由讲师进行实时指导的大数据本地培训课程将首先介绍大数据的元素概念，然后介绍用于执行数据分析的编程语言和方法。在课程的演示练习环节，我们会讨论、比较并使用用于实现大数据存储、分布式处理、可伸缩性的工具和基础架构。
大数据培训形式包括“现场实时培训”和“远程实时培训”。现场实时培训可在客户位于中国的所在场所或NobleProg位于中国的企业培训中心进行，远程实时培训可通过交互式远程桌面进行。
NobleProg -- 您的本地培训提供商
客户评论
可以应用的场景及案例
zhaopeng liu - Fmr
课程: Spark for Developers
案例分析
国栋 张
课程: Spark for Developers
本届会议的所有部分
Eric Han - Fmr
课程: Spark for Developers
实例演习; 实际工作经验分享
澳新银行
课程: Data Vault: Building a Scalable Data Warehouse
老师对数据仓库的知识很全面，赞！
澳新银行
课程: Data Vault: Building a Scalable Data Warehouse
老师讲解细致, 讨论气氛融洽
澳新银行
课程: Data Vault: Building a Scalable Data Warehouse
事实上，所有数据和软件都已准备好在已准备好的VM上使用，由外部磁盘中的培训师提供。
vyzVoice
课程: Hadoop for Developers and Administrators
Machine Translated
材料范围
Maciej Jonczyk
课程: From Data to Decision with Big Data and Predictive Analytics
Machine Translated
系统化ML领域的知识
Orange Polska
课程: From Data to Decision with Big Data and Predictive Analytics
Machine Translated
培训后可以探索很多问题。
Klaudia Kłębek
课程: Data Mining z wykorzystaniem R
Machine Translated
培训师知识渊博，包括我感兴趣的领域。
Mohamed Salama
课程: Data Mining & Machine Learning with R
Machine Translated
非常适合需求。
Yashan Wang
课程: Data Mining with R
Machine Translated
理查德非常冷静和有条不紊，具有分析洞察力 - 正是提出这种课程所需的品质。
Kieran Mac Kenna
课程: Spark for Developers
Machine Translated
我喜欢做的练习。
Nour Assaf
课程: Data Mining and Analysis
Machine Translated
动手练习和培训师能够用简单的术语解释复杂的主题。
youssef chamoun
课程: Data Mining and Analysis
Machine Translated
所提供的信息很有意思，最好的部分是在我们从Durex获得数据并且处理我们熟悉的数据并执行操作以获得结果时。
Jessica Chaar
课程: Data Mining and Analysis
Machine Translated
我最喜欢教练提供真实的实例。
Simon Hahn
课程: Administrator Training for Apache Hadoop
Machine Translated
我真的很享受培训师的巨大能力。
Grzegorz Gorski
课程: Administrator Training for Apache Hadoop
Machine Translated
我真的很喜欢这些实践课程。
Jacek Pieczątka
课程: Administrator Training for Apache Hadoop
Machine Translated
我认为这些信息很有趣。
Allison May
课程: Data Visualization
Machine Translated
我非常感谢Jeff使用了适用于教育数据的数据和示例。他使它变得有趣和互动。
Carol Wells Bazzichi
课程: Data Visualization
Machine Translated
了解所有图表类型及其用途。学习混乱的价值。了解显示时间数据的方法。
Susan Williams
课程: Data Visualization
Machine Translated
培训师很热情。
Diane Lucas
课程: Data Visualization
Machine Translated
我真的很喜欢内容/讲师。
Craig Roberson
课程: Data Visualization
Machine Translated
我是一名实践学习者，这是他做了很多的事情。
Lisa Comfort
课程: Data Visualization
Machine Translated
我喜欢这些例子。
Peter Coleman
课程: Data Visualization
Machine Translated
我通常从这些例子中受益。
Peter Coleman
课程: Data Visualization
Machine Translated
我喜欢现实世界的好例子，对现有报道的评论。
Ronald Parrish
课程: Data Visualization
Machine Translated
我真的受益于培训师愿意分享更多。
Balaram Chandra Paul
课程: A practical introduction to Data Analysis and Big Data
Machine Translated
我们对整个环境了解得更多。
John Kidd
课程: Spark for Developers
Machine Translated
培训师让课堂变得有趣和有趣，这对全天培训有很大帮助。
Ryan Speelman
课程: Spark for Developers
Machine Translated
我认为培训师具有将幽默和现实生活故事相结合的优秀风格，使手头的主题非常平易近人。我强烈推荐这位教授。
课程: Spark for Developers
Machine Translated
非常喜欢互动的学习方式。
Luigi Loiacono
课程: Data Analysis with Hive/HiveQL
Machine Translated
这是一次非常实用的训练，我喜欢动手练习。
Proximus
课程: Data Analysis with Hive/HiveQL
Machine Translated
我受益于良好的概述，理论与练习之间的良好平衡。
Proximus
课程: Data Analysis with Hive/HiveQL
Machine Translated
由于虚拟机，我非常喜欢动态交互和“动手”主题，非常刺激！
Philippe Job
课程: Data Analysis with Hive/HiveQL
Machine Translated
Ernesto在解释使用Spark及其各种模块的高级概念方面做得很好。
Michael Nemerouf
课程: Spark for Developers
Machine Translated
我受益于培训师的能力和知识。
Jonathan Puvilland
课程: Data Analysis with Hive/HiveQL
Machine Translated
我通常从技术的介绍中受益。
Continental AG / Abteilung: CF IT Finance
课程: A practical introduction to Data Analysis and Big Data
Machine Translated
整体而言，内容很好。
Sameer Rohadia
课程: A practical introduction to Data Analysis and Big Data
Machine Translated
迈克尔培训师对大数据和R的主题非常了解和熟练。他非常灵活，能够快速定制满足客户需求的培训。他也非常有能力在旅途中解决技术和主题问题。神奇而专业的训练！
Xiaoyuan Geng - Ottawa Research and Development Center, Science Technology Branch, Agriculture and Agri-Food Canada
课程: Programming with Big Data in R
Machine Translated
我非常喜欢推出新包装。
Ottawa Research and Development Center, Science Technology Branch, Agriculture and Agri-Food Canada
