Distributed

Distributed computing gives numerous transforming proficient administrations like framework as a bolster, PC programming as a bolster, framework as an administration, and establishment like an administration. India:+91 USA: +

Duration: 4 Hours Size: 1.17 GB 01 - Start Building NSX Switches and Routers. 02 - Verifying the vSphere Environment Before Adding VXLAN Networking. 03 - Understanding the Value of NSX Distributed Switches. 04 - Creating an NSX Logical Switch. 05 - Enabling CDO Mode in NSX. 06 - Advanced NSX Controller Cluster Management. 07 - Viewing VTEP, MAC, and ARP Tables. 08 - Understanding the Value of NSX Distributed Routers. 09 - Creating and Connecting an NSX Distributed Logical Router. 10 - Creating and Connecting an NSX Edge Services Gateway. 11 - Using the CLI on an Edge Device. 12 - Using the CLI to View DLR Properties. 13 - Using the CLI of the NSX Manager to View Everything. 14 - Using NET-VDR to View DLR Information. 15 - Understanding the Value of Using Dynamic Routing. 16 - Enabling OSPF on NSX DLR and Edge Routers. 17 - Enabling BGP on NSX DLR and Edge Routers. 18 - Redistributing Dynamic Routing Protocols in NSX. 19 - Protecting NSX Edge Devices with High Availability. 20 - Configuring NSX High Availability for Edge Devices. 21 - Configuring NSX High Availability for Distributed Logical Routers. 22 - Enabling Equal Cost Multi Path (ECMP) Routing in NSX. 23 - Understanding NSX Layer 2 Bridging. 24 - Configuring a Layer 2 Bridge on an NSX Distributed Logical Router. 25 - Universal NSX Switches and Routers. 26 - Creating Universal NSX Switches. 27 - Creating Universal NSX Routers. 28 - Using Local Egress and Locale IDs with Universal DLRs. PDF Advanced Design and Implementation of Virtual Machines (2).pdf Advanced Design and Implementation of Virtual Machines.pdf Designing Hyper-V Solutions.pdf Implementing VMware vCenter Server.pdf Mastering VMware vSphere 6.5_ Leverage the power of vSphere for effective virtualization, administration, management and monitoring of data centers.pdf microsoft-azure-step-by-step-guide.pdf vCenter Server Appliance.pdf vmware-vcenter-server6-deployment-guide-white-paper.pdf vsan-661-administration-guide.pdf vsphere-esxi-vcenter-server-511-host-management-guide.pdf vsphere-esxi-vcenter-server-67-host-management-guide.pdf vsphere-esxi-vcenter-server-671-virtual-machine-admin-guide.pdf vsphere-vcenter-server-671-installation-guide.pdf vsphere-vcenter-server-671-upgrade-guide.pdf vsphere-vcenter-server-672-upgrade-guide.pdf

Hadoop for Developers and Administrators Hadoop for Developers and Administrators Syllabus COURSE CONTENT Traning at hadoop school of training Magarpatta city Pune. (+91-93257-93756) www.hadoopschooloftraining.co.in Next level of development and administration Schedule: Day 1:BigData • Why is Big Data important • What is Big Data • Characteristics of Big Data • How did data become so big • Why should you care about Big Data • Use Cases of Big Data Analysis • What are possible options for analyzing big data • Traditional Distributed Systems • Problems with traditional distributed systems • What is Hadoop • History of Hadoop • How does Hadoop solve Big Data problem • Components of Hadoop • What is HDFS • How HDFS works • What is Mapreduce • How Mapreduce works • How Hadoop works as a system Day2: Hadoop ecosystem • Pig • What is Pig • How it works • An example • Hive • What is Hive • How it works • An example • Flume • What is Flume • How it works • An example • Sqoop • What is Sqoop • How it works • An example • Oozie • What is Oozie • How it works • An example • HDFS in detail • Map Reduce in details Day3: Hands On-¬‐ • VMsetup • Setting up Virtual Machine • Installing Hadoop in Pseudo Distributed mode Day 4: Hands On-¬‐ • Programs • Running your first MapReduce Program • Sqoop Hands on • Flume Hands Day 5: • Multinode cluster setup • Setting up a multimode cluster Day 6: • Planning your Hadoop cluster • Considerations for setting up Hadoop Cluster • Hardware considerations • Software considerations • Other considerations Day 7: Disecting the Wordcount Program • Understanding the Driver • Understanding the Mapper • Understanding the Reducer Day 8: • Diving deeper into MapReduce • API • Understanding combiners • Understanding partitioners • Understanding input formats • Understanding output formats • Distributed Cache • Understanding Counters Day 9: • Common Mapreduce patterns • Sorting Serching • Inverted Indexes Day 10: • Common Mapreduce patterns • TF-IDF • Word-Cooccurance Day 11: • Hands on Mapreduce Day 12: • Hands on Mapreduce Day 13: • Introduction to Pig and Hive • Pig program structure and execution process • Joins • Filtering • Group and Co-Group • Schema merging and redefining schema • Pig functions • Motivation and Understanding Hive • Using Hive Command line Interface • Data types and File Formats • Basic DDL operations • Schema Design Day 14: • Hands on Hive and Pig Day 15: • Advanced Hadoop Concepts • Yarn • Hadoop Federation • Authntication in Hadoop • High Availbability Day 16: • Administration Refresher • Setting up hadoop cluster - Considerations • Most important configurations • Installation options Day 17: • Scheduling in Hadoop • FIFO Scheduler • Fair Scheduler Day 18: • Monitoring your Hadoop Cluster • Monitoring tools available • Ganglia • Monitoring best practices Day 19: • Administration Best practices • Hadoop Administration best practices • Tools of the trade Day 20: • Test • Test – 50 questions test (20- Development releated, 20- • Administration related and 10 Hadoop in General Please Contact- Hadoop School of Training Destination Center, Second Floor Magarpatta City Pune: 411013 Phone: India: +91-93257-93756 USA: 001-347-983-8512 www.hadoopschooloftraining.co.in Email: learninghub01@gmail.com Skype: learning.hub01

