本地，有指导的Apache Spark培训课程通过Handson实践演示Spark如何适应大数据生态系统，以及如何使用Spark进行数据分析。 Apache Spark培训可作为“现场实时培训”或“远程实时培训”。现场实地培训可在当地客户现场进行中国或者在NobleProg公司的培训中心中国 。远程实时培训通过交互式远程桌面进行。 NobleProg您当地的培训提供商。
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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
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
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我们对整个环境了解得更多。
John Kidd
课程: Spark for Developers
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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
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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
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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
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这是我用过的最好的动手编程课程之一。
Laura Kahn
课程: Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP
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这是我13年职业生涯中最优秀的在线培训之一。保持伟大的工作！
课程: Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP
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当我们想要提出关于不在课程提纲上的事情的半相关问题时，理查德非常愿意离题。解释很清楚，他提出的任何建议都是关于警告的。
ARM Limited
课程: Spark for Developers
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我非常喜欢的虚拟机 老师对这个话题以及其他话题都很了解, 他很好, 也很友好。 我喜欢迪拜的设施。
Safar Alqahtani - Elm Information Security
课程: Big Data Analytics in Health
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练习任务
Pawel Kozikowski - GE Medical Systems Polska Sp. Zoo
课程: Python and Spark for Big Data (PySpark)
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Ajay非常友好，乐于助人，也对他正在讨论的话题有所了解。
Biniam Guulay - ICE International Copyright Enterprise Germany GmbH
课程: Spark for Developers
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实验练习。从随后几天的第一天开始应用该理论。
Dell
课程: A Practical Introduction to Stream Processing
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老师已根据我们当前的需求调整了培训计划。
EduBroker Sp. z o.o.
课程: Python and Spark for Big Data (PySpark)
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以不同的方式进行类似的练习确实有助于理解每个组件（ Hadoop / Spark，独立/集群）可以自己以及一起做什么。它为我提供了有关在开发时以及在群集上部署应用程序时应如何在本地计算机上测试应用程序的想法。
Thomas Carcaud - IT Frankfurt GmbH
课程: Spark for Developers
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个人关注。
ARCHANA ANILKUMAR - PPL
课程: Python and Spark for Big Data (PySpark)
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去学习火花流，databricks和奥斯红移
Lim Meng Tee - Jobstreet.com Shared Services Sdn. Bhd.
课程: Apache Spark in the Cloud
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内容和知识。
Jobstreet.com Shared Services Sdn. Bhd.
课程: Apache Spark in the Cloud
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这是非常丰富的。 我有很少的经验与火花之前和迄今为止本课程提供了一个很好的介绍标的。
Intelligent Medical Objects
课程: Apache Spark in the Cloud
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了解火花的罩子下发生了什么，这太棒了。了解引擎盖下发生了什么有助于更好地了解您的代码为什么正在执行或没有执行您期望它执行的。很多培训都是动手的， 这总是伟大的， 关于优化的部分是特别相关的， 我目前的工作， 这是很好的。
Intelligent Medical Objects
课程: Apache Spark in the Cloud
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这是一个伟大的班级！ 我最喜欢的安德拉什介绍了很清楚什么火花是所有有关，在那里它来自，并有什么问题，它是能够解决。 我比其他一些介绍更好，我看到这只是融入如何使用它。 安德拉斯对这个话题有深刻的知识，并解释了事情。
Intelligent Medical Objects
课程: Apache Spark in the Cloud
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现场例子是给定和显示的基本方面的火花。
Intelligent Medical Objects
课程: Apache Spark in the Cloud
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1 \。 高层次概念与技术细节之间的平衡。 安德拉斯对他的教学非常有开发。 运动
Steven Wu - Intelligent Medical Objects
课程: Apache Spark in the Cloud
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手持会议/分配
Poornima Chenthamarakshan - Intelligent Medical Objects
课程: Apache Spark in the Cloud
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教练根据观众要求稍微调整训练，所以我们有一些光临一些争议主题，我们要求
Intelligent Medical Objects
课程: Apache Spark in the Cloud
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他的速度，是伟大的。 我喜欢这个事实，他也理解理论，所以我明白为什么我会做他正在问的事情。
Intelligent Medical Objects
课程: Apache Spark in the Cloud
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
这是我13年职业生涯中最优秀的在线培训之一。保持伟大的工作！
课程: Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP
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Spark子类别
Apache Spark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
21小时
课程概览
OBJECTIVE:
This course will introduce Apache Spark. The students will learn how Spark fits into the Big Data ecosystem, and how to use Spark for data analysis. The course covers Spark shell for interactive data analysis, Spark internals, Spark APIs, Spark SQL, Spark streaming, and machine learning and graphX.
