A. 大数据英文怎么说
大数据（bigdata），IT行业术语，是指无法在一定时间范围内用常规软件工具进行捕捉、管理和处理的数据集合，是需要新处理模式才能具有更强的决策力、洞察发现力和流程优化能力的海量、高增长率和多样化的信息资产。
在维克托·迈尔-舍恩伯格及肯尼斯·库克耶编写的《大数据时代》中大数据指不用随机分析法（抽样调查）这样捷径，而采用所有数据进行分析处理。大数据的5V特点（IBM提出）：Volume（大量）、Velocity（高速）、Variety（多样）、Value（低价值密度）、Veracity（真实性）。
大数据包括结构化、半结构化和非结构化数据，非结构化数据越来越成为数据的主要部分。据IDC的调查报告显示：企业中百分之80的数据都是非结构化数据，这些数据每年都按指数增长百分之60。大数据就是互联网发展到现今阶段的一种表象或特征而已，没有必要神话它或对它保持敬畏之心，在以云计算为代表的技术创新大幕的衬托下，这些原本看起来很难收集和使用的数据开始容易被利用起来了，通过各行各业的不断创新，大数据会逐步为人类创造更多的价值。
B. "大数据"怎样用英文表述呢
大数据的英文翻译是big data。
释义：大数据；巨量资料；海量资料；海量数据
big block data称为大区块资料 ; 大区块资料
Big Bang Data数据大爆炸
Big Earth Data地球大数据
Big Brain Data大脑巨量资料
Big Complex Data大型复杂数据
1、?
大数据将如何改变您的做事方式？
2、.
但是庞大数据还会产生远比这更为严重的后果。
3、 themselves.
在庞大数据的世界中，相关数据几乎是自行浮出水面。
4、Ifyourservicedeals withbigdata,that's howthey'rerelated.
如果你的服务要处理大数据，那正是它们相关的东西。
5、Whatis therelationshiptoSOA?Related tothisisBigData, how isitrelatedtoSOA?
它与SOA之间有什么关系吗？与之关联的是大数据，那么它又是怎样和SOA关联起来的呢？
C. 大数据用英文怎么说
大数据(big data)指规模巨大且复杂，用现有的数据处理工具(on-hand database management
tools)难以获取(capture)、整理( curate)、管理(
manage)以及处理(process)的数据信息统称。大数据的特点可以总结为4V：volume(大量)、velocity(高速)、variety(多变)、veracity(准确)。
D. 随着大数据时代英文
摘要：As the big data era, all kinds of data in the society is growing at a rapid speed, the library also inevitably faced with the impact of the wave data information. This paper analyses the characteristics of data, data source library management, focusing on the large data will be brought about by the challenge, the final analysis of the times books management development direction of large data. Mainly includes the influence on library management of large data: data of complex data processing test library computing power, data analysis to the mining depth of traditional requirements for Library Infrastructure challenges and big data era. Future library management from the exploratory data analysis tools and technology, attach importance to the construction of infrastructure and data collection, improving several books management intelligent degree of development. Keywords large structured data and unstructured Library
E. hadoop参考文献有哪些
《大数据技术原理与应用—概念、存储、处理、分析与应用》。hadoop参考文献有《大数据技术原理与应用—概念、存储、处理、分析与应用》，Hadoop是一个开源的框架，可编写和运行分布式应用处理大规模数据。
F. 求一篇与大数据或者大数据信息安全专业相关的原版英文文献及其翻译,3000字左右。好人，拜托！
Big data refers to the huge volume of data that cannot
be stored and processed with in a time frame in
traditional file system.
The next question comes in mind is how big this data
needs to be in order to classify as a big data. There is a
lot of misconception in referring a term big data. We
usually refer a data to be big if its size is in gigabyte,
terabyte, Petabyte or Exabyte or anything larger than
this size. This does not define a big data completely.
Even a small amount of file can be referred to as a big
data depending upon the content is being used.
Let’s just take an example to make it clear. If we attach
a 100 MB file to an email, we cannot be able to do so.
As a email does not support an attachment of this size.
Therefore with respect to an email, this 100mb file
can be referred to as a big data. Similarly if we want to
process 1 TB of data in a given time frame, we cannot
do this with a traditional system since the resource
with it is not sufficient to accomplish this task.
As you are aware of various social sites such as
Facebook, twitter, Google+, LinkedIn or YouTube
contains data in huge amount. But as the users are
growing on these social sites, the storing and processing
the enormous data is becoming a challenging task.
Storing this data is important for various firms to
generate huge revenue which is not possible with a
traditional file system. Here is what Hadoop comes in
the existence.
Big Data simply means that huge amount
of structured, unstructured and semi-structured
data that has the ability to be processed for information. Now a days massive amount of data
proced because of growth in technology,
digitalization and by a variety of sources, including
business application transactions, videos, picture ,
electronic mails, social media, and so on. So to process
these data the big data concept is introced.
