Hadoop 130419075715-phpapp02(1)
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The document summarizes key components of Hadoop including: 1) The NameNode, located on the master node, stores metadata for HDFS such as file locations and attributes. 2) DataNodes, located on slave nodes, store and retrieve data blocks. 3) The JobTracker, located on the master node, schedules jobs and determines which TaskTrackers on slave nodes will run tasks.
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Hadoop
Hadoop has a master/slave architecture. The master node runs the NameNode, JobTracker, and optionally SecondaryNameNode. The NameNode stores metadata about data locations. DataNodes on slave nodes store the actual data blocks. The JobTracker schedules jobs, assigning tasks to TaskTrackers on slaves which perform the work. The SecondaryNameNode assists the NameNode in the event of failures. MapReduce jobs split files into blocks, map tasks process the blocks in parallel on slaves, and reduce tasks consolidate the results.
Pptx present
The document summarizes key components of Hadoop including:
1) The NameNode, located on the master node, stores metadata for HDFS such as file locations and attributes.
2) DataNodes, located on slave nodes, store and retrieve data blocks.
3) The JobTracker, located on the master node, schedules jobs and assigns tasks to TaskTrackers on slave nodes.
Pp1tx present
The document discusses the key components of Hadoop including the NameNode, DataNodes, JobTracker, TaskTracker, and SecondaryNameNode.
The NameNode stores metadata about data locations and attributes and is located on the master node. DataNodes store the actual data blocks across slave nodes. The JobTracker schedules jobs and is also located on the master node, while TaskTrackers execute tasks on slave nodes. The SecondaryNameNode assists if the NameNode fails.
Hadoop uses a master/slave architecture with the NameNode and JobTracker on the master and DataNodes and TaskTrackers on slave nodes. MapReduce is used for distributed processing where data is split into blocks and mapped and reduced across nodes.
1cailbbtbilgqjw00cff 140424042232-phpapp01
The document discusses the key components of Hadoop including the NameNode, DataNodes, JobTracker, TaskTracker, and SecondaryNameNode.
The NameNode stores metadata for HDFS and tracks where data is stored across DataNodes on the slaves. The JobTracker schedules jobs on TaskTrackers across the slaves. The SecondaryNameNode assists if the NameNode fails. Hadoop uses a master-slave architecture with the NameNode and JobTracker on the master and DataNodes and TaskTrackers on the slaves.
Pptx present
The document discusses the key components of Hadoop including the NameNode, DataNodes, JobTracker, TaskTracker, and SecondaryNameNode.
The NameNode manages the file system metadata and keeps track of where data is stored across the DataNodes. The DataNodes store the actual data blocks.
The JobTracker manages jobs by determining which TaskTrackers on the slaves will execute tasks. The SecondaryNameNode helps if the NameNode fails by providing additional backup of metadata.
Hadoop 130419075715-phpapp02(1)
The document discusses the key components of Hadoop including the NameNode, DataNodes, JobTracker, TaskTracker, and SecondaryNameNode.
The NameNode manages the file system metadata and keeps track of where data is stored across the DataNodes. The DataNodes store the actual data blocks.
The JobTracker manages jobs by determining which TaskTrackers on the slaves will execute tasks. The SecondaryNameNode helps if the NameNode fails by providing additional backup of metadata.
Test schedule
The document discusses the key components of Hadoop including the NameNode, DataNodes, JobTracker, TaskTracker, and SecondaryNameNode.
The NameNode stores metadata about data locations and attributes and is located on the master node. DataNodes store the actual data blocks across slave nodes. The JobTracker schedules jobs and is also located on the master node, while TaskTrackers execute tasks on slave nodes. The SecondaryNameNode assists if the NameNode fails.
Hadoop uses a master/slave architecture with the NameNode and JobTracker on the master and DataNodes and TaskTrackers on slave nodes. It also utilizes MapReduce for parallel processing of large datasets split across blocks in HDFS.
5anbcquvtfgv1pvhfif9 140508053553-phpapp01
The document discusses the key components of Hadoop including the NameNode, DataNodes, JobTracker, TaskTracker, and SecondaryNameNode.
The NameNode manages the file system metadata and keeps track of where data is stored across the DataNodes. The DataNodes store the actual data blocks.
The JobTracker manages jobs by determining which TaskTrackers on the slaves will execute tasks. The SecondaryNameNode helps if the NameNode fails by providing additional backup of metadata.