课程: Programming with Big Data in R
Machine Translated
导师Michael Yan先生很好地与观众互动，指导很明确。导师还可以根据培训期间学生的要求添加更多信息。
Ottawa Research and Development Center, Science Technology Branch, Agriculture and Agri-Food Canada
课程: Programming with Big Data in R
Machine Translated
主题和节奏是完美的。
Tim - Ottawa Research and Development Center, Science Technology Branch, Agriculture and Agri-Food Canada
课程: Programming with Big Data in R
Machine Translated
我认为培训师具有将幽默和现实生活故事相结合的优秀风格，使手头的主题非常平易近人。我强烈推荐这位教授。
课程: Spark for Developers
Machine Translated
大数据课程大纲
课程名称
课程时长
课程概览
课程名称
课程时长
课程概览
35小时
课程概览
Advances in technologies and the increasing amount of information are transforming how business is conducted in many industries, including government. Government data generation and digital archiving rates are on the rise due to the rapid growth of mobile devices and applications, smart sensors and devices, cloud computing solutions, and citizen-facing portals. As digital information expands and becomes more complex, information management, processing, storage, security, and disposition become more complex as well. New capture, search, discovery, and analysis tools are helping organizations gain insights from their unstructured data. The government market is at a tipping point, realizing that information is a strategic asset, and government needs to protect, leverage, and analyze both structured and unstructured information to better serve and meet mission requirements. As government leaders strive to evolve data-driven organizations to successfully accomplish mission, they are laying the groundwork to correlate dependencies across events, people, processes, and information.
High-value government solutions will be created from a mashup of the most disruptive technologies:
- Mobile devices and applications
- Cloud services
- Social business technologies and networking
- Big Data and analytics
IDC predicts that by 2020, the IT industry will reach $5 trillion, approximately $1.7 trillion larger than today, and that 80% of the industry's growth will be driven by these 3rd Platform technologies. In the long term, these technologies will be key tools for dealing with the complexity of increased digital information. Big Data is one of the intelligent industry solutions and allows government to make better decisions by taking action based on patterns revealed by analyzing large volumes of data — related and unrelated, structured and unstructured.
But accomplishing these feats takes far more than simply accumulating massive quantities of data.“Making sense of thesevolumes of Big Datarequires cutting-edge tools and technologies that can analyze and extract useful knowledge from vast and diverse streams of information,” Tom Kalil and Fen Zhao of the White House Office of Science and Technology Policy wrote in a post on the OSTP Blog.
The White House took a step toward helping agencies find these technologies when it established the National Big Data Research and Development Initiative in 2012. The initiative included more than $200 million to make the most of the explosion of Big Data and the tools needed to analyze it.
The challenges that Big Data poses are nearly as daunting as its promise is encouraging. Storing data efficiently is one of these challenges. As always, budgets are tight, so agencies must minimize the per-megabyte price of storage and keep the data within easy access so that users can get it when they want it and how they need it. Backing up massive quantities of data heightens the challenge.
Analyzing the data effectively is another major challenge. Many agencies employ commercial tools that enable them to sift through the mountains of data, spotting trends that can help them operate more efficiently. (A recent study by MeriTalk found that federal IT executives think Big Data could help agencies save more than $500 billion while also fulfilling mission objectives.).