Course / Training duration: (25+ Hours) – Both Weekdays or Weekends Location: Chennai Area: Velachery 100ft road About Credo Systemz: Credo Systemz is the leading training institute and android development company located in Velachery 100ft road,which is providing all kind of Software related training for the candidates with the expertise knowledge. About Trainer: Actually trainers in Credo Systemz are working professionals who are working in leading CMMI level 5 MNC’s and the experience of the trainers will range minimum from 8 -10 Years minimum. They also have experience in training space for around 7 Years. About Hadoop: Hadoop is an Apache Software Foundation Project that provides two things Hadoop Distributed File System (HDFS) Framework and API for building and running Map Reduce Jobs HDFS is build almost similar to Unix File System except data storage is distributed across several machines. It has built in mechanisms to handle machine outages and optimized for better output rather than latency. HDFS cluster consists of Data Node, Name Node and Secondary Name Node. Data can be accessed using either the Java API or the Hadoop Command. The features of HDFS are Scalability, FailureTolerent, Space, Pairs well with map machine. Above all Hadoop Map/Reduce is a software framework for easily writing applications which process vast amounts of data (multi-terabyte data-sets) in-parallel on large clusters (thousands of nodes) of commodity hardware in a reliable, fault-tolerant manner. Hadoop Training syllabus: Introduction to hadoop and Big data Challenges in Hadoop Distributed systems Advantages of distributed system Hadoop architecture HDFS and Maps Eco Systems Hives HBase and Map FSO operations How to read data from hadoop URL Hadoop Streaming Map Reduce Chain mapping API SQOOP Benefit of this Training: 1. With this training you (candidate) will get more knowledge about Hadoop. 2. Training will be like both Theoretical as well as Practical Oriented. 3. Theoretical Knowledge will be very much helpful while attending interviews. 4. Practical knowledge will be very much helpful to complete your assigned task 5. You will get real time inputs from the trainer as he/she is having real time experience in the industry. 6. We will be conducting the Online Mock Certification exam is the biggest specialty in clearing exams. 7. Trainers will conduct the MOCK INTERVIEW. 8. Mock Interview will help you to evaluate yourself like a) Identify your Strength and b) Mitigate your Weakness. Contact Details: Name: Vickram PhNum: Address: New # 30, Old # 16A, Third Main Road, Rajalakshmi Nagar, Velachery, (Opp. to MuruganKalyanaMandapam) Chennai - .

We provide the complete assistance Research Paper service to M.Tech/PhD students. We work for students with their own require guidelines of the organization. Help service offered at Thesis Guru are specialized in their own way that assure best guidance to the clients. Presence of numerous challenging research processes, Lots of challenges are facing by the M.Tech/PhD Students to summit their thesis, research paper /project in time. Timely and effectively completion is priority of Thesis Guru. Thesis Guru is a Single Platform where all Domains are available. A. Hadoop - Hadoop is used for storing data and running applications on clusters of commodity hardware. It is open source software. Hadoop provides huge storage for all kind of data, extensive processing and the potential to handle almost limitless tasks. 1.Big Data Analysis 2.Medical Data 3.Prediction 4.Anamoly Detection in Big Data 5.Image Processing in Hadoop 6.Hadoop with Cryptography 7.Hadoop Security 8. Hadoop used with: a)HIVE b)PIG c)Cascandra d)HIPI B.Parallel Computing 1.Pipeline Optimization 2.Network on HIP (NOXIM) 3.Cache Coherence C.Distributed Computing: 1.Distributed Database 2.Policy in Distributed Database 3.Distributed computation 4.Hybrid cloud and Hadoop on Big Data ThesisGuru - A Unit Of DevelopTech SCO 112-113, 1st Floor, Sector 34-A Chandigarh (UT) India Contact Us +91 9780131206 +91 8283823284