Recommended
Hadoop
Hadoop has a master/slave architecture. The master node runs the NameNode, JobTracker, and optionally SecondaryNameNode. The NameNode stores metadata about data locations. DataNodes on slave nodes store the actual data blocks. The JobTracker schedules jobs, assigning tasks to TaskTrackers on slaves which perform the work. The SecondaryNameNode assists the NameNode in the event of failures. MapReduce jobs split files into blocks, map tasks process the blocks in parallel on slaves, and reduce tasks consolidate the results.
Pptx present
The document summarizes key components of Hadoop including:
1) The NameNode, located on the master node, stores metadata for HDFS such as file locations and attributes.
2) DataNodes, located on slave nodes, store and retrieve data blocks.
3) The JobTracker, located on the master node, schedules jobs and assigns tasks to TaskTrackers on slave nodes.
Pp1tx present
The document discusses the key components of Hadoop including the NameNode, DataNodes, JobTracker, TaskTracker, and SecondaryNameNode.
The NameNode stores metadata about data locations and attributes and is located on the master node. DataNodes store the actual data blocks across slave nodes. The JobTracker schedules jobs and is also located on the master node, while TaskTrackers execute tasks on slave nodes. The SecondaryNameNode assists if the NameNode fails.
Hadoop uses a master/slave architecture with the NameNode and JobTracker on the master and DataNodes and TaskTrackers on slave nodes. MapReduce is used for distributed processing where data is split into blocks and mapped and reduced across nodes.
1cailbbtbilgqjw00cff 140424042232-phpapp01
The document discusses the key components of Hadoop including the NameNode, DataNodes, JobTracker, TaskTracker, and SecondaryNameNode.
The NameNode stores metadata for HDFS and tracks where data is stored across DataNodes on the slaves. The JobTracker schedules jobs on TaskTrackers across the slaves. The SecondaryNameNode assists if the NameNode fails. Hadoop uses a master-slave architecture with the NameNode and JobTracker on the master and DataNodes and TaskTrackers on the slaves.
Pptx present
The document discusses the key components of Hadoop including the NameNode, DataNodes, JobTracker, TaskTracker, and SecondaryNameNode.
The NameNode manages the file system metadata and keeps track of where data is stored across the DataNodes. The DataNodes store the actual data blocks.
The JobTracker manages jobs by determining which TaskTrackers on the slaves will execute tasks. The SecondaryNameNode helps if the NameNode fails by providing additional backup of metadata.
Hadoop 130419075715-phpapp02(1)
The document discusses the key components of Hadoop including the NameNode, DataNodes, JobTracker, TaskTracker, and SecondaryNameNode.
The NameNode manages the file system metadata and keeps track of where data is stored across the DataNodes. The DataNodes store the actual data blocks.
The JobTracker manages jobs by determining which TaskTrackers on the slaves will execute tasks. The SecondaryNameNode helps if the NameNode fails by providing additional backup of metadata.
Test schedule
The document discusses the key components of Hadoop including the NameNode, DataNodes, JobTracker, TaskTracker, and SecondaryNameNode.
The NameNode stores metadata about data locations and attributes and is located on the master node. DataNodes store the actual data blocks across slave nodes. The JobTracker schedules jobs and is also located on the master node, while TaskTrackers execute tasks on slave nodes. The SecondaryNameNode assists if the NameNode fails.
Hadoop uses a master/slave architecture with the NameNode and JobTracker on the master and DataNodes and TaskTrackers on slave nodes. It also utilizes MapReduce for parallel processing of large datasets split across blocks in HDFS.
5anbcquvtfgv1pvhfif9 140508053553-phpapp01
The document discusses the key components of Hadoop including the NameNode, DataNodes, JobTracker, TaskTracker, and SecondaryNameNode.
The NameNode manages the file system metadata and keeps track of where data is stored across the DataNodes. The DataNodes store the actual data blocks.
The JobTracker manages jobs by determining which TaskTrackers on the slaves will execute tasks. The SecondaryNameNode helps if the NameNode fails by providing additional backup of metadata.
Hadoop 130419075715-phpapp02(1)
The document discusses the key components of Hadoop including the NameNode, DataNodes, JobTracker, and SecondaryNameNode.
The NameNode stores metadata about data locations and attributes and is located on the master node. DataNodes store the actual data blocks across slave nodes.
The JobTracker is responsible for scheduling jobs and is also located on the master node. When a job is submitted, the JobTracker determines which TaskTrackers on the slaves will execute the tasks.
The SecondaryNameNode acts as a backup to the NameNode in case of failure and is also located on the master node.
5anbcquvtfgv1pvhfif9 140508053553-phpapp01 (1)
The document discusses the key components of Hadoop including the NameNode, DataNodes, JobTracker, TaskTracker, and SecondaryNameNode.
