2017年大数据的十大发展趋势
转自:灯塔大数据;微信:DTbigdat
佛瑞斯特研究公司(Forrester)的研究人员发现,2016年,近40%的公司正在实施和扩展大数据技术应用,另有30%的公司计划在未来12个月内采用大数据技术。2016年NewVantage Partners的大数据管理调查发现,62.5%的公司现在至少有一个大数据项目投入生产,只有5.4%的公司没有大数据应用计划,或者是没有正在进行的大数据项目。
研究人员称,会有越来越多的公司加速采用大数据技术。互联网数据中心(IDC)预测,到2020年大数据和分析技术市场,将从今年的1301亿美元增加至2030亿美元。“公司对数据可用性要求的提高,新一代技术的出现与发展,以及数据驱动决策带来的文化转变,都继续刺激着市场对大数据和分析技术服务的需求”, IDC副总裁Dan Vesset表示。 “2015年该市场全球收入为1,220亿美元,预计到2016年,这一数字将增长11.3%,并预计在2020年以11.7%的复合年增长率(CAGR)继续增长。”编者注:CAGR并不等于现实生活中GR(Growth Rate)的数值。它的目的是描述一个投资回报率转变成一个较稳定的投资回报所得到的预想值。我们可以认为CAGR平滑了回报曲线,不会为短期回报的剧变而迷失。
英文原文:
Big Data Trends
Open source applications like Apache Hadoop, Spark and others have come to dominate the big data space, and that trend looks likely to continue. One survey found that nearly 60 percent of enterprises expect to have Hadoop clusters running in production by the end of this year. And according to Forrester, Hadoop usage is increasing 32.9 percent per year.
2. In-Memory Technology
One of the technologies that companies are investigating in an attempt to speed their big data processing is in-memory technology. In a traditional database, the data is stored in storage systems equipped with hard drives or solid state drives (SSDs). In-memory technology stores the data in RAM instead, which is many, many times faster. A report from Forrester Research forecasts that in-memory data fabric will grow 29.2 percent per year.
Several different vendors offer in-memory database technology, notably SAP, IBM, Pivotal.
Image Source: Micron Technology
As big data analytics capabilities have progressed, some enterprises have begun investing in machine learning (ML). Machine learning is a branch of artificial intelligence that focuses on allowing computers to learn new things without being explicitly programmed. In other words, it analyzes existing big data stores to come to conclusions which change how the application behaves.
According to Gartner machine learning is one of the top 10 strategic technology trends for 2017. It noted that today's most advanced machine learning and artificial intelligence systems are moving "beyond traditional rule-based algorithms to create systems that understand, learn, predict, adapt and potentially operate autonomously."
Image Source: MapR
Predictive analytics is closely related to machine learning; in fact, ML systems often provide the engines for predictive analytics software. In the early days of big data analytics, organizations were looking back at their data to see what happened and then later they started using their analytics tools to investigate why those things happened. Predictive analytics goes one step further, using the big data analysis to predict what will happen in the future.
The number of organizations using predictive analytics today is surprisingly low—only 29 percent according to a 2016 survey from PwC. However, numerous vendors have recently come out with predictive analytics tools, so that number could skyrocket in the coming years as businesses become more aware of this powerful tool.
Image Source: Gartner
Another way that enterprises are using machine learning and AI technologies is to create intelligent apps. These applications often incorporate big data analytics, analyzing users' previous behaviors in order to provide personalization and better service. One example that has become very familiar is the recommendation engines that now power many ecommerce and entertainment apps.
In its list of Top 10 Strategic Technology Trends for 2017, Gartner listed intelligent apps second. "Over the next 10 years, virtually every app, application and service will incorporate some level of AI," said David Cearley, vice president and Gartner Fellow. "This will form a long-term trend that will continually evolve and expand the application of AI and machine learning for apps and services."
Image Source: Microsoft
Many enterprises are also incorporating big data analytics into their security strategy. Organizations' security log data provides a treasure trove of information about past cyberattack attempts that organizations can use to predict, prevent and mitigate future attempts. As a result, some organizations are integrating their security information and event management (SIEM) software with big data platforms like Hadoop. Others are turning to security vendors whose products incorporate big data analytics capabilities.
Image Source: IBM
With all those new devices and applications coming online, organizations are going to experience even faster data growth than they have experienced in the past. Many will need new technologies and systems in order to be able to handle and make sense of the flood of big data coming from their IoT deployments.
One new technology that could help companies deal with their IoT big data is edge computing. In edge computing, the big data analysis happens very close to the IoT devices and sensors instead of in a data center or the cloud. For enterprises, this offers some significant benefits. They have less data flowing over their networks, which can improve performance and save on cloud computing costs. It allows organizations to delete IoT data that is only valuable for a limited amount of time, reducing storage and infrastructure costs. Edge computing can also speed up the analysis process, allowing decision makers to take action on insights faster than before.
Image Source: Dell.com
Image Source: Robert Half Technology 2017 Salary Guide for Technology Professionals
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