Custom-developed Big Data tools also are allowing agencies to address the need to analyze their data. For example, the Oak Ridge National Laboratory’s Computational Data Analytics Group has made its Piranha data analytics system available to other agencies. The system has helped medical researchers find a link that can alert doctors to aortic aneurysms before they strike. It’s also used for more mundane tasks, such as sifting through résumés to connect job candidates with hiring managers.
High-value government solutions will be created from a mashup of the most disruptive technologies:
- Mobile devices and applications
- Cloud services
- Social business technologies and networking
- Big Data and analytics
IDC predicts that by 2020, the IT industry will reach $5 trillion, approximately $1.7 trillion larger than today, and that 80% of the industry's growth will be driven by these 3rd Platform technologies. In the long term, these technologies will be key tools for dealing with the complexity of increased digital information. Big Data is one of the intelligent industry solutions and allows government to make better decisions by taking action based on patterns revealed by analyzing large volumes of data — related and unrelated, structured and unstructured.
But accomplishing these feats takes far more than simply accumulating massive quantities of data.“Making sense of thesevolumes of Big Datarequires cutting-edge tools and technologies that can analyze and extract useful knowledge from vast and diverse streams of information,” Tom Kalil and Fen Zhao of the White House Office of Science and Technology Policy wrote in a post on the OSTP Blog.
The White House took a step toward helping agencies find these technologies when it established the National Big Data Research and Development Initiative in 2012. The initiative included more than $200 million to make the most of the explosion of Big Data and the tools needed to analyze it.
The challenges that Big Data poses are nearly as daunting as its promise is encouraging. Storing data efficiently is one of these challenges. As always, budgets are tight, so agencies must minimize the per-megabyte price of storage and keep the data within easy access so that users can get it when they want it and how they need it. Backing up massive quantities of data heightens the challenge.
Analyzing the data effectively is another major challenge. Many agencies employ commercial tools that enable them to sift through the mountains of data, spotting trends that can help them operate more efficiently. (A recent study by MeriTalk found that federal IT executives think Big Data could help agencies save more than $500 billion while also fulfilling mission objectives.).
Custom-developed Big Data tools also are allowing agencies to address the need to analyze their data. For example, the Oak Ridge National Laboratory’s Computational Data Analytics Group has made its Piranha data analytics system available to other agencies. The system has helped medical researchers find a link that can alert doctors to aortic aneurysms before they strike. It’s also used for more mundane tasks, such as sifting through résumés to connect job candidates with hiring managers.
35小时
课程概览
概观
Communication服务提供商（CSP）面临着降低成本和最大化每用户平均收入（ARPU）的压力，同时确保出色的客户体验，但数据量不断增长。全球移动数据流量将以2016年的78％的复合年增长率（CAGR）增长，达到每月10.8艾字节。
同时，CSP正在生成大量数据，包括呼叫详细记录（CDR），网络数据和客户数据。充分利用这些数据的公司可以获得竞争优势。根据经济学人智库（Economist Intelligence Unit）最近的一项调查，使用数据导向决策的公司可以将生产率提高5-6％。然而，53％的公司仅利用其有价值数据的一半，而四分之一的受访者表示大量有用数据尚未开发。数据量非常大，无法进行手动分析，大多数传统软件系统无法跟上，导致丢弃或忽略有价值的数据。