The NameNode stores metadata about data locations and attributes and is located on the master node. DataNodes store the actual data blocks across slave nodes. The JobTracker schedules jobs and is also located on the master node, while TaskTrackers execute tasks on slave nodes. The SecondaryNameNode assists if the NameNode fails.
Hadoop uses a master/slave architecture with the NameNode and JobTracker on the master and DataNodes and TaskTrackers on slave nodes. MapReduce is used for distributed processing where tasks are assigned to nodes and results are aggregated.
Ppt1x present
The document discusses the key components of Hadoop including the NameNode, DataNodes, JobTracker, TaskTracker, and SecondaryNameNode.
The NameNode stores metadata for HDFS and tracks where data is stored across DataNodes on the slaves. The JobTracker schedules jobs on TaskTrackers across the slaves. The SecondaryNameNode assists the NameNode in the event of failure. Hadoop uses a master-slave architecture with the NameNode and JobTracker on the master and DataNodes and TaskTrackers on the slaves.
Heisenberg
The document discusses the key components of Hadoop including the NameNode, DataNodes, JobTracker, TaskTracker, and SecondaryNameNode.
The NameNode stores metadata for HDFS and tracks where data is stored across DataNodes on the slaves. The JobTracker schedules jobs on TaskTrackers across the slaves. The SecondaryNameNode assists the NameNode in the event of failures. Hadoop uses a master-slave architecture with the NameNode and JobTracker on the master and DataNodes and TaskTrackers on the slaves.
5anbcquvtfgv1pvhfif9 140508053553-phpapp01
The document discusses the key components of Hadoop including the NameNode, DataNodes, JobTracker, TaskTracker, and SecondaryNameNode.
The NameNode stores metadata for HDFS and tracks where data is stored across DataNodes on the slaves. The JobTracker schedules jobs on TaskTrackers across the slaves. The SecondaryNameNode assists the NameNode in the event of failure. Hadoop uses a master-slave architecture with the NameNode and JobTracker on the master and DataNodes and TaskTrackers on the slaves.
#jeet
The document discusses the key components of Hadoop including the NameNode, DataNodes, JobTracker, TaskTracker, and SecondaryNameNode.
The NameNode stores metadata about data locations and attributes and is located on the master node. DataNodes store the actual data blocks across slave nodes. The JobTracker schedules jobs and is also located on the master node, while TaskTrackers execute tasks on slave nodes. The SecondaryNameNode assists if the NameNode fails.
Hadoop uses a master/slave architecture with the NameNode and JobTracker on the master and DataNodes and TaskTrackers on slave nodes. MapReduce is used for distributed processing where data is split into blocks and mapped and reduced across nodes.
5anbcquvtfgv1pvhfif9 140508053553-phpapp01 (2)
The document discusses the key components of Hadoop including the NameNode, DataNodes, JobTracker, TaskTracker, and SecondaryNameNode.
The NameNode stores metadata for HDFS and tracks where data is stored across DataNodes on the slaves. The JobTracker schedules jobs on TaskTrackers across the slaves. The SecondaryNameNode assists the NameNode in the event of failures. Hadoop uses a master-slave architecture with the NameNode and JobTracker on the master and DataNodes and TaskTrackers on the slaves.
Checkupload1 140213043220-phpapp01
The document discusses the key components of Hadoop including the NameNode, DataNodes, JobTracker, TaskTracker, and SecondaryNameNode.
The NameNode stores metadata about data locations and attributes and is located on the master node. DataNodes store the actual data blocks across slave nodes. The JobTracker schedules jobs and is also located on the master node, while TaskTrackers execute tasks on slave nodes. The SecondaryNameNode assists if the NameNode fails.
Hadoop uses a master/slave architecture with the NameNode and JobTracker on the master and DataNodes and TaskTrackers on slave nodes. MapReduce is used for distributed processing where data is split into blocks and mapped and reduced across nodes.
Hadoop 130419075715-phpapp02(1)
The document discusses the key components of Hadoop including the NameNode, DataNodes, JobTracker, TaskTracker, and SecondaryNameNode.
The NameNode stores metadata about data locations and attributes and is located on the master node. DataNodes store the actual data blocks across slave nodes. The JobTracker schedules jobs and is also located on the master node, while TaskTrackers execute tasks on slave nodes. The SecondaryNameNode assists if the NameNode fails.
