大数据与数据分析之间的区别

1.大数据：
大数据指的是海量数据，并且数据随时间以快速的速度增长。它包含结构化，非结构化和半结构化数据，这些数据是如此之大和复杂，并且无法通过任何传统的数据管理工具进行管理。需要专用的大数据管理工具来存储和处理数据。容量，速度和多样性代表了大数据的主要特征。

股票交易所，数据仓库，传感器，社交媒体站点，喷气机引擎等是大数据的不同来源。

大数据应用：

• 金融服务大数据
• 通讯中的大数据
• 通讯，媒体和娱乐
• 零售大数据
• 银行与证券

大数据的好处：

• 多元化收入流
• 大数据是安全的
• 权威且可行
• 产品价格优化
• 更大的创新

2.数据分析：
数据分析是指分析原始数据并找出有关该信息的结论的过程。通过检查原始数据并从中提取有价值的见解，它有助于获取原始数据并揭示模式。数据分析的目的是提高生产力和业务收益。它可以帮助公司更好地了解他们的客户，相应地计划策略并开发产品。描述性，诊断性，预测性，说明性是数据分析的四种基本类型。

数据分析的应用：

• 卫生保健
• 出差旅行
• 赌博
• 能源管理
• 风险检测与管理

数据分析的好处：

• 改善性能
• 更好的决策
• 保持质量和一致性
• 数据驱动的营销
• 实时预测和监控

大数据与数据分析之间的区别：

S.No.	BIG DATA	DATA ANALYTICS
01.	Big data refers to the large volume of data and also the data is increasing with a rapid speed with respect to time.	Data Analytics refers to the process of analyzing the raw data and finding out conclusions about that information.
02.	Big data includes Structured, Unstructured and Semi-structured the three types of data.	Descriptive, Diagnostic, Predictive, Prescriptive are the four basic types of data analytics.
03.	The purpose of big data is to store huge volume of data and to process it.	The purpose of data analytics is to analyze the raw data and find out insights for the information.
04.	Parallel computing and other complex automation tools are used to handle big data.	Predictive and statistical modelling with relatively simple tools are used to handle data analytics.
05.	Big data operations are handled by big data professionals.	Data analytics is performed by skilled data analysts.
06.	Big data analysts need the knowledge of programming, NoSQL databases, distributed systems and frameworks.	Data Analysts need the knowledge of programming, statistics, and mathematics.
07.	Big data is mainly found in financial services, Media and Entertainment, communication, Banking, information technology, and retail etc.	Data analytics is mainly used in business for risk detection and management, science, travelling, health care, Gaming, energy management, and information technology.
08.	It supports in dealing with huge volume of data.	It supports in examining raw data and recognizing useful information.
09.	It is considered as the first step as first big data generated and then stored.	It is considered as second step as it performs analysis on the large data sets.
10.	Some of the big data tools are Apache Hadoop, Cloudera Distribution for Hadoop, Cassandra, MongoDB etc.	Some of the data analytics tools are Tableau Public, Python, Apache Spark, Excel, RapidMiner, KNIME etc.