In this instructor-led, live training in 中国, participants will learn how to use Python and Spark together to analyze big data as they work on hands-on exercises.
By the end of this training, participants will be able to:
Learn how to use Spark with Python to analyze Big Data.
Work on exercises that mimic real world cases.
Use different tools and techniques for big data analysis using PySpark.
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
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.
Talend Open Studio for Big Data 是用于大数据处理的开源 ETL 工具。 它包括一个发展环境与 Big Data 来源和目标互动,并运行工作,而无需编写代码。
由教练领导,现场培训(在线或在线)旨在技术人员谁希望部署 Talend Open Studio for Big Data 以简化阅读和阅读过程通过 Big Data 。
安装和设置 Talend Open Studio for Big Data.
连接到(0)系统,如Cloudera、HortonWorks、MapR、Amazon EMR 和 Apache。
了解并设置 Open Studio 的大数据组件和连接器。
设置参数以自动生成 MapReduce 代码。
使用 Open Studio's drag-and-drop 界面运行 Hadoop 工作。
The course is dedicated to IT specialists that are looking for a solution to store and process large data sets in distributed system environment
Getting knowledge regarding Hadoop cluster administration
大数据,培训,课程,培训课程, 企业大数据培训, 短期大数据培训, 大数据课程, 大数据周末培训, 大数据晚上培训, 大数据训练, 学习大数据, 大数据老师, 学大数据班, 大数据远程教育, 一对一大数据课程, 小组大数据课程, 大数据培训师, 大数据辅导班, 大数据教程, 大数据私教, 大数据辅导, 大数据讲师Big Data,培训,课程,培训课程, 企业Big Data培训, 短期Big Data培训, Big Data课程, Big Data周末培训, Big Data晚上培训, Big Data训练, 学习Big Data, Big Data老师, 学Big Data班, Big Data远程教育, 一对一Big Data课程, 小组Big Data课程, Big Data培训师, Big Data辅导班, Big Data教程, Big Data私教, Big Data辅导, Big Data讲师