- Practical Big Data Analytics
- Nataraj Dasgupta
- 145字
- 2021-07-02 19:26:19
Big Data Mining for the Masses
Implementing a big data mining platform in an enterprise environment that serves specific business requirements is non-trivial. While it is relatively simple to build a big data platform, the novel nature of the tools present a challenge in terms of adoption by business-facing users used to traditional methods of data mining. This, ultimately, is a measure of how successful the platform becomes within an organization.
This chapter introduces some of the salient characteristics of big data analytics relevant for both practitioners and end users of analytics tools. This will include the following topics:
- What is big data mining?
- Big data mining in the enterprise:
- Building a use case
- Stakeholders of the solution
- Implementation life cycle
- Key technologies in big data mining:
- Selecting the hardware stack:
- Single/multinode architecture
- Cloud-based environments
- Selecting the software stack:
- Hadoop, Spark, and NoSQL
- Cloud-based environments
- Selecting the hardware stack:
推薦閱讀
- Microsoft Power BI Quick Start Guide
- 大數(shù)據(jù)戰(zhàn)爭:人工智能時代不能不說的事
- Hadoop Real-World Solutions Cookbook(Second Edition)
- 現(xiàn)代機械運動控制技術
- Grome Terrain Modeling with Ogre3D,UDK,and Unity3D
- 網(wǎng)站前臺設計綜合實訓
- 分析力!專業(yè)Excel的制作與分析實用法則
- 人工智能:語言智能處理
- Learning ServiceNow
- 單片機技術項目化原理與實訓
- Mastering Ansible(Second Edition)
- RealFlow流體制作經(jīng)典實例解析
- 軟測之魂
- Learning OpenShift
- 博弈論與無線傳感器網(wǎng)絡安全