借助Big Data ＆Analytics的高速，可扩展的大数据软件，CSP可以挖掘所有数据，以便在更短的时间内做出更好的决策。不同的Big Data产品和技术提供了一个端到端的软件平台，用于收集，准备，分析和呈现大数据的洞察力。应用领域包括网络性能监控，欺诈检测，客户流失检测和信用风险分析。 Big Data和分析产品可以扩展到处理数TB的数据，但是这些工具的实现需要新的基于云的数据库系统，如Hadoop或大规模并行计算处理器（KPU等）
本期针对Telco的Big Data BI课程涵盖了CSP为提高生产力和开辟新业务收入流而投资的所有新兴领域。该课程将提供完整的360度全方位视图，以便在Telco中查看Big Data BI，以便决策者和管理人员可以非常全面地了解Telco中Big Data BI的可能性，从而提高生产率和收益。
课程目标
该课程的主要目标是在电信Business （ Marketing /销售，网络运营，财务运营和客户关系Management ）的4个领域引入新的Big Data商业智能技术。学生将被介绍如下：
- 引入到Big Data -什么是4Vs（音量，速度，种类和准确性）在Big Data -产生-从电信透视提取和管理
- Big Data分析与旧数据分析的区别
- Big Data的内部理由-Telco观点
- Hadoop生态系统简介 - 熟悉所有Hadoop工具，如Hive ，Pig，SPARC，以及它们如何用于解决Big Data问题
- 如何提取Big Data来分析分析工具 - Business Analysis如何通过集成的Hadoop仪表板方法减少收集和分析数据的痛点
- 针对Telco的Insight分析，可视化分析和预测分析的基本介绍
- 客户流失分析和Big Data Big Data分析可以减少客户流失和客户对电信案例研究的不满
- 网络元数据和IPDR的网络故障和服务故障分析
- 销售和运营数据的财务分析 - 欺诈，浪费和ROI估算
- 客户获取问题 - 目标营销，客户细分和销售数据的交叉销售
- 所有Big Data分析产品的介绍和摘要以及它们适用于Telco分析空间的位置
- 结论 - 如何采用分步方法在您的组织中引入Big Data Business Intelligence
目标观众
- Telco CIO办公室的网络运营，财务经理，CRM经理和顶级IT经理。
- 电信Business分析师
- CFO办公室经理/分析师
- 运营经理
- 质量保证经理
Communication服务提供商（CSP）面临着降低成本和最大化每用户平均收入（ARPU）的压力，同时确保出色的客户体验，但数据量不断增长。全球移动数据流量将以2016年的78％的复合年增长率（CAGR）增长，达到每月10.8艾字节。
同时，CSP正在生成大量数据，包括呼叫详细记录（CDR），网络数据和客户数据。充分利用这些数据的公司可以获得竞争优势。根据经济学人智库（Economist Intelligence Unit）最近的一项调查，使用数据导向决策的公司可以将生产率提高5-6％。然而，53％的公司仅利用其有价值数据的一半，而四分之一的受访者表示大量有用数据尚未开发。数据量非常大，无法进行手动分析，大多数传统软件系统无法跟上，导致丢弃或忽略有价值的数据。
借助Big Data ＆Analytics的高速，可扩展的大数据软件，CSP可以挖掘所有数据，以便在更短的时间内做出更好的决策。不同的Big Data产品和技术提供了一个端到端的软件平台，用于收集，准备，分析和呈现大数据的洞察力。应用领域包括网络性能监控，欺诈检测，客户流失检测和信用风险分析。 Big Data和分析产品可以扩展到处理数TB的数据，但是这些工具的实现需要新的基于云的数据库系统，如Hadoop或大规模并行计算处理器（KPU等）
本期针对Telco的Big Data BI课程涵盖了CSP为提高生产力和开辟新业务收入流而投资的所有新兴领域。该课程将提供完整的360度全方位视图，以便在Telco中查看Big Data BI，以便决策者和管理人员可以非常全面地了解Telco中Big Data BI的可能性，从而提高生产率和收益。
课程目标
该课程的主要目标是在电信Business （ Marketing /销售，网络运营，财务运营和客户关系Management ）的4个领域引入新的Big Data商业智能技术。学生将被介绍如下：
- 引入到Big Data -什么是4Vs（音量，速度，种类和准确性）在Big Data -产生-从电信透视提取和管理
- Big Data分析与旧数据分析的区别
- Big Data的内部理由-Telco观点
- Hadoop生态系统简介 - 熟悉所有Hadoop工具，如Hive ，Pig，SPARC，以及它们如何用于解决Big Data问题
- 如何提取Big Data来分析分析工具 - Business Analysis如何通过集成的Hadoop仪表板方法减少收集和分析数据的痛点
- 针对Telco的Insight分析，可视化分析和预测分析的基本介绍
- 客户流失分析和Big Data Big Data分析可以减少客户流失和客户对电信案例研究的不满
- 网络元数据和IPDR的网络故障和服务故障分析
- 销售和运营数据的财务分析 - 欺诈，浪费和ROI估算
- 客户获取问题 - 目标营销，客户细分和销售数据的交叉销售
- 所有Big Data分析产品的介绍和摘要以及它们适用于Telco分析空间的位置
- 结论 - 如何采用分步方法在您的组织中引入Big Data Business Intelligence
目标观众
- Telco CIO办公室的网络运营，财务经理，CRM经理和顶级IT经理。
- 电信Business分析师
- CFO办公室经理/分析师
- 运营经理
- 质量保证经理
21小时
课程概览
Audience
If you try to make sense out of the data you have access to or want to analyse unstructured data available on the net (like Twitter, Linked in, etc...) this course is for you.
It is mostly aimed at decision makers and people who need to choose what data is worth collecting and what is worth analyzing.
It is not aimed at people configuring the solution, those people will benefit from the big picture though.
Delivery Mode
During the course delegates will be presented with working examples of mostly open source technologies.
Short lectures will be followed by presentation and simple exercises by the participants
Content and Software used
All software used is updated each time the course is run, so we check the newest versions possible.
It covers the process from obtaining, formatting, processing and analysing the data, to explain how to automate decision making process with machine learning.
If you try to make sense out of the data you have access to or want to analyse unstructured data available on the net (like Twitter, Linked in, etc...) this course is for you.
