本地，讲师指导的实时Apache Hadoop培训课程通过交互式实践练习演示Hadoop生态系统的核心组件以及如何使用这些技术解决大规模问题。 Hadoop培训可作为“现场实时培训”或“远程实时培训”。现场培训可以在当地的客户场所进行中国或者在NobleProg的企业培训中心中国 。远程实时培训通过交互式远程桌面进行。 NobleProg - 您当地的培训机构
Machine Translated
客户评论
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
我最喜欢教练提供真实的实例。
Simon Hahn
课程: Administrator Training for Apache Hadoop
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我真的很享受培训师的巨大能力。
Grzegorz Gorski
课程: Administrator Training for Apache Hadoop
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我真的很喜欢这些实践课程。
Jacek Pieczątka
课程: Administrator Training for Apache Hadoop
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非常喜欢互动的学习方式。
Luigi Loiacono
课程: Data Analysis with Hive/HiveQL
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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
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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
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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
我受益于培训师的能力和知识。
Jonathan Puvilland
课程: Data Analysis with Hive/HiveQL
Machine Translated
这是非常亲力亲为的，我们花了一半的时间实际在Clouded / Hardtop做事，运行不同的命令，检查系统，等等。额外的材料（书籍，网站等）真的很感激，我们将不得不继续学习。安装非常有趣，而且非常方便，从头开始的集群设置非常好。
Ericsson
课程: Administrator Training for Apache Hadoop
Machine Translated
教练很棒，真的很了解他的东西。我学到了很多关于我之前不知道的软件，这对我的工作有很大的帮助！
Steve McPhail - Alberta Health Services - Information Technology
课程: Data Analysis with Hive/HiveQL
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关于Hive，HDFS的高级原则......
Geert Suys - Proximus Group
课程: Data Analysis with Hive/HiveQL
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双手。混合练习/理论
Proximus Group
课程: Data Analysis with Hive/HiveQL
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Fulvio能够掌握我们公司的业务案例，并能够几乎立即与课程材料相关联。
Samuel Peeters - Proximus Group
课程: Data Analysis with Hive/HiveQL
Machine Translated
很多动手练习。
Ericsson
课程: Administrator Training for Apache Hadoop
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Ambari管理工具。能够讨论除电信之外的其他业务案例的实际Hadoop体验。
Ericsson
课程: Administrator Training for Apache Hadoop
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我认为他在为观众量身定做体验方面做得很好。此类主要用于覆盖 hive 的数据分析, 但我和我的同事正在进行 hive 管理, 没有真正的数据分析职责。
ian reif - Franchise Tax Board
课程: Data Analysis with Hive/HiveQL
Machine Translated
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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培训主题和培训员的参与
Izba Administracji Skarbowej w Lublinie
课程: Apache NiFi for Administrators
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与参加培训的人沟通。
Andrzej Szewczuk - Izba Administracji Skarbowej w Lublinie
课程: Apache NiFi for Administrators
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实际做的事情，理论也得到了Ajay的好评
Dominik Mazur - Capgemini Polska Sp. z o.o.
课程: Hadoop Administration on MapR
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演习
Capgemini Polska Sp. z o.o.
课程: Hadoop Administration on MapR
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练习的有用性
Algomine sp.z.o.o sp.k.
课程: Apache NiFi for Administrators
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我发现培训很好，信息量很大......但是可以分发4到5天，让我们可以了解不同方面的更多细节。
Veterans Affairs Canada
课程: Hadoop Administration
Machine Translated
我非常喜欢这种训练。安东拥有丰富的知识，并以非常方便的方式提出了必要的理论。很棒的是，培训是很多有趣的练习，所以我们从一开始就接触到了我们所知道的技术。
Szymon Dybczak - Algomine sp.z.o.o sp.k.
课程: Apache NiFi for Administrators
Machine Translated
我发现这个课程给了一个很好的概述，并迅速触及了一些我甚至没有考虑的领域。
Veterans Affairs Canada
课程: Hadoop Administration
Machine Translated
我非常喜欢使用集群的工作练习，以查看跨集群的节点的性能和扩展功能。
CACI Ltd
课程: Apache NiFi for Developers
Machine Translated
Ajay是一位非常有经验的顾问，能够回答我们所有的问题，甚至就我们目前从事的项目的最佳实践提出建议。
CACI Ltd
课程: Apache NiFi for Administrators
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实用部分。为所讨论的问题精心挑选了任务。
Rossmann SDP
课程: HBase for Developers
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Apache Hadoop子类别
Hadoop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
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
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
14小时
课程概览
Tigon is an open-source, real-time, low-latency, high-throughput, native YARN, stream processing framework that sits on top of HDFS and HBase for persistence. Tigon applications address use cases such as network intrusion detection and analytics, social media market analysis, location analytics, and real-time recommendations to users.
This instructor-led, live training introduces Tigon's approach to blending real-time and batch processing as it walks participants through the creation a sample application.