AUDIENCE :
Developers / Data Analysts
This course will introduce Apache Spark. The students will learn how Spark fits into the Big Data ecosystem, and how to use Spark for data analysis. The course covers Spark shell for interactive data analysis, Spark internals, Spark APIs, Spark SQL, Spark streaming, and machine learning and graphX.
AUDIENCE :
Developers / Data Analysts
21小时
课程概览
This instructor-led, live training in (online or onsite) introduces Hortonworks Data Platform (HDP) and walks participants through the deployment of Spark + Hadoop solution.
By the end of this training, participants will be able to:
- Use Hortonworks to reliably run Hadoop at a large scale.
- Unify Hadoop's security, governance, and operations capabilities with Spark's agile analytic workflows.
- Use Hortonworks to investigate, validate, certify and support each of the components in a Spark project.
- Process different types of data, including structured, unstructured, in-motion, and at-rest.
By the end of this training, participants will be able to:
- Use Hortonworks to reliably run Hadoop at a large scale.
- Unify Hadoop's security, governance, and operations capabilities with Spark's agile analytic workflows.
- Use Hortonworks to investigate, validate, certify and support each of the components in a Spark project.
- Process different types of data, including structured, unstructured, in-motion, and at-rest.
14小时
课程概览
Magellan is an open-source distributed execution engine for geospatial analytics on big data. Implemented on top of Apache Spark, it extends Spark SQL and provides a relational abstraction for geospatial analytics.
This instructor-led, live training introduces the concepts and approaches for implementing geospacial analytics and walks participants through the creation of a predictive analysis application using Magellan on Spark.
By the end of this training, participants will be able to:
- Efficiently query, parse and join geospatial datasets at scale
- Implement geospatial data in business intelligence and predictive analytics applications
- Use spatial context to extend the capabilities of mobile devices, sensors, logs, and wearables
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
This instructor-led, live training introduces the concepts and approaches for implementing geospacial analytics and walks participants through the creation of a predictive analysis application using Magellan on Spark.
By the end of this training, participants will be able to:
- Efficiently query, parse and join geospatial datasets at scale
- Implement geospatial data in business intelligence and predictive analytics applications
- Use spatial context to extend the capabilities of mobile devices, sensors, logs, and wearables
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
7小时
课程概览
Alluxio is an open-source virtual distributed storage system that unifies disparate storage systems and enables applications to interact with data at memory speed. It is used by companies such as Intel, Baidu and Alibaba.
In this instructor-led, live training, participants will learn how to use Alluxio to bridge different computation frameworks with storage systems and efficiently manage multi-petabyte scale data as they step through the creation of an application with Alluxio.
By the end of this training, participants will be able to:
- Develop an application with Alluxio
- Connect big data systems and applications while preserving one namespace
- Efficiently extract value from big data in any storage format
- Improve workload performance
- Deploy and manage Alluxio standalone or clustered
Audience
- Data scientist
- Developer
- System administrator
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn how to use Alluxio to bridge different computation frameworks with storage systems and efficiently manage multi-petabyte scale data as they step through the creation of an application with Alluxio.
By the end of this training, participants will be able to:
- Develop an application with Alluxio
- Connect big data systems and applications while preserving one namespace
- Efficiently extract value from big data in any storage format
- Improve workload performance
- Deploy and manage Alluxio standalone or clustered
Audience
- Data scientist
- Developer
- System administrator
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
7小时
课程概览
Spark SQL is Apache Spark's module for working with structured and unstructured data. Spark SQL provides information about the structure of the data as well as the computation being performed. This information can be used to perform optimizations. Two common uses for Spark SQL are:
- to execute SQL queries.
- to read data from an existing Hive installation.
In this instructor-led, live training (onsite or remote), participants will learn how to analyze various types of data sets using Spark SQL.
By the end of this training, participants will be able to:
- Install and configure Spark SQL.
- Perform data analysis using Spark SQL.
- Query data sets in different formats.
- Visualize data and query results.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
- to execute SQL queries.
- to read data from an existing Hive installation.
In this instructor-led, live training (onsite or remote), participants will learn how to analyze various types of data sets using Spark SQL.
By the end of this training, participants will be able to:
- Install and configure Spark SQL.
- Perform data analysis using Spark SQL.
- Query data sets in different formats.
- Visualize data and query results.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
21小时
课程概览
In this instructor-led, live training in 中国 (onsite or remote), participants will learn how to set up and integrate different Stream Processing frameworks with existing big data storage systems and related software applications and microservices.
By the end of this training, participants will be able to:
- Install and configure different Stream Processing frameworks, such as Spark Streaming and Kafka Streaming.
- Understand and select the most appropriate framework for the job.
- Process of data continuously, concurrently, and in a record-by-record fashion.
- Integrate Stream Processing solutions with existing databases, data warehouses, data lakes, etc.