Structured data: a data that does have a proper format
associated to it known as structured data. For example
the data stored in database files or data stored in excel
sheets.
Semi-Structured Data: A data that does not have a
proper format associated to it known as structured data.
For example the data stored in mail files or in docx.
files.
Unstructured data: a data that does not have any format
associated to it known as structured data. For example
an image files, audio files and video files.
Big data is categorized into 3 v’s associated with it that
are as follows:[1]
Volume: It is the amount of data to be generated i.e.
in a huge quantity.
Velocity: It is the speed at which the data getting
generated.
Variety: It refers to the different kind data which is
generated.
A. Challenges Faced by Big Data
There are two main challenges faced by big data [2]
i. How to store and manage huge volume of data
efficiently.
ii. How do we process and extract valuable
information from huge volume data within a given
time frame.
These main challenges lead to the development of
hadoop framework.
Hadoop is an open source framework developed by
ck cutting in 2006 and managed by the apache
software foundation. Hadoop was named after yellow
toy elephant.
Hadoop was designed to store and process data
efficiently. Hadoop framework comprises of two main
components that are:
i. HDFS: It stands for Hadoop distributed file
system which takes care of storage of data within
hadoop cluster.
ii. MAPREDUCE: it takes care of a processing of a
data that is present in the HDFS.
Now let’s just have a look on Hadoop cluster:
Here in this there are two nodes that are Master Node
and slave node.
Master node is responsible for Name node and Job
Tracker demon. Here node is technical term used to
denote machine present in the cluster and demon is
the technical term used to show the background
processes running on a Linux machine.
The slave node on the other hand is responsible for
running the data node and the task tracker demons.
The name node and data node are responsible for
storing and managing the data and commonly referred
to as storage node. Whereas the job tracker and task
tracker is responsible for processing and computing a
data and commonly known as Compute node.
Normally the name node and job tracker runs on a
single machine whereas a data node and task tracker
runs on different machines.
B. Features Of Hadoop:[3]
i. Cost effective system: It does not require any
special hardware. It simply can be implemented
in a common machine technically known as
commodity hardware.
ii. Large cluster of nodes: A hadoop system can
support a large number of nodes which provides
a huge storage and processing system.
iii. Parallel processing: a hadoop cluster provide the
accessibility to access and manage data parallel
which saves a lot of time.
iv. Distributed data: it takes care of splinting and
distributing of data across all nodes within a cluster
.it also replicates the data over the entire cluster.
v. Automatic failover management: once and AFM
is configured on a cluster, the admin needs not to
worry about the failed machine. Hadoop replicates
the configuration Here one of each data iscopied or replicated to the node in the same rack
and the hadoop take care of the internetworking
between two racks.
vi. Data locality optimization: This is the most
powerful thing of hadoop which make it the most
efficient feature. Here if a person requests for a
huge data which relies in some other place, the
machine will sends the code of that data and then
other person compiles it and use it in particular
as it saves a log to bandwidth
vii. Heterogeneous cluster: node or machine can be
of different vendor and can be working on
different flavor of operating systems.
viii. Scalability: in hadoop adding a machine or
removing a machine does not effect on a cluster.
Even the adding or removing the component of
machine does not.
C. Hadoop Architecture
Hadoop comprises of two components
i. HDFS
ii. MAPREDUCE
Hadoop distributes big data in several chunks and store
data in several nodes within a cluster which
significantly reces the time.
Hadoop replicates each part of data into each machine
that are present within the cluster.
The no. of copies replicated depends on the replication
factor. By default the replication factor is 3. Therefore
in this case there are 3 copies to each data on 3 different
machines。
reference:Mahajan, P., Gaba, G., & Chauhan, N. S. (2016). Big Data Security. IITM Journal of Management and IT, 7(1), 89-94.
自己拿去翻译网站翻吧，不懂可以问
G. 大数据是什么
大数据是什么意思呢?
如果从字面意思来看，大数据指的是巨量数据。那么可能有人会问，多大量级的数据才叫大数据?不同的机构或学者有不同的理解，难以有一个非常定量的定义，只能说，大数据的计量单位已经越过TB级别发展到PB、EB、ZB、YB甚至BB级别。
最早提出“大数据”这一概念的 是全球知名咨询公司麦肯锡，它是这样定义大数据的：一种规模大到在获取、存储、管理、分析方面大大超出了传统数据库软件工具能力范围的数据集合,具有海量的数据规模、快速的数据流转、多样的数据类型以及价值密度低四大特征。
研究机构Gartner是这样定义大数据的：“大数据”是需要新处理模式才能具有更强的决策力、洞察发现力和流转优化能力来适应海量、高增长率和多样化的信息资产。若从技术角度来看，大数据的战略意义不在于掌握庞大的数据，而在于对这些含有意义的数据进行专业化处理，换言之，如果把大数据比作一种产业，那么这种产业盈利的关键在于提高对数据的“加工能力”，通过“加工”实现数据的“增值”。