By the end of this training, participants will be able to:
- Create powerful, stream processing applications for handling large volumes of data
- Process stream sources such as Twitter and Webserver Logs
- Use Tigon for rapid joining, filtering, and aggregating of streams
Audience
- Developers
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
This instructor-led, live training introduces Tigon's approach to blending real-time and batch processing as it walks participants through the creation a sample application.
By the end of this training, participants will be able to:
- Create powerful, stream processing applications for handling large volumes of data
- Process stream sources such as Twitter and Webserver Logs
- Use Tigon for rapid joining, filtering, and aggregating of streams
Audience
- Developers
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
14小时
课程概览
Datameer is a business intelligence and analytics platform built on Hadoop. It allows end-users to access, explore and correlate large-scale, structured, semi-structured and unstructured data in an easy-to-use fashion.
In this instructor-led, live training, participants will learn how to use Datameer to overcome Hadoop's steep learning curve as they step through the setup and analysis of a series of big data sources.
By the end of this training, participants will be able to:
- Create, curate, and interactively explore an enterprise data lake
- Access business intelligence data warehouses, transactional databases and other analytic stores
- Use a spreadsheet user-interface to design end-to-end data processing pipelines
- Access pre-built functions to explore complex data relationships
- Use drag-and-drop wizards to visualize data and create dashboards
- Use tables, charts, graphs, and maps to analyze query results
Audience
- Data analysts
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 Datameer to overcome Hadoop's steep learning curve as they step through the setup and analysis of a series of big data sources.
By the end of this training, participants will be able to:
- Create, curate, and interactively explore an enterprise data lake
- Access business intelligence data warehouses, transactional databases and other analytic stores
- Use a spreadsheet user-interface to design end-to-end data processing pipelines
- Access pre-built functions to explore complex data relationships
- Use drag-and-drop wizards to visualize data and create dashboards
- Use tables, charts, graphs, and maps to analyze query results
Audience
- Data analysts
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
21小时
课程概览
In this instructor-led, live training in 中国 (onsite or remote), participants will learn how to deploy and manage Apache NiFi in a live lab environment.
By the end of this training, participants will be able to:
- Install and configure Apachi NiFi.
- Source, transform and manage data from disparate, distributed data sources, including databases and big data lakes.
- Automate dataflows.
- Enable streaming analytics.
- Apply various approaches for data ingestion.
- Transform Big Data and into business insights.
By the end of this training, participants will be able to:
- Install and configure Apachi NiFi.
- Source, transform and manage data from disparate, distributed data sources, including databases and big data lakes.
- Automate dataflows.
- Enable streaming analytics.
- Apply various approaches for data ingestion.
- Transform Big Data and into business insights.
7小时
课程概览
In this instructor-led, live training in 中国, participants will learn the fundamentals of flow-based programming as they develop a number of demo extensions, components and processors using Apache NiFi.
By the end of this training, participants will be able to:
- Understand NiFi's architecture and dataflow concepts.
- Develop extensions using NiFi and third-party APIs.
- Custom develop their own Apache Nifi processor.
- Ingest and process real-time data from disparate and uncommon file formats and data sources.
By the end of this training, participants will be able to:
- Understand NiFi's architecture and dataflow concepts.
- Develop extensions using NiFi and third-party APIs.
- Custom develop their own Apache Nifi processor.
- Ingest and process real-time data from disparate and uncommon file formats and data sources.
28小时
课程概览
Hadoop is a popular Big Data processing framework. Python is a high-level programming language famous for its clear syntax and code readibility.
In this instructor-led, live training, participants will learn how to work with Hadoop, MapReduce, Pig, and Spark using Python as they step through multiple examples and use cases.
By the end of this training, participants will be able to:
- Understand the basic concepts behind Hadoop, MapReduce, Pig, and Spark
- Use Python with Hadoop Distributed File System (HDFS), MapReduce, Pig, and Spark
- Use Snakebite to programmatically access HDFS within Python
- Use mrjob to write MapReduce jobs in Python
- Write Spark programs with Python
- Extend the functionality of pig using Python UDFs
- Manage MapReduce jobs and Pig scripts using Luigi
Audience
- Developers
- IT Professionals
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 work with Hadoop, MapReduce, Pig, and Spark using Python as they step through multiple examples and use cases.
By the end of this training, participants will be able to:
- Understand the basic concepts behind Hadoop, MapReduce, Pig, and Spark
- Use Python with Hadoop Distributed File System (HDFS), MapReduce, Pig, and Spark
- Use Snakebite to programmatically access HDFS within Python
- Use mrjob to write MapReduce jobs in Python
- Write Spark programs with Python
- Extend the functionality of pig using Python UDFs
- Manage MapReduce jobs and Pig scripts using Luigi
Audience
- Developers
- IT Professionals
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
14小时
课程概览
Sqoop is an open source software tool for transfering data between Hadoop and relational databases or mainframes. It can be used to import data from a relational database management system (RDBMS) such as MySQL or Oracle or a mainframe into the Hadoop Distributed File System (HDFS). Thereafter, the data can be transformed in Hadoop MapReduce, and then re-exported back into an RDBMS.
