What is the translation of " 大数据分析工具 " in English?

Examples of using 大数据分析工具 in Chinese and their translations into English

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通过社交监控和大数据分析工具维护品牌声誉。
Manage brand reputation with social monitoring and big data analytics tools.
大数据分析工具允许公司分析数据并获得洞察力。
Big data analytics tool allows companies to analyze the data and gain insights.
大数据分析工具趋势和最佳实践指南.
Guide to big data analytics tools, trends and best practices.
大数据分析工具提供各种分析包和模块,为用户提供选项。
Big Data analytics tools offer a variety of analytics packages and modules to give users options.
大数据分析工具对于公司和企业非常重要,互联网时代组织现在大量的生成和管理数据非常庞大。
Big Data Analytics tools are important for companies and enterprises because of the sheer volume of Big Data now generated and managed by modern organizations.
这些工具大体上可以分为两类:存储和分析大数据分析工具
This tool can be of two types: Storage and Analysis of Big Data analytical tools.
大数据分析工具让企业能够评估一名顾客的数据质量与数量,确保数据的一致性、价值性和真实性。
Big data analytics tools allow companies to assess the quality and quantity of data about a customer to ensure that it's consistent, meaningful and real.
这些工具可以分为两类:存储和分析大数据分析工具
This tool can be of two types: Storage and Analysis of Big Data analytical tools.
大数据分析工具还可以帮助企业节省时间和经济,并帮助获取洞察力,为数据驱动的决策提供信息。
Big Data Analytics tools help businesses in saving time and money and also in gaining insights to make data-driven decisions.
大数据分析工具还可以帮助企业节省时间和经济,并帮助获取洞察力,为数据驱动的决策提供信息。
Big Data Analytics tools also help businesses save time and money and aid in gaining insights to inform data-driven decisions.
与SIEM一样,大数据分析工具具有在网络上准确发现设备的能力。
Like SIEM, big data analytics tools have the ability to accurately discover devices on a network.
大数据分析工具捕捉机器级别的信息,以提高产量和吞吐量。
Big data analytics tools capture machine-level information to boost production yield and throughput.
这些工具大体上可以分为两类:存储和分析大数据分析工具
These tools can be two types: Storage and Analysis Big Data analytics tools.
他们可以收集有关域历史的相关详细信息,并将其与大数据分析工具结合使用。
They can gather relevant details about a domain's history andcombine this with big data analytics tools.
这些工具可以分为两类:存储和分析大数据分析工具
These tools can be two types: Storage and Analysis Big Data analytics tools.
Cloudera-提供Hadoop发行版,以及数据科学和大数据分析工具.
Cloudera- offers a Hadoop distribution, plus data science and big data analytics tools.
因此,大数据分析工具可以快速发现网络上的设备并支持事件响应工作流。
Due to this fact, a big data analytics tool can quickly discover devices on the network and support incident response workflow.
为此,新一代大数据分析工具就变得不可或缺。
To do this, a new generation of big data analytics tools is needed.
大数据分析工具已经出现,用于实时、交互式工作负载和对大型数据集的回顾性、复杂性分析。
Big data analysis tools have emerged for real-time, interactive workloads and retrospective, complex analysis of larger data sets.
通过MapReduce和ApacheHadoop等技术,大数据分析工具也在市场上蔓延。
Through technologies such as MapReduce and Apache Hadoop, access to big data analytics tools are increasing in the market.
毕业生将掌握大数据分析工具的知识,并能够使用它们来解决管理任务。
Graduates will be equipped with the knowledge of big data analytics tools and able to use them to solve managerial tasks.-.
随着越来越多的企业采用基于云计算的解决方案,他们也将拥有大数据分析工具的优势。
As more agencies adopt cloud-based solutions,they will have the benefits of big data analytics tools at their disposal.
慎重选择您的大数据分析工具.
Carefully choose your big data analytics tools.
谨慎选择你使用的大数据分析工具.
Carefully choose your big data analytics tools.
小企业也能用大数据分析工具??
How can a small company use Big Data Analytics?
利用大数据分析工具,提高资本投资决策质量。
Applying big data analytics to enhance capital investment decisions.
了解Turkcell/LifecellVentures如何利用他们的大数据分析工具,通过智慧城市模拟,即时应对人道主义危机.
Learn how Turkcell/Lifecell Ventures uses their big data analytics tools to respond instantly to humanitarian crises with smart city simulations.
相比之下,用于需求预测的现代大数据分析工具提供了跨用户业务流程的全面数据视图。
By contrast, modern big data analytics tools for demand forecasting offer a comprehensive view of data across the user's business processes.
机器学习工程师更有可能使用大数据分析工具,因此76%的受访者没有选择任何工具。
Big data tools are more likely to be used by machine learning engineers, that's why 76% of the respondents selected none.
为了填补这一空白,他们经常寻找能够让业务用户使用的大数据分析工具,提供自助式的服务。
To fill in the gaps, they often look for big data analytics tools that promise to allow business users to self-service their own needs.
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