Hadoop uses a master/slave architecture with the NameNode and JobTracker on the master and DataNodes and TaskTrackers on slave nodes. MapReduce is used for distributed processing where data is split into blocks and mapped and reduced across nodes.
1cailbbtbilgqjw00cff 140424042232-phpapp01
1cailbbtbilgqjw00cff 140424042232-phpapp01This is a long company name trying to break the username filed
The document discusses the key components of Hadoop including the NameNode, DataNodes, JobTracker, TaskTracker, and SecondaryNameNode.
The NameNode stores metadata for HDFS and tracks where data is stored across DataNodes on the slaves. The JobTracker schedules jobs on TaskTrackers across the slaves. The SecondaryNameNode assists if the NameNode fails. Hadoop uses a master-slave architecture with the NameNode and JobTracker on the master and DataNodes and TaskTrackers on the slaves.Doc document
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Hadoop is a distributed processing framework that operates on a master-slave architecture. The master node runs the NameNode, JobTracker, and SecondaryNameNode processes. The NameNode manages the file system metadata and location information for data stored across the DataNodes on slave nodes. The JobTracker manages job scheduling and coordination, assigning tasks to TaskTrackers on slaves. The SecondaryNameNode provides backup support in case the NameNode fails. Hadoop uses MapReduce as a programming model where user code is run as Maps on data subsets and the outputs are aggregated by Reduces.
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This document provides an overview of the Hadoop framework. It describes the key components of Hadoop including the NameNode, DataNodes, JobTracker, TaskTracker, and SecondaryNameNode. The NameNode manages file metadata and location information stored across DataNodes. The JobTracker schedules and tracks jobs on TaskTrackers running on slave nodes. The SecondaryNameNode helps recover metadata if the NameNode fails. Hadoop uses a master/slave architecture with the NameNode and JobTracker on the master and DataNodes and TaskTrackers on slave nodes.
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The document discusses the key components of Hadoop including the NameNode, DataNodes, JobTracker, and SecondaryNameNode.
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The NameNode stores metadata about data locations and attributes and is located on the master node. DataNodes store the actual data blocks across slave nodes. The JobTracker schedules jobs and is also located on the master node, while TaskTrackers execute tasks on slave nodes. The SecondaryNameNode assists if the NameNode fails.
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The NameNode stores metadata about data locations and attributes and is located on the master node. DataNodes store the actual data blocks across slave nodes. The JobTracker schedules jobs and is also located on the master node, while TaskTrackers execute tasks on slave nodes. The SecondaryNameNode assists if the NameNode fails.
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The document discusses the key components of Hadoop including the NameNode, DataNodes, JobTracker, TaskTracker, and SecondaryNameNode.
The NameNode stores metadata for HDFS and tracks where data is stored across DataNodes on the slaves. The JobTracker schedules jobs on TaskTrackers across the slaves. The SecondaryNameNode assists the NameNode in the event of failures. Hadoop uses a master-slave architecture with the NameNode and JobTracker on the master and DataNodes and TaskTrackers on the slaves.
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一比一原版(QMUE毕业证书)英国爱丁堡玛格丽特女王大学毕业证文凭如何办理
永久可查学历认证【微信：A575476】【(QMUE毕业证书)英国爱丁堡玛格丽特女王大学毕业证成绩单Offer】【微信：A575476】（留信学历认证永久存档查询）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信：A575476】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信：A575476】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
→ 【关于价格问题（保证一手价格）
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：可来公司面谈，可签订合同，会陪同客户一起到教育部认证窗口递交认证材料，客户在教育部官方认证查询网站查询到认证通过结果后付款，不成功不收费！
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Industrial Tech SW: Category Renewal and Creation
Every industrial revolution has created a new set of categories and a new set of players.
Multiple new technologies have emerged, but Samsara and C3.ai are only two companies which have gone public so far.
Manufacturing startups constitute the largest pipeline share of unicorns and IPO candidates in the SF Bay Area, and software startups dominate in Germany.
Dpboss Matka Guessing Satta Matta Matka Kalyan Chart Indian Matka
Dpboss Matka Guessing Satta Matta Matka Kalyan Chart Indian Matka
Profiles of Iconic Fashion Personalities.pdf
The fashion industry is dynamic and ever-changing, continuously sculpted by trailblazing visionaries who challenge norms and redefine beauty. This document delves into the profiles of some of the most iconic fashion personalities whose impact has left a lasting impression on the industry. From timeless designers to modern-day influencers, each individual has uniquely woven their thread into the rich fabric of fashion history, contributing to its ongoing evolution.