We are IBM Business Partner for conducting the Industrial internship program on IBM Live Projects. Learn Application Development Directly from IBM Certified Developers & Get certified by IBM Big Data with Hadoop (IBM InfoSphere BigInsights) On IBM Tools Big Data and Hadoop 6 weeks training course is designed to provide knowledge and skills to become a successful Hadoop Developer. In-depth knowledge of concepts such as Hadoop 6 weeks Distributed File System, Hadoop Cluster- Single and Multi node, Hadoop 2.x, Flume, Sqoop, Map-Reduce, PIG, Hive, Hbase, Zookeeper, Oozie etc. will be covered in the course. SESSION 1: GRASPING THE FUNDAMENTALS OF BIG DATA a. The Evolution of Data Management b. Understanding the Waves of Managing Data c. Defining Big Data, The Big Data Journey d. Building a Successful Big Data Management Architecture SESSION 2: EXAMINING BIG DATA TYPES a. Defining Structured Data, Defining Unstructured Data b. Looking at Real-Time and Non-Real-Time Requirement c. Putting Big Data Together SESSION 3: DISTRIBUTED COMPUTING a. A Brief History of Distributed Computing b. Understanding the Basics of Distributed Computing c. Getting Performance Right d. Managing Desktops and Devices in the Cloud e. Service Oriented Architecture and the Cloud f. Managing the Cloud Environment SESSION 4: DIGGING INTO BIG DATA TECHNOLOGY COMPONENTS a. Exploring the Big Data Stack b. Layer 0: Redundant Physical Infrastructure c. Layer 1: Security Infrastructure SESSION 5: INTERFACES AND FEEDS TO AND FROM APPLICATIONS AND THE INTERNET a. Operational Databases b. Organizing Data Services and Tools c. Analytical Data Warehouses d. Big Data Analytics, Big Data Applications

Type Computer Hadoop is a free, Java-based programming framework that supports the processing of large data sets in a distributed computing environment. It is part of the Apache project sponsored by the Apache Software Foundation. Hadoop makes it possible to run applications on systems with thousands of nodes involving thousands of terabytes. Its distributed file system facilitates rapid data transfer rates among nodes and allows the system to continue operating uninterrupted in case of a node failure. This approach lowers the risk of catastrophic system failure, even if a significant number of nodes become inoperative. Hadoop was inspired by Google's MapReduce, a software framework in which an application is broken down into numerous small parts. Any of these parts (also called fragments or blocks) can be run on any node in the cluster. Doug Cutting, Hadoop's creator, named the framework after his child's stuffed toy elephant. The current Apache Hadoop ecosystem consists of the Hadoop kernel, MapReduce, the Hadoop distributed file system (HDFS) and a number of related projects such as Apache Hive, HBase and Zookeeper. The Hadoop framework is used by major players including Google, Yahoo and IBM, largely for applications involving search engines and advertising. The preferred operating systems are Windows and Linux but Hadoop can also work with BSD and OS X. Thanks & Regards, Sky InfoTech Corporate Office: A-50,Sec-64,Noida(U.P) Noida- , / Delhi –/ Gurgaon – /

