Big data analytics is the use of advanced analytic techniques against very large, diverse data sets that include structured, semi-structured and unstructured data, from different sources, and in different sizes from terabytes to zettabytes.Big data is a term applied to data sets whose size or type is beyond the ability of traditional relational databases to capture, manage and process the data with low latency. Big data has one or more of the following characteristics: high volume, high velocity or high variety. Artificial intelligence (AI), mobile, social and the Internet of Things (IoT) are driving data complexity through new forms and sources of data. For example, big data comes from sensors, devices, video/audio, networks, log files, transactional applications, web, and social media — much of it generated in real time and at a very large scale.Analysis of big data allows analysts, researchers and business users to make better and faster decisions using data that was previously inaccessible or unusable. Businesses can use advanced analytics techniques such as text analytics, machine learning, predictive analytics, data mining, statistics and natural language processing to gain new insights from previously untapped data sources independently or together with existing enterprise data.
大数据分析是对非常大、多样的数据集使用先进的分析技术，这些数据集包括结构化、半结构化和非结构化数据，来自不同的源，大小从TB到Zettabytes不等。大数据是一个用于数据集的术语，其大小或类型超出了传统关系数据库捕获、管理和处理低延迟数据的能力。大数据具有以下一个或多个特征：高容量、高速或高多样性。人工智能（AI）、移动、社会和物联网（IOT）正通过新的数据形式和来源推动数据复杂性。例如，大数据来自传感器、设备、视频/音频、网络、日志文件、事务性应用程序、网络和社交媒体——其中大部分都是实时生成的，而且规模非常大。对大数据的分析允许分析师、研究人员和业务用户使用以前无法访问或无法使用的数据做出更好更快的决策。企业可以使用先进的分析技术，如文本分析、机器学习、预测性分析、数据挖掘、统计和自然语言处理，独立地或与现有企业数据一起从以前未开发的数据源获得新的见解。
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