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“After being the most listed dog breed in the United States for 31
years in a row, the Labrador Retriever has dropped to second place
in the American Kennel Club's annual survey of the country's most
popular canines. The French Bulldog is the new top dog in the
United States as of 2022. The stylish puppy has ascended the
rankings in rapid time despite having health concerns and limited
color choices.”
一比一原版新西兰奥塔哥大学毕业证（otago毕业证）如何办理
一模一样【微信：A575476】【新西兰奥塔哥大学毕业证（otago毕业证）成绩单Offer】【微信：A575476】（留信学历认证永久存档查询）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信：A575476】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信：A575476】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
→ 【关于价格问题（保证一手价格）
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：可来公司面谈，可签订合同，会陪同客户一起到教育部认证窗口递交认证材料，客户在教育部官方认证查询网站查询到认证通过结果后付款，不成功不收费！
Unlocking WhatsApp Marketing with HubSpot: Integrating Messaging into Your Ma...
50 million companies worldwide leverage WhatsApp as a key marketing channel. You may have considered adding it to your marketing mix, or probably already driving impressive conversions with WhatsApp.
But wait. What happens when you fully integrate your WhatsApp campaigns with HubSpot?
That's exactly what we explored in this session.
We take a look at everything that you need to know in order to deploy effective WhatsApp marketing strategies, and integrate it with your buyer journey in HubSpot. From technical requirements to innovative campaign strategies, to advanced campaign reporting - we discuss all that and more, to leverage WhatsApp for maximum impact. Check out more details about the event here https://events.hubspot.com/events/details/hubspot-new-delhi-presents-unlocking-whatsapp-marketing-with-hubspot-integrating-messaging-into-your-marketing-strategy/
Digital Transformation Frameworks: Driving Digital Excellence
[To download this presentation, visit:
https://www.oeconsulting.com.sg/training-presentations]
This presentation is a curated compilation of PowerPoint diagrams and templates designed to illustrate 20 different digital transformation frameworks and models. These frameworks are based on recent industry trends and best practices, ensuring that the content remains relevant and up-to-date.
Key highlights include Microsoft's Digital Transformation Framework, which focuses on driving innovation and efficiency, and McKinsey's Ten Guiding Principles, which provide strategic insights for successful digital transformation. Additionally, Forrester's framework emphasizes enhancing customer experiences and modernizing IT infrastructure, while IDC's MaturityScape helps assess and develop organizational digital maturity. MIT's framework explores cutting-edge strategies for achieving digital success.
These materials are perfect for enhancing your business or classroom presentations, offering visual aids to supplement your insights. Please note that while comprehensive, these slides are intended as supplementary resources and may not be complete for standalone instructional purposes.
Frameworks/Models included:
Microsoft’s Digital Transformation Framework
McKinsey’s Ten Guiding Principles of Digital Transformation
Forrester’s Digital Transformation Framework
IDC’s Digital Transformation MaturityScape
MIT’s Digital Transformation Framework
Gartner’s Digital Transformation Framework
Accenture’s Digital Strategy & Enterprise Frameworks
Deloitte’s Digital Industrial Transformation Framework
Capgemini’s Digital Transformation Framework
PwC’s Digital Transformation Framework
Cisco’s Digital Transformation Framework
Cognizant’s Digital Transformation Framework
DXC Technology’s Digital Transformation Framework
The BCG Strategy Palette
McKinsey’s Digital Transformation Framework
Digital Transformation Compass
Four Levels of Digital Maturity
Design Thinking Framework
Business Model Canvas
Customer Journey Map
Prescriptive analytics BA4206 Anna University PPT
Business analysis - Prescriptive analytics Introduction to Prescriptive analytics
Prescriptive Modeling
Non Linear Optimization
Demonstrating Business Performance Improvement
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- 1. Hadoop Edit by Cassell Hsu 2013.04.19
- 3. Hadoop • Master • NameNode • JobTracker • SecondaryNameNode
- 4. NameNode • Where is NameNode? • Master • HDFS(Hadoop Distributed File System) • What is NameNode? • 資料之位置資訊 • 資料之屬性 ？？
- 5. NameNode • 位置資訊? • 所有資料皆存放在 – DataNode
- 6. DataNode • What is DataNode • 存放資料 • Where is DataNode • HDFS • Slaves (and Master)
- 9. Hadoop • Master • NameNode • JobTracker • SecondaryNameNode
- 10. JobTracker • What is JobTracker? • 排程工作 • Where is JobTracker? • Master 誰來工作？
- 11. JobTracker & TaskTrackerJobTracker TaskTracker Where Master Slaves What 排程工作 執行工作
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