It is mostly aimed at decision makers and people who need to choose what data is worth collecting and what is worth analyzing.
It is not aimed at people configuring the solution, those people will benefit from the big picture though.
Delivery Mode
During the course delegates will be presented with working examples of mostly open source technologies.
Short lectures will be followed by presentation and simple exercises by the participants
Content and Software used
All software used is updated each time the course is run, so we check the newest versions possible.
It covers the process from obtaining, formatting, processing and analysing the data, to explain how to automate decision making process with machine learning.
35小时
课程概览
Day 1 - provides a high-level overview of essential Big Data topic areas. The module is divided into a series of sections, each of which is accompanied by a hands-on exercise.
Day 2 - explores a range of topics that relate analysis practices and tools for Big Data environments. It does not get into implementation or programming details, but instead keeps coverage at a conceptual level, focusing on topics that enable participants to develop a comprehensive understanding of the common analysis functions and features offered by Big Data solutions.
Day 3 - provides an overview of the fundamental and essential topic areas relating to Big Data solution platform architecture. It covers Big Data mechanisms required for the development of a Big Data solution platform and architectural options for assembling a data processing platform. Common scenarios are also presented to provide a basic understanding of how a Big Data solution platform is generally used.
Day 4 - builds upon Day 3 by exploring advanced topics relatng to Big Data solution platform architecture. In particular, different architectural layers that make up the Big Data solution platform are introduced and discussed, including data sources, data ingress, data storage, data processing and security.
Day 5 - covers a number of exercises and problems designed to test the delegates ability to apply knowledge of topics covered Day 3 and 4.
Day 2 - explores a range of topics that relate analysis practices and tools for Big Data environments. It does not get into implementation or programming details, but instead keeps coverage at a conceptual level, focusing on topics that enable participants to develop a comprehensive understanding of the common analysis functions and features offered by Big Data solutions.
Day 3 - provides an overview of the fundamental and essential topic areas relating to Big Data solution platform architecture. It covers Big Data mechanisms required for the development of a Big Data solution platform and architectural options for assembling a data processing platform. Common scenarios are also presented to provide a basic understanding of how a Big Data solution platform is generally used.
Day 4 - builds upon Day 3 by exploring advanced topics relatng to Big Data solution platform architecture. In particular, different architectural layers that make up the Big Data solution platform are introduced and discussed, including data sources, data ingress, data storage, data processing and security.
Day 5 - covers a number of exercises and problems designed to test the delegates ability to apply knowledge of topics covered Day 3 and 4.
21小时
课程概览
Big Data is a term that refers to solutions destined for storing and processing large data sets. Developed by Google initially, these Big Data solutions have evolved and inspired other similar projects, many of which are available as open-source. R is a popular programming language in the financial industry.
14小时
课程概览
When traditional storage technologies don't handle the amount of data you need to store there are hundereds of alternatives. This course try to guide the participants what are alternatives for storing and analyzing Big Data and what are theirs pros and cons.
This course is mostly focused on discussion and presentation of solutions, though hands-on exercises are available on demand.
This course is mostly focused on discussion and presentation of solutions, though hands-on exercises are available on demand.
14小时
课程概览
The course is part of the Data Scientist skill set (Domain: Data and Technology).
35小时
课程概览
Big data is data sets that are so voluminous and complex that traditional data processing application software are inadequate to deal with them. Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating and information privacy.
35小时
课程概览
参与者完成此次培训后，将会对大数据及其相关技术、方法、工具有一个实际和真实的理解。
参与者将有机会通过动手练习将这些知识付诸实践。小组互动和讲师反馈是课堂的重要组成部分。
本课程首先介绍大数据的基本概念，然后讲解用于执行数据分析的编程语言和方法，最后我们会讨论可启用大数据存储、分布式处理及可扩展性的工具和基础架构。
受众
- 开发人员/程序员
- IT顾问
课程形式
- 部分讲座、部分讨论、实操、偶尔测评进度
参与者将有机会通过动手练习将这些知识付诸实践。小组互动和讲师反馈是课堂的重要组成部分。
本课程首先介绍大数据的基本概念，然后讲解用于执行数据分析的编程语言和方法，最后我们会讨论可启用大数据存储、分布式处理及可扩展性的工具和基础架构。
受众
- 开发人员/程序员
- IT顾问
课程形式
- 部分讲座、部分讨论、实操、偶尔测评进度
14小时
课程概览
Vespa is an open-source big data processing and serving engine created by Yahoo. It is used to respond to user queries, make recommendations, and provide personalized content and advertisements in real-time.
This instructor-led, live training introduces the challenges of serving large-scale data and walks participants through the creation of an application that can compute responses to user requests, over large datasets in real-time.