- Integrate the most appropriate stream processing library with enterprise applications and microservices.
By the end of this training, participants will be able to:
- Install and configure different Stream Processing frameworks, such as Spark Streaming and Kafka Streaming.
- Understand and select the most appropriate framework for the job.
- Process of data continuously, concurrently, and in a record-by-record fashion.
- Integrate Stream Processing solutions with existing databases, data warehouses, data lakes, etc.
- Integrate the most appropriate stream processing library with enterprise applications and microservices.
21小时
课程概览
Big data analytics involves the process of examining large amounts of varied data sets in order to uncover correlations, hidden patterns, and other useful insights.
The health industry has massive amounts of complex heterogeneous medical and clinical data. Applying big data analytics on health data presents huge potential in deriving insights for improving delivery of healthcare. However, the enormity of these datasets poses great challenges in analyses and practical applications to a clinical environment.
In this instructor-led, live training (remote), participants will learn how to perform big data analytics in health as they step through a series of hands-on live-lab exercises.
By the end of this training, participants will be able to:
- Install and configure big data analytics tools such as Hadoop MapReduce and Spark
- Understand the characteristics of medical data
- Apply big data techniques to deal with medical data
- Study big data systems and algorithms in the context of health applications
Audience
- Developers
- Data Scientists
Format of the Course
- Part lecture, part discussion, exercises and heavy hands-on practice.
Note
- To request a customized training for this course, please contact us to arrange.
The health industry has massive amounts of complex heterogeneous medical and clinical data. Applying big data analytics on health data presents huge potential in deriving insights for improving delivery of healthcare. However, the enormity of these datasets poses great challenges in analyses and practical applications to a clinical environment.
In this instructor-led, live training (remote), participants will learn how to perform big data analytics in health as they step through a series of hands-on live-lab exercises.
By the end of this training, participants will be able to:
- Install and configure big data analytics tools such as Hadoop MapReduce and Spark
- Understand the characteristics of medical data
- Apply big data techniques to deal with medical data
- Study big data systems and algorithms in the context of health applications
Audience
- Developers
- Data Scientists
Format of the Course
- Part lecture, part discussion, exercises and heavy hands-on practice.
Note
- To request a customized training for this course, please contact us to arrange.
21小时
课程概览
Apache Spark's learning curve is slowly increasing at the begining, it needs a lot of effort to get the first return. This course aims to jump through the first tough part. After taking this course the participants will understand the basics of Apache Spark , they will clearly differentiate RDD from DataFrame, they will learn Python and Scala API, they will understand executors and tasks, etc. Also following the best practices, this course strongly focuses on cloud deployment, Databricks and AWS. The students will also understand the differences between AWS EMR and AWS Glue, one of the lastest Spark service of AWS.
AUDIENCE:
Data Engineer, DevOps, Data Scientist
AUDIENCE:
Data Engineer, DevOps, Data Scientist
21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 stream big data with Spark Streaming and Scala.
By the end of this training, participants will be able to:
- Create Spark applications with the Scala programming language.
- Use Spark Streaming to process continuous streams of data.
- Process streams of real-time data with Spark Streaming.
By the end of this training, participants will be able to:
- Create Spark applications with the Scala programming language.
- Use Spark Streaming to process continuous streams of data.
- Process streams of real-time data with Spark Streaming.
14小时
课程概览
This instructor-led, live training in 中国 (online or onsite) is aimed at data scientists who wish to use the SMACK stack to build data processing platforms for big data solutions.
By the end of this training, participants will be able to:
- Implement a data pipeline architecture for processing big data.
- Develop a cluster infrastructure with Apache Mesos and Docker.
- Analyze data with Spark and Scala.
- Manage unstructured data with Apache Cassandra.
By the end of this training, participants will be able to:
- Implement a data pipeline architecture for processing big data.
- Develop a cluster infrastructure with Apache Mesos and Docker.
- Analyze data with Spark and Scala.
- Manage unstructured data with Apache Cassandra.
21小时
课程概览
This instructor-led, live training in 中国 (online or onsite) is aimed at engineers who wish to set up and deploy Apache Spark system for processing very large amounts of data.
By the end of this training, participants will be able to:
- Install and configure Apache Spark.
- Quickly process and analyze very large data sets.
- Understand the difference between Apache Spark and Hadoop MapReduce and when to use which.
- Integrate Apache Spark with other machine learning tools.
By the end of this training, participants will be able to:
- Install and configure Apache Spark.
- Quickly process and analyze very large data sets.
- Understand the difference between Apache Spark and Hadoop MapReduce and when to use which.
- Integrate Apache Spark with other machine learning tools.
21小时
课程概览
This instructor-led, live training in 中国 (online or onsite) is aimed at developers who wish to carry out big data analysis using Apache Spark in their .NET applications.
