有能力基于对基础架构的投资，开发大数据可用和友好的环境并进行维护。 一家公司无法提供足够的资源来实现可伸缩性，并且也没有必要的专业知识。

Cloud server resources increase directly proportional to amount of data.

云服务器资源与数据量成正比。

The connected server network of Cloud is used to manage the complete resources. Cloud computing infrastructures are designed to grow or reduce with the demands and needs. Companies can meet the high requirements – such as high processing power, amount of memory, high I / O, high-performance databases, etc. – that are expected from big data, easily face through the use of cloud computing infrastructure without investing heavily in their own resources.

连接的Cloud服务器网络用于管理全部资源。 云计算基础架构旨在随需求增长或减少。 公司可以满足大数据所期望的高要求，例如高处理能力，内存量，高I / O，高性能数据库等，可以通过使用云计算基础架构轻松应对而无需大量投资自己的资源。

As, I have stated above Cloud has many measures : A Cloud can be Private, Public and Hybrid. Cloud concepts such as infrastructure-as-a-service (IaaS) combine both worlds and take in a unique position. For those who understand the SAN / NAS approach, resources can also be use to design massively parallel systems. For companies who find it difficult to deal with the above technologies or understand this, IaaS providers offer appropriate solutions to avoid the complexity of storage technologies and to focus on the challenges facing the company.

如前所述，云具有许多措施：云可以是私有，公共和混合的。 诸如基础架构即服务(IaaS)之类的云概念结合了两个方面，并处于独特的位置。 对于那些了解SAN / NAS方法的人来说，资源也可以用于设计大规模并行系统。 对于发现难以使用上述技术或难以理解这些技术的公司，IaaS提供商可提供适当的解决方案，以避免存储技术的复杂性并专注于公司面临的挑战。

An acceptable solution comes from cloud computing pioneer eUkhost Web Services. The Intelligent Cloud hosting platform called eNlight is been designed to deploy all the required resources at any point of time to the websites. Uptime, Flexibility and resources are the main key features which makes eNlight one big data solution for all.

可接受的解决方案来自云计算先驱eUkhost Web服务。 名为eNlight的智能云托管平台旨在在任何时间点将所有必需的资源部署到网站。 正常运行时间，灵活性和资源是使eNlight成为所有人的大数据解决方案的主要关键功能。

翻译自: https://www.eukhost.com/blog/webhosting/what-is-big-data-and-cloud-computing/

什么是云计算和大数据