By the end of this training, participants will be able to:
- Use Vespa to quickly compute data (store, search, rank, organize) at serving time while a user waits
- Implement Vespa into existing applications involving feature search, recommendations, and personalization
- Integrate and deploy Vespa with existing big data systems such as Hadoop and Storm.
Audience
- Developers
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
This instructor-led, live training introduces the challenges of serving large-scale data and walks participants through the creation of an application that can compute responses to user requests, over large datasets in real-time.
By the end of this training, participants will be able to:
- Use Vespa to quickly compute data (store, search, rank, organize) at serving time while a user waits
- Implement Vespa into existing applications involving feature search, recommendations, and personalization
- Integrate and deploy Vespa with existing big data systems such as Hadoop and Storm.
Audience
- Developers
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
14小时
课程概览
To meet compliance of the regulators, CSPs (Communication service providers) can tap into Big Data Analytics which not only help them to meet compliance but within the scope of same project they can increase customer satisfaction and thus reduce the churn. In fact since compliance is related to Quality of service tied to a contract, any initiative towards meeting the compliance, will improve the “competitive edge” of the CSPs. Therefore, it is important that Regulators should be able to advise/guide a set of Big Data analytic practice for CSPs that will be of mutual benefit between the regulators and CSPs.
The course consists of 8 modules (4 on day 1, and 4 on day 2)
The course consists of 8 modules (4 on day 1, and 4 on day 2)
35小时
课程概览
Advances in technologies and the increasing amount of information are transforming how law enforcement is conducted. The challenges that Big Data pose are nearly as daunting as Big Data's promise. Storing data efficiently is one of these challenges; effectively analyzing it is another.
In this instructor-led, live training, participants will learn the mindset with which to approach Big Data technologies, assess their impact on existing processes and policies, and implement these technologies for the purpose of identifying criminal activity and preventing crime. Case studies from law enforcement organizations around the world will be examined to gain insights on their adoption approaches, challenges and results.
By the end of this training, participants will be able to:
- Combine Big Data technology with traditional data gathering processes to piece together a story during an investigation
- Implement industrial big data storage and processing solutions for data analysis
- Prepare a proposal for the adoption of the most adequate tools and processes for enabling a data-driven approach to criminal investigation
Audience
- Law Enforcement specialists with a technical background
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn the mindset with which to approach Big Data technologies, assess their impact on existing processes and policies, and implement these technologies for the purpose of identifying criminal activity and preventing crime. Case studies from law enforcement organizations around the world will be examined to gain insights on their adoption approaches, challenges and results.
By the end of this training, participants will be able to:
- Combine Big Data technology with traditional data gathering processes to piece together a story during an investigation
- Implement industrial big data storage and processing solutions for data analysis
- Prepare a proposal for the adoption of the most adequate tools and processes for enabling a data-driven approach to criminal investigation
Audience
- Law Enforcement specialists with a technical background
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
14小时
课程概览
This classroom based training session will explore Big Data. Delegates will have computer based examples and case study exercises to undertake with relevant big data tools
14小时
课程概览
Objective : This training course aims at helping attendees understand why Big Data is changing our lives and how it is altering the way businesses see us as consumers. Indeed, users of big data in businesses find that big data unleashes a wealth of information and insights which translate to higher profits, reduced costs, and less risk. However, the downside was frustration sometimes when putting too much emphasis on individual technologies and not enough focus on the pillars of big data management.
Attendees will learn during this course how to manage the big data using its three pillars of data integration, data governance and data security in order to turn big data into real business value. Different exercices conducted on a case study of customer management will help attendees to better understand the underlying processes.
Attendees will learn during this course how to manage the big data using its three pillars of data integration, data governance and data security in order to turn big data into real business value. Different exercices conducted on a case study of customer management will help attendees to better understand the underlying processes.
7小时
课程概览
这一由讲师指导的实时培训（现场或远程）面向的是希望学习如何实施机器学习策略同时最大限度地利用大数据的技术人员。
在本次培训结束后，学员将能够：
- 了解机器学习的发展和趋势。
- 了解机器学习如何在不同行业中使用。
- 熟悉在组织内实现机器学习的工具、技能、服务。
- 了解机器学习如何用于增强数据挖掘和分析。
- 了解数据中台是什么，以及企业如何使用它。
- 了解大数据和智能应用程序在各个行业中的作用。
课程形式
- 互动讲座和讨论。
- 大量练习和实操。
- 在现场实验室环境中动手实现。
课程自定义选项
- 如需本课程的定制培训，请联系我们以作安排。
在本次培训结束后，学员将能够：
- 了解机器学习的发展和趋势。
- 了解机器学习如何在不同行业中使用。
- 熟悉在组织内实现机器学习的工具、技能、服务。
- 了解机器学习如何用于增强数据挖掘和分析。
- 了解数据中台是什么，以及企业如何使用它。
- 了解大数据和智能应用程序在各个行业中的作用。
课程形式
- 互动讲座和讨论。
- 大量练习和实操。
- 在现场实验室环境中动手实现。
课程自定义选项
- 如需本课程的定制培训，请联系我们以作安排。
7小时
课程概览
This instructor-led, live training in 中国 (online or onsite) is aimed at software engineers who wish to use Sqoop and Flume for big data.