This training on Jmeter software testing is provided by an IT Professional having vast experience on performance testing. He has expertise on performance engineering. He has worked on a large number of projects involving performance testing and performance engineering. He takes corporate trainings on performance testing tools like Rational Performance Tester, Load Runner, OpenSTA, Rational Robot and of course JMeter. JMeter Software Testing Training Agenda Session 1 – Introduction Introducing JMeter? What is Functional/regression/stress testin? What is a test plan? Identifying the testing needs Defining the steps of test Creating JMeter test JMeter GUI The basic elements of a JMeter test plan Building a functional test with your Internet browser Executing your functional test Reading the results of your test The power of JMeterOverview of available JMeter components and functions Implementing an advanced functional test using the key JMeter functions and components Designing and implementing your own practical example to “test” a website of your choice Stress testing Analyzing website traffic Identifying what you to test Modelling the real world in JMeter Extracting and reading the results Other key features Stress testing a database Using JMeter from the command line Editing a test plan in a text/XML editor Session 2Distributed (Remote) TestingPreparing remote environment Running distributed Gathering and analyzing results Using distributed testing with load balancers Variables Creating user variable Extracting data from a web page to a variable Functions Using functions Function helper Session 3Using BeanShell ScriptingShort introduction to BeanShell Creating samplers BeanShell listener __BeanShell function Testing Application with Real Data Configuring Apache Web Server to record appropriate data Access Log Sampler Security issues Performance Testing Fundamentals Stress testing Load testing Soak testing Running multiple threads Setting rump-up period Threads and users Distributed Testing Configuring servers Gathering results Submitting Forms Extracting form ID or checksums Generating sequence or random data Getting data from database Recording forms with a JMeter proxy server Submitting data recorded in log files Managing Sessions Session managers Session per thread Session per user Session 4 Load Distribution Using Apache log files to determine distribution Analyzing distribution and creating appropriate test plans Timers Gaussian Random Timer Other Resources and Load Time Images Java scripts JMeter and HTTP headers policy (browser and proxy caching) Resource Monitoring Monitoring and analyzing CPU resources Monitoring database queries Monitoring memory utilization Monitoring network traffic Running monitoring tools periodically Analyzing and Interpreting Load Test Results Running tests at night and creating periodical reports Statistics available from JMeter Sample, Average, Median, Deviation, Throughput Response time graphs Margins of Error Analyzing results with Excel Interpreting statistical results Finding the bottlenecks Regression and correlations Also, we offer trainings on high end software testing tools like Rational Functional Tester RFT, Selenium, Quick Test Professional QTP, Rational Quality Manager RQM, Quality Center QC, Loadrunner LR and many more. Call me at: .

RCP Technologies is a unique and pioneer institute mainly started with the soul objective of imparting cutting edge, technologically superior and state-of-art, high-end, and career oriented curriculum based courses in Big Data Hadoop and other courses like Data Science, Apache Storm, Spark, Python, Agile, MongoDB, SOA, OSB, Tableau, Cassandra, ADF, Informatica, Obiee, Obia etc. We have trainers who consultants worked in MNC companies. We make sure the best trainers are selected and students gain a lot of knowledge once the training is completed. We don't target lot of students for our training classes but will make sure the exact number of students who are the best fit for carrying out a curriculum are selected. We are not quantity oriented but quality oriented. If you are looking for a professional training in the IT, then we are the best in the business. We have inhouse training facilities along with server setup which makes it a complete corporate environment. For more details call us at share your contact details. What is Hadoop? Hadoop is a free, Java-based programming framework that supports the processing of large data sets in a distributed computing environment. It is part of the Apache project sponsored by the Apache Software Foundation. Hadoop was born out of a need to process big data, as the amount of generated data continued to rapidly increase. As the Web generated more and more information, it was becoming quite challenging to index the content, so Google created MapReduce in , then Yahoo! created Hadoop as a way to implement the MapReduce function. Hadoop is now an open-source Apache implementation project. Hadoop makes it possible to run applications on systems with thousands of nodes involving thousands of terabytes. Its distributed file system facilitates rapid data transfer rates among nodes and allows the system to continue operating uninterrupted in case of a node failure. This approach lowers the risk of catastrophic system failure, even if a significant number of nodes become inoperative. As for the more specific functions, Hadoop has a large scale file system (Hadoop Distributed File System or HDFS), it can write programs, it manages the distribution of programs, then accepts the results, and generates a data result set. Only RCP technologies provides training designed by the leaders and committers of Hadoop. We present valuable real world scenario-based training developed by the core architects, builders and operators of Hadoop with unmatched depth and expertise so that you can be assured you are learning from the experts. RCP technologies is a Interactive Learning Portal started by industry experts with an aim to provide Quality Training in Hadoop Technology. RCP Technologies is offering quality Training services to hadoop to students worldwide. HADOOP Classroom & Online Training by RCP Technologies with an excellent and real time faculty. Our Big Data Hadoop course content designed as per the current IT industry requirement. Apache Hadoop is having very good demand in the market. We provide regular and weekend classes as well as Normal track/Fast track based on the learners requirement and availability Visit us at: RCP technologies Pvt Ltd, #302, Annapurna Block,Aditya Enclave, Near Mitrivanam, Ameerpet, Hyderabad. Helpline:

Hadoop is a free, Java-based programming framework that supports the processing of large data sets in a distributed computing environment. It is part of the Apache project sponsored by the Apache Software Foundation. Hadoop makes it possible to run applications on systems with thousands of nodes involving thousands of terabytes. Its distributed file system facilitates rapid data transfer rates among nodes and allows the system to continue operating uninterrupted in case of a node failure. This approach lowers the risk of catastrophic system failure, even if a significant number of nodes become inoperative. Hadoop was inspired by Google's MapReduce, a software framework in which an application is broken down into numerous small parts. Any of these parts (also called fragments or blocks) can be run on any node in the cluster. Doug Cutting, Hadoop's creator, named the framework after his child's stuffed toy elephant. The current Apache Hadoop ecosystem consists of the Hadoop kernel, MapReduce, the Hadoop distributed file system (HDFS) and a number of related projects such as Apache Hive, HBase and Zookeeper. The Hadoop framework is used by major players including Google, Yahoo and IBM, largely for applications involving search engines and advertising. The preferred operating systems are Windows and Linux but Hadoop can also work with BSD and OS X. Thanks & Regards, Sky InfoTech Corporate Office: A-50,Sec-64,Noida(U.P) Noida- , / Delhi –/ Gurgaon – / Website- http://skyinfotech.in/HADOOP.aspx

Hadoop Develoment Course Contents: Big Data Concepts: 1.What is Big Data 2.How it differs from traditional data 3.Characteristics of big data Hadoop: 1.Overview 2.Components of Hadoop 3.Performance and scalability 4.Hadoop in the context of other data stores 5.Differences between noSQL and Hadoop Unix: 1.installation 2.Basic commands 3.Users and groups Hadoop installations: 1.Standalone mode 2.Pseudo distributed mode HDFS: 1.Architecture 2.Configuring HDFS 3.Blocks, name node and data nodes 4.Job tracker and task tracker 5.Hadoop fs command line interace 6.Basic file system operations & file system api 7.Executing default map reduce examples in hadoop MapReduce: 1.What is MapReduse and how it works 2.Configuring eclipse with Hadoop 3.Mapper, reducer and driver 4.Serialization 5.Custom writable implementation examples in java 6.Input formats 7.Output formats 8.Counters 9.Writing custom counters in java 10.Streaming 11.Sorting (partial, total and secondary) 12.Joins (map side and reduce side joins) 13.No reducer programs 14.Programs in map reduce Hive 1.What is Hive 2.How is data organizes in Hive 3.Data units 4.Data types 5.Operators and functions 6.Creation of tables and partitions 7.Loading the data into HDFS 8.Partition based query 9.Joins 10.Aggregations 11.Multi Tables file inserts 12.Arrays, maps, union all 13.Altering and dropping tables 14.Custom map reduce scripts HBase 1.Zookeeper 2.Data organization in hbase 3.Creating, altering, dropping, inserting the data 4.Joins, Aggregations 5.Custom map reduce scripts 6.Integration of HBase, hive and Hadoop Hadoop Adminstration: The following are common from the above course BigData concepts, Hadoop, unix, Hadoop installations, HDFS. Mapreduce (introduction only) Extra concepts: 1.Hadoop fully distributed mode installation 2.Execution of map reduce programs in fully distributed mode 3.Job scheduling with oozie 4.Monitoring with nagios 5.Logging with Flume 6.Data transfer from other RDBMS using Sqoop Contact us for more details ELEGANT IT SERVICES

Hadoop is a free, Java-based programming framework that supports the processing of large data sets in a distributed computing environment. It is part of the Apache project sponsored by the Apache Software Foundation. Hadoop makes it possible to run applications on systems with thousands of nodes involving thousands of terabytes. Its distributed file system facilitates rapid data transfer rates among nodes and allows the system to continue operating uninterrupted in case of a node failure. This approach lowers the risk of catastrophic system failure, even if a significant number of nodes become inoperative. Hadoop was inspired by Google's MapReduce, a software framework in which an application is broken down into numerous small parts. Any of these parts (also called fragments or blocks) can be run on any node in the cluster. Doug Cutting, Hadoop's creator, named the framework after his child's stuffed toy elephant. The current Apache Hadoop ecosystem consists of the Hadoop kernel, MapReduce, the Hadoop distributed file system (HDFS) and a number of related projects such as Apache Hive, HBase and Zookeeper. The Hadoop framework is used by major players including Google, Yahoo and IBM, largely for applications involving search engines and advertising. The preferred operating systems are Windows and Linux but Hadoop can also work with BSD and OS X. Thanks & Regards, Sky InfoTech Corporate Office: A-50,Sec-64,Noida(U.P) Noida- 0120 4242224, 9717292598/9717292599 Delhi –9717292601/9717292602 Gurgaon – 9810866624/9810866642