By the end of this training, participants will be able to:
- Install and configure Apache Spark.
- Understand how .NET implements Spark APIs so that they can be accessed from a .NET application.
- Develop data processing applications using C# or F#, capable of handling data sets whose size is measured in terabytes and pedabytes.
- Develop machine learning features for a .NET application using Apache Spark capabilities.
- Carry out exploratory analysis using SQL queries on big data sets.
By the end of this training, participants will be able to:
- Install and configure Apache Spark.
- Understand how .NET implements Spark APIs so that they can be accessed from a .NET application.
- Develop data processing applications using C# or F#, capable of handling data sets whose size is measured in terabytes and pedabytes.
- Develop machine learning features for a .NET application using Apache Spark capabilities.
- Carry out exploratory analysis using SQL queries on big data sets.
35小时
课程概览
MLlib is Spark’s machine learning (ML) library. Its goal is to make practical machine learning scalable and easy. It consists of common learning algorithms and utilities, including classification, regression, clustering, collaborative filtering, dimensionality reduction, as well as lower-level optimization primitives and higher-level pipeline APIs.
It divides into two packages:
-
spark.mllib contains the original API built on top of RDDs.
-
spark.ml provides higher-level API built on top of DataFrames for constructing ML pipelines.
Audience
This course is directed at engineers and developers seeking to utilize a built in Machine Library for Apache Spark
It divides into two packages:
-
spark.mllib contains the original API built on top of RDDs.
-
spark.ml provides higher-level API built on top of DataFrames for constructing ML pipelines.
Audience
This course is directed at engineers and developers seeking to utilize a built in Machine Library for Apache Spark
21小时
课程概览
This course is aimed at developers and data scientists who wish to understand and implement AI within their applications. Special focus is given to Data Analysis, Distributed AI and NLP.
28小时
课程概览
大量现实问题可以用图形来描述。例如，Web图形、社交网络图形、火车网络图形、语言图形。这些图形往往太大，处理它们需要一组专门的工具和流程——这些工具和流程可以称为图形计算（也称为图形分析）。
在这一由讲师指导的实时培训中，学员将学习处理图形数据的技术产品和实施方法。目的是识别真实世界里的对象、它们的特征和关系，然后使用图形计算方法对这些关系进行建模并将它们作为数据处理。通过一系列的案例研究、动手练习和实时部署，我们将从广泛的概述开始，然后对特定工具展开详细的学习。
在本次培训结束之后，学员将能够：
- 理解图形数据如何持久化和遍历
- 为给定任务选择最佳框架（从图形数据库到批处理框架）
- 实现Hadoop、Spark、GraphX、Pregel，并行地在多台机器上进行图形计算
- 从图形、流程、遍历方面查看现实世界的大数据问题
受众
- 开发人员
课程形式
- 部分讲座、部分讨论、练习和大量实操
在这一由讲师指导的实时培训中，学员将学习处理图形数据的技术产品和实施方法。目的是识别真实世界里的对象、它们的特征和关系，然后使用图形计算方法对这些关系进行建模并将它们作为数据处理。通过一系列的案例研究、动手练习和实时部署，我们将从广泛的概述开始，然后对特定工具展开详细的学习。
在本次培训结束之后，学员将能够：
- 理解图形数据如何持久化和遍历
- 为给定任务选择最佳框架（从图形数据库到批处理框架）
- 实现Hadoop、Spark、GraphX、Pregel，并行地在多台机器上进行图形计算
- 从图形、流程、遍历方面查看现实世界的大数据问题
受众
- 开发人员
课程形式
- 部分讲座、部分讨论、练习和大量实操
21小时
课程概览
Spark是一个用于查询、分析和转换大数据的数据处理引擎。Python是一种高级编程语言，因其清晰的语法和代码可读性而闻名。PySpark允许用户将Spark与Python连接。
在这一由讲师引导的现场培训中，学员将通过实践练习学习如何使用Python和Spark一起分析大数据。
在本次培训结束后，学员将能够：
- 了解如何使用Spark和Python一起分析大数据
- 开展模拟真实世界环境的练习
- 用不同的工具和技术通过PySpark进行大数据分析
受众
- 开发人员
- IT专业人士
- 数据科学家
课程形式
- 部分讲座、部分讨论、练习和大量实操
在这一由讲师引导的现场培训中，学员将通过实践练习学习如何使用Python和Spark一起分析大数据。
在本次培训结束后，学员将能够：
- 了解如何使用Spark和Python一起分析大数据
- 开展模拟真实世界环境的练习
- 用不同的工具和技术通过PySpark进行大数据分析
受众
- 开发人员
- IT专业人士
- 数据科学家
课程形式
- 部分讲座、部分讨论、练习和大量实操
近期Apache Spark培训课程
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