By the end of this training, participants will be able to:
- Ingest big data with Sqoop and Flume.
- Ingest data from multiple data sources.
- Move data from relational databases to HDFS and Hive.
- Export data from HDFS to a relational database.
By the end of this training, participants will be able to:
- Ingest big data with Sqoop and Flume.
- Ingest data from multiple data sources.
- Move data from relational databases to HDFS and Hive.
- Export data from HDFS to a relational database.
28小时
课程概览
This instructor-led, live training in 中国 (online or onsite) is aimed at technical persons who wish to deploy Talend Open Studio for Big Data to simplifying the process of reading and crunching through Big Data.
By the end of this training, participants will be able to:
- Install and configure Talend Open Studio for Big Data.
- Connect with Big Data systems such as Cloudera, HortonWorks, MapR, Amazon EMR and Apache.
- Understand and set up Open Studio's big data components and connectors.
- Configure parameters to automatically generate MapReduce code.
- Use Open Studio's drag-and-drop interface to run Hadoop jobs.
- Prototype big data pipelines.
- Automate big data integration projects.
By the end of this training, participants will be able to:
- Install and configure Talend Open Studio for Big Data.
- Connect with Big Data systems such as Cloudera, HortonWorks, MapR, Amazon EMR and Apache.
- Understand and set up Open Studio's big data components and connectors.
- Configure parameters to automatically generate MapReduce code.
- Use Open Studio's drag-and-drop interface to run Hadoop jobs.
- Prototype big data pipelines.
- Automate big data integration projects.
21小时
课程概览
The course is dedicated to IT specialists that are looking for a solution to store and process large data sets in distributed system environment
Course goal:
Getting knowledge regarding Hadoop cluster administration
Course goal:
Getting knowledge regarding Hadoop cluster administration
35小时
课程概览
Audience:
The course is intended for IT specialists looking for a solution to store and process large data sets in a distributed system environment
Goal:
Deep knowledge on Hadoop cluster administration.
The course is intended for IT specialists looking for a solution to store and process large data sets in a distributed system environment
Goal:
Deep knowledge on Hadoop cluster administration.
28小时
课程概览
Audience:
This course is intended to demystify big data/hadoop technology and to show it is not difficult to understand.
This course is intended to demystify big data/hadoop technology and to show it is not difficult to understand.
28小时
课程概览
Apache Hadoop is the most popular framework for processing Big Data on clusters of servers. This course will introduce a developer to various components (HDFS, MapReduce, Pig, Hive and HBase) Hadoop ecosystem.
21小时
课程概览
Apache Hadoop is one of the most popular frameworks for processing Big Data on clusters of servers. This course delves into data management in HDFS, advanced Pig, Hive, and HBase. These advanced programming techniques will be beneficial to experienced Hadoop developers.
Audience: developers
Duration: three days
Format: lectures (50%) and hands-on labs (50%).
Audience: developers
Duration: three days
Format: lectures (50%) and hands-on labs (50%).
21小时
课程概览
This course introduces HBase – a NoSQL store on top of Hadoop. The course is intended for developers who will be using HBase to develop applications, and administrators who will manage HBase clusters.
We will walk a developer through HBase architecture and data modelling and application development on HBase. It will also discuss using MapReduce with HBase, and some administration topics, related to performance optimization. The course is very hands-on with lots of lab exercises.
Duration : 3 days
Audience : Developers & Administrators
We will walk a developer through HBase architecture and data modelling and application development on HBase. It will also discuss using MapReduce with HBase, and some administration topics, related to performance optimization. The course is very hands-on with lots of lab exercises.
Duration : 3 days
Audience : Developers & Administrators
21小时
课程概览
Apache Hadoop is the most popular framework for processing Big Data on clusters of servers. In this three (optionally, four) days course, attendees will learn about the business benefits and use cases for Hadoop and its ecosystem, how to plan cluster deployment and growth, how to install, maintain, monitor, troubleshoot and optimize Hadoop. They will also practice cluster bulk data load, get familiar with various Hadoop distributions, and practice installing and managing Hadoop ecosystem tools. The course finishes off with discussion of securing cluster with Kerberos.
“…The materials were very well prepared and covered thoroughly. The Lab was very helpful and well organized”
— Andrew Nguyen, Principal Integration DW Engineer, Microsoft Online Advertising
Audience
Hadoop administrators
Format
Lectures and hands-on labs, approximate balance 60% lectures, 40% labs.