Best Hadoop Training Institute Hadoop is a free, Java-based programming framework that supports the processing of large data sets in a distributed computing environment. It is part of the Apache project sponsored by the Apache Software Foundation. Hadoop makes it possible to run applications on systems with thousands of nodes involving thousands of terabytes. Its distributed file system facilitates rapid data transfer rates among nodes and allows the system to continue operating uninterrupted in case of a node failure. This approach lowers the risk of catastrophic system failure, even if a significant number of nodes become inoperative. Hadoop was inspired by Google's MapReduce, a software framework in which an application is broken down into numerous small parts. Any of these parts (also called fragments or blocks) can be run on any node in the cluster. Doug Cutting, Hadoop's creator, named the framework after his child's stuffed toy elephant. The current Apache Hadoop ecosystem consists of the Hadoop kernel, MapReduce, the Hadoop distributed file system (HDFS) and a number of related projects such as Apache Hive, HBase and Zookeeper. The Hadoop framework is used by major players including Google, Yahoo and IBM, largely for applications involving search engines and advertising. The preferred operating systems are Windows and Linux but Hadoop can also work with BSD and OS X. Thanks & Regards, Sky InfoTech Corporate Office: A-50,Sec-64,Noida(U.P) Noida- 0120 4242224, 9717292598/9717292599 Delhi –9717292601/9717292602 Gurgaon – 9810866624/9810866642

Best Hadoop Training Institute Hadoop is a free, Java-based programming framework that supports the processing of large data sets in a distributed computing environment. It is part of the Apache project sponsored by the Apache Software Foundation. Hadoop makes it possible to run applications on systems with thousands of nodes involving thousands of terabytes. Its distributed file system facilitates rapid data transfer rates among nodes and allows the system to continue operating uninterrupted in case of a node failure. This approach lowers the risk of catastrophic system failure, even if a significant number of nodes become inoperative. Hadoop was inspired by Google's MapReduce, a software framework in which an application is broken down into numerous small parts. Any of these parts (also called fragments or blocks) can be run on any node in the cluster. Doug Cutting, Hadoop's creator, named the framework after his child's stuffed toy elephant. The current Apache Hadoop ecosystem consists of the Hadoop kernel, MapReduce, the Hadoop distributed file system (HDFS) and a number of related projects such as Apache Hive, HBase and Zookeeper. The Hadoop framework is used by major players including Google, Yahoo and IBM, largely for applications involving search engines and advertising. The preferred operating systems are Windows and Linux but Hadoop can also work with BSD and OS X. Thanks & Regards, Sky InfoTech Corporate Office: A-50,Sec-64,Noida(U.P) Noida- 0120 4242224, 9717292598/9717292599 Delhi –9717292601/9717292602 Gurgaon – 9810866624/9810866642 Website -

YOUR PATHWAY TO BECOMING A GREAT VMWARE TRAINING THINK IT VMware Training in Chennai provides a world-class VMware online training, classroom training and corporate training, designed to validate and recognizing IT experts with technical capabilities and real world experiences required to effectively install, deploy, and manage support VMware and solutions. As a demand for IT experts with VMware skills and knowledge increases, it’s essential for differentiates yourself in the markets with certification and training that validate your technical skills and capabilities. Best VMware training Chennai VMware training institutes in Chennai a Complete in-depth training courses introduce most compelling feature of VMware vSphere 4. VMware course Chennai demonstrates the vSphere features and that helps to reduce the IT costs while developing flexibility, availability, efficiency, and manageability. Virtualization with VMware VSphere 5.1 and 4 with Installation, configuration Managing and much beyond. Introduction about virtualization with comparison with Microsoft, VMware and SUN methods of virtualization. Install and configuring the VMware ESXi and ESX, managing the ESXi via Vcenter Server components, DCUI and VMware Vcenter Server, esxcli, installation, configuration of standard and distributed switches, and Storage and VM Vmotion, designing the network configuration, Access control and resource pools, Accessing the IP storage and managing the VMFS Datastore, using templates and clones for creating virtual machines, Data recovery procedure using appliances, Performance monitoring and alarms, installing and configuring appliances, Distributed power management, HA, update manager Fault tolerance and Distributed resource scheduler. THINK IT VMware course Chennai Placement and trainer Profile Our VMware Trainers •More than 8+ Years of working experiences in the VMware Technologies •Has worked on 7 real time VMware projects •Working in MNC’s •Strong Theoretical and Practical Knowledge •VMware certified experts Best VMware training Chennai Placements details •Trained 127+ Students so far •More than 103 students placed VMware training in Chennai Course Content •Create Virtual Machines •VMware vCenter Server •Data Protection •Configuring and Managing Virtual Networks •Configuring and Managing Virtual Storage •Virtual Machine Management •Authenticate and Access Control •Introducing VMware vShield Zones •Fault Tolerance and High Availability •Patch Management •Scalability •Install VMware Components VMware training institutes in Chennai - Free Demo Class has been provided by MNC professionals with you flexible Timings… want to know more details contact us +7358206068 visit us Keywords VMware training in Chennai VMware training institutes in Chennai VMware vSphere VMware esxi VMware online training Best VMware training Chennai VMware course Chennai