In this instructor-led, live training, participants will learn how to use Sqoop to import data from a traditional relational database to Hadoop storage such HDFS or Hive and vice versa.
By the end of this training, participants will be able to:
- Install and configure Sqoop
- Import data from MySQL to HDFS and Hive
- Import data from HDFS and Hive to MySQL
Audience
- System administrators
- Data engineers
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.
In this instructor-led, live training, participants will learn how to use Sqoop to import data from a traditional relational database to Hadoop storage such HDFS or Hive and vice versa.
By the end of this training, participants will be able to:
- Install and configure Sqoop
- Import data from MySQL to HDFS and Hive
- Import data from HDFS and Hive to MySQL
Audience
- System administrators
- Data engineers
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
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.
35小时
课程概览
This instructor-led, live training in 中国 (online or onsite) is aimed at system administrators who wish to learn how to set up, deploy and manage Hadoop clusters within their organization.
By the end of this training, participants will be able to:
- Install and configure Apache Hadoop.
- Understand the four major components in the Hadoop ecoystem: HDFS, MapReduce, YARN, and Hadoop Common.
- Use Hadoop Distributed File System (HDFS) to scale a cluster to hundreds or thousands of nodes.
- Set up HDFS to operate as storage engine for on-premise Spark deployments.
- Set up Spark to access alternative storage solutions such as Amazon S3 and NoSQL database systems such as Redis, Elasticsearch, Couchbase, Aerospike, etc.
- Carry out administrative tasks such as provisioning, management, monitoring and securing an Apache Hadoop cluster.
By the end of this training, participants will be able to:
- Install and configure Apache Hadoop.
- Understand the four major components in the Hadoop ecoystem: HDFS, MapReduce, YARN, and Hadoop Common.
- Use Hadoop Distributed File System (HDFS) to scale a cluster to hundreds or thousands of nodes.
- Set up HDFS to operate as storage engine for on-premise Spark deployments.
- Set up Spark to access alternative storage solutions such as Amazon S3 and NoSQL database systems such as Redis, Elasticsearch, Couchbase, Aerospike, etc.
- Carry out administrative tasks such as provisioning, management, monitoring and securing an Apache Hadoop cluster.
7小时
课程概览
This course covers how to use Hive SQL language (AKA: Hive HQL, SQL on Hive, HiveQL) for people who extract data from Hive
21小时
课程概览
Cloudera Impala is an open source massively parallel processing (MPP) SQL query engine for Apache Hadoop clusters.
Impala enables users to issue low-latency SQL queries to data stored in Hadoop Distributed File System and Apache Hbase without requiring data movement or transformation.
Audience
This course is aimed at analysts and data scientists performing analysis on data stored in Hadoop via Business Intelligence or SQL tools.
After this course delegates will be able to
- Extract meaningful information from Hadoop clusters with Impala.
- Write specific programs to facilitate Business Intelligence in Impala SQL Dialect.
- Troubleshoot Impala.
Impala enables users to issue low-latency SQL queries to data stored in Hadoop Distributed File System and Apache Hbase without requiring data movement or transformation.
Audience
This course is aimed at analysts and data scientists performing analysis on data stored in Hadoop via Business Intelligence or SQL tools.
After this course delegates will be able to
- Extract meaningful information from Hadoop clusters with Impala.
- Write specific programs to facilitate Business Intelligence in Impala SQL Dialect.
- Troubleshoot Impala.
21小时
课程概览
Apache Ambari is an open-source management platform for provisioning, managing, monitoring and securing Apache Hadoop clusters.
In this instructor-led live training participants will learn the management tools and practices provided by Ambari to successfully manage Hadoop clusters.
By the end of this training, participants will be able to:
- Set up a live Big Data cluster using Ambari
- Apply Ambari's advanced features and functionalities to various use cases
- Seamlessly add and remove nodes as needed
- Improve a Hadoop cluster's performance through tuning and tweaking
Audience
- DevOps
- System Administrators
- DBAs
- Hadoop testing professionals
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led live training participants will learn the management tools and practices provided by Ambari to successfully manage Hadoop clusters.
By the end of this training, participants will be able to:
- Set up a live Big Data cluster using Ambari
- Apply Ambari's advanced features and functionalities to various use cases
- Seamlessly add and remove nodes as needed
- Improve a Hadoop cluster's performance through tuning and tweaking
Audience
- DevOps
- System Administrators
- DBAs
- Hadoop testing professionals
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
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.
近期Hadoop培训课程
08-11-2021 09:30
上海 - 上海中区广场
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15-11-2021 09:30
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28 小时
16-11-2021 09:30
上海 - 上海中区广场
28 小时
08-12-2021 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
21 小时
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