“…The materials were very well prepared and covered thoroughly. The Lab was very helpful and well organized”
— Andrew Nguyen, Principal Integration DW Engineer, Microsoft Online Advertising
Audience
Hadoop administrators
Format
Lectures and hands-on labs, approximate balance 60% lectures, 40% labs.
21小时
课程概览
Apache Hadoop is the most popular framework for processing Big Data. Hadoop provides rich and deep analytics capability, and it is making in-roads in to tradional BI analytics world. This course will introduce an analyst to the core components of Hadoop eco system and its analytics
Audience
Business Analysts
Duration
three days
Format
Lectures and hands on labs.
Audience
Business Analysts
Duration
three days
Format
Lectures and hands on labs.
21小时
课程概览
Hadoop is the most popular Big Data processing framework.
14小时
课程概览
Audience
- Developers
Format of the Course
- Lectures, hands-on practice, small tests along the way to gauge understanding
- Developers
Format of the Course
- Lectures, hands-on practice, small tests along the way to gauge understanding
21小时
课程概览
This course is intended for developers, architects, data scientists or any profile that requires access to data either intensively or on a regular basis.
The major focus of the course is data manipulation and transformation.
Among the tools in the Hadoop ecosystem this course includes the use of Pig and Hive both of which are heavily used for data transformation and manipulation.
This training also addresses performance metrics and performance optimisation.
The course is entirely hands on and is punctuated by presentations of the theoretical aspects.
The major focus of the course is data manipulation and transformation.
Among the tools in the Hadoop ecosystem this course includes the use of Pig and Hive both of which are heavily used for data transformation and manipulation.
This training also addresses performance metrics and performance optimisation.
The course is entirely hands on and is punctuated by presentations of the theoretical aspects.
14小时
课程概览
随着越来越多的软件和IT项目从本地处理和数据管理转向分布式处理和大数据存储，项目经理们正在意识到需要升级他们的知识和技能，以掌握与大数据项目和机会相关的概念和实践。
本课程将向项目经理介绍当下最流行的大数据处理框架：Hadoop。
在这一由讲师引导的培训中，学员将学习Hadoop生态系统的核心组件，以及这些技术如何用于解决大规模问题。在学习这些基础的过程中，学员还将提高他们与这些系统的开发人员和实施人员以及许多IT项目涉及的数据科学家和分析师沟通的能力。
受众
- 希望将Hadoop应用到其现有开发或IT基础架构中的项目经理
- 需要与包括大数据工程师、数据科学家和业务分析师在内的跨职能团队进行沟通的项目经理
课程形式
- 部分讲座、部分讨论、练习和大量实操
本课程将向项目经理介绍当下最流行的大数据处理框架：Hadoop。
在这一由讲师引导的培训中，学员将学习Hadoop生态系统的核心组件，以及这些技术如何用于解决大规模问题。在学习这些基础的过程中，学员还将提高他们与这些系统的开发人员和实施人员以及许多IT项目涉及的数据科学家和分析师沟通的能力。
受众
- 希望将Hadoop应用到其现有开发或IT基础架构中的项目经理
- 需要与包括大数据工程师、数据科学家和业务分析师在内的跨职能团队进行沟通的项目经理
课程形式
- 部分讲座、部分讨论、练习和大量实操
14小时
课程概览
Apache Samza is an open-source near-realtime, asynchronous computational framework for stream processing. It uses Apache Kafka for messaging, and Apache Hadoop YARN for fault tolerance, processor isolation, security, and resource management.
This instructor-led, live training introduces the principles behind messaging systems and distributed stream processing, while walking participants through the creation of a sample Samza-based project and job execution.
By the end of this training, participants will be able to:
- Use Samza to simplify the code needed to produce and consume messages.
- Decouple the handling of messages from an application.
- Use Samza to implement near-realtime asynchronous computation.
- Use stream processing to provide a higher level of abstraction over messaging systems.
Audience
- Developers
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
This instructor-led, live training introduces the principles behind messaging systems and distributed stream processing, while walking participants through the creation of a sample Samza-based project and job execution.
By the end of this training, participants will be able to:
- Use Samza to simplify the code needed to produce and consume messages.
- Decouple the handling of messages from an application.
- Use Samza to implement near-realtime asynchronous computation.
- Use stream processing to provide a higher level of abstraction over messaging systems.
Audience
- Developers
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
近期大数据培训课程
18-11-2021 09:30
上海 - 上海中区广场
14 小时
13-12-2021 09:30
上海 - 上海中区广场
14 小时
13-12-2021 09:30
上海 - 上海中区广场
14 小时
15-12-2021 09:30
上海 - 上海中区广场
21 小时
20-12-2021 09:30
上海 - 上海中区广场
28 小时
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