mso-bidi-theme-font:minor-latin">Apache Spark has become one of the key cluster-computing frame works in the world. Spark can be deployed in numereous ways like in machine Learning, Streaming data and graphic processing. Spark supports programming languages like Python, Scala, Java, and R. Apache Hadoop mso-bidi-theme-font:minor-latin"> is an open-source framework written in Java that allows us to store and process Big Data in a distributed environment, across various clusters of computers using simple programming constructs. To do this, Hadoop uses an algorithm called  Map Reduce mso-bidi-theme-font:minor-latin">, which divides the task into small parts and assigns them to a set of computers. Hadoop also has its own file system,  Hadoop Distributed File System (HDFS),  which is based on  Google File System (GFS). HDFS is designed to run on low-cost hardware. Apache Spark mso-bidi-theme-font:minor-latin"> is an open-source distributed cluster-computing framework. Spark is a data processing engine developed to provide faster and easy-to-use analytics than  Hadoop Ma pReduce. mso-bidi-theme-font:minor-latin;color:#222222;mso-ansi-language:EN-GB">Apache Spark in the big data industry is because of its in-memory data processing that makes it high-speed data processing engine compare to Map Reduce. Apache Spark has huge potential to contribute to Big data related business in the industry.  Apache Spark is a Big data processing interface which provides not only programming interface in the data cluster but also adequate fault tolerance and data parallelism. This open-source platform is efficient in speedy processing of massive datasets. Calibri;mso-bidi-theme-font:minor-latin;color:#222222"> 115%;mso-bidi-font-family:Calibri;mso-bidi-theme-font:minor-latin">Contact us: mso-bidi-theme-font:minor-latin">  http://www.monstercourses.com/ mso-bidi-theme-font:minor-latin">USA:  + 1 772 777 1557 line-height:115%;mso-bidi-font-family:Calibri;mso-bidi-theme-font:minor-latin; color:red"> & +44 702 409 4077 line-height:115%;mso-bidi-font-family:Calibri;mso-bidi-theme-font:minor-latin"> mso-bidi-font-family:Calibri;mso-bidi-theme-font:minor-latin">Skype ID: MonsterCourses Calibri;mso-bidi-theme-font:minor-latin"> mso-bidi-theme-font:minor-latin"> 

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HADOOP Training in Hyderabad @Sadguru Technologies Course Objective Summary During this course, you will learn: • Introduction to Big Data and Analytics • Introduction to Hadoop • Hadoop ecosystem - Concepts • Hadoop Map-reduce concepts and features • Developing the map-reduce Applications • Pig concepts • Hive concepts • Sqoop concepts • Flume Concepts • Oozie workflow concepts • Impala Concepts • Hue Concepts • HBASE Concepts • ZooKeeper Concepts • Real Life Use Cases Reporting Tool • Tableau 1. Virtualbox/VM Ware • Basics • Installations • Backups • Snapshots 2. Linux • Basics • Installations • Commands 3. Hadoop • Why Hadoop? • Scaling • Distributed Framework • Hadoop v/s RDBMS • Brief history of hadoop 4. Setup hadoop • Pseudo mode • Cluster mode • Ipv6 • Ssh • Installation of java, hadoop • Configurations of hadoop • Hadoop Processes ( NN, SNN, JT, DN, TT) • Temporary directory • UI • Common errors when running hadoop cluster, solutions 5. HDFS- Hadoop distributed File System • HDFS Design and Architecture • HDFS Concepts • Interacting HDFS using command line • Interacting HDFS using Java APIs • Dataflow • Blocks • Replica 6. Hadoop Processes • Name node • Secondary name node • Job tracker • Task tracker • Data node 7. Map Reduce • Developing Map Reduce Application • Phases in Map Reduce Framework • Map Reduce Input and Output Formats • Advanced Concepts • Sample Applications • Combiner 8. Joining datasets in Mapreduce jobs • Map-side join • Reduce-Side join

