大数据和预测分析之间的区别

大数据是大型组织和企业获得的庞大、庞大或海量的数据、信息或相关统计数据。由于难以手动计算大数据，因此创建和准备了许多软件和数据存储。它用于发现模式和趋势，并做出与人类行为和交互技术相关的决策。

预测分析包括通过研究当前和过去的数据趋势来预测未来的结果。它利用数据建模、数据挖掘、机器学习和深度学习算法从数据中提取所需的信息，并为未来规划行为模式。一些用于预测分析的行业工具包括 Periscope Data、Google AI Platform、SAP Predictive Analytics、Anaconda、Microsoft Azure、Rapid Insight Veera 和 KNIME Analytics Platform。

大数据和预测分析之间的区别

SR.NO	Big Data	Predictive Analytics
1.	Big Data is group of technologies. It is a collection of huge data which is multiplying continuously.	Predictive analytics is the process by which raw data is first processed into structured data and then patterns are identified to predict future events.
2.	It deals with the quantity of data, typically in the range of .5 terabytes or more.	It deals with the application of statistical models to existing data to forecast.
3.	It’s a best practice for enormous data.	It’s a best practice for data for future prediction.
4.	It has a vast backend technology imports for Dashboards and Visualizations like D3js and some paid ones like Spotfire a TIBCO tool for reporting.	It has tool with built-in integrations of the reporting tools like Microsoft BI tools. So, no need to fetch it from source or from some outside vendors.
5.	Its engines like Spark and Hadoop comes with built-in Machine Learning libraries but the incorporation with AI is still an R&D task for the Data Engineers.	It deals with the platform based on the probability and mathematical calculation.
6.	It has high level of advancement, its engines have eventually upgraded themselves throughout the development processes and level of cross-platform compatibility.	It has medium level of advancement, has a limited change of algorithmic patterns as they are giving them better score from the start with respect to their field and domain-specific work analysis.
7.	It is used to make data driven decisions.	It is used for risk evaluation and prediction of future outcomes.