HADOOP Training in Hyderabad @Sadguru Technologies Course Objective Summary During this course, you will learn: • Introduction to Big Data and Analytics • Introduction to Hadoop • Hadoop ecosystem - Concepts • Hadoop Map-reduce concepts and features • Developing the map-reduce Applications • Pig concepts • Hive concepts • Sqoop concepts • Flume Concepts • Oozie workflow concepts • Impala Concepts • Hue Concepts • HBASE Concepts • ZooKeeper Concepts • Real Life Use Cases Reporting Tool • Tableau 1. Virtualbox/VM Ware • Basics • Installations • Backups • Snapshots 2. Linux • Basics • Installations • Commands 3. Hadoop • Why Hadoop? • Scaling • Distributed Framework • Hadoop v/s RDBMS • Brief history of hadoop 4. Setup hadoop • Pseudo mode • Cluster mode • Ipv6 • Ssh • Installation of java, hadoop • Configurations of hadoop • Hadoop Processes ( NN, SNN, JT, DN, TT) • Temporary directory • UI • Common errors when running hadoop cluster, solutions 5. HDFS- Hadoop distributed File System • HDFS Design and Architecture • HDFS Concepts • Interacting HDFS using command line • Interacting HDFS using Java APIs • Dataflow • Blocks • Replica 6. Hadoop Processes • Name node • Secondary name node • Job tracker • Task tracker • Data node 7. Map Reduce • Developing Map Reduce Application • Phases in Map Reduce Framework • Map Reduce Input and Output Formats • Advanced Concepts • Sample Applications • Combiner 8. Joining datasets in Mapreduce jobs • Map-side join • Reduce-Side join 9. Map reduce – customization • Custom Input format class • Hash Partitioner • Custom Partitioner • Sorting techniques • Custom Output format class

HADOOP Training in Hyderabad @Sadguru Technologies Course Objective Summary During this course, you will learn: • Introduction to Big Data and Analytics • Introduction to Hadoop • Hadoop ecosystem - Concepts • Hadoop Map-reduce concepts and features • Developing the map-reduce Applications • Pig concepts • Hive concepts • Sqoop concepts • Flume Concepts • Oozie workflow concepts • Impala Concepts • Hue Concepts • HBASE Concepts • ZooKeeper Concepts • Real Life Use Cases Reporting Tool • Tableau 1. Virtualbox/VM Ware • Basics • Installations • Backups • Snapshots 2. Linux • Basics • Installations • Commands 3. Hadoop • Why Hadoop? • Scaling • Distributed Framework • Hadoop v/s RDBMS • Brief history of hadoop 4. Setup hadoop • Pseudo mode • Cluster mode • Ipv6 • Ssh • Installation of java, hadoop • Configurations of hadoop • Hadoop Processes ( NN, SNN, JT, DN, TT) • Temporary directory • UI • Common errors when running hadoop cluster, solutions 5. HDFS- Hadoop distributed File System • HDFS Design and Architecture • HDFS Concepts • Interacting HDFS using command line • Interacting HDFS using Java APIs • Dataflow • Blocks • Replica 6. Hadoop Processes • Name node • Secondary name node • Job tracker • Task tracker • Data node 7. Map Reduce • Developing Map Reduce Application • Phases in Map Reduce Framework • Map Reduce Input and Output Formats • Advanced Concepts • Sample Applications • Combiner 8. Joining datasets in Mapreduce jobs • Map-side join • Reduce-Side join 9. Map reduce – customization • Custom Input format class • Hash Partitioner • Custom Partitioner • Sorting techniques • Custom Output format class 10. Hadoop Programming Languages :- I.HIVE • Introduction • Installation and Configuration • Interacting HDFS using HIVE • Map Reduce Programs through HIVE • HIVE Commands • Loading, Filtering, Grouping…. • Data types, Operators….. • Joins, Groups…. • Sample programs in HIVE

HADOOP Training in Hyderabad @Sadguru Technologies Course Objective Summary During this course, you will learn: • Introduction to Big Data and Analytics • Introduction to Hadoop • Hadoop ecosystem - Concepts • Hadoop Map-reduce concepts and features • Developing the map-reduce Applications • Pig concepts • Hive concepts • Sqoop concepts • Flume Concepts • Oozie workflow concepts • Impala Concepts • Hue Concepts • HBASE Concepts • ZooKeeper Concepts • Real Life Use Cases Reporting Tool • Tableau 1. Virtualbox/VM Ware • Basics • Installations • Backups • Snapshots 2. Linux • Basics • Installations • Commands 3. Hadoop • Why Hadoop? • Scaling • Distributed Framework • Hadoop v/s RDBMS • Brief history of hadoop 4. Setup hadoop • Pseudo mode • Cluster mode • Ipv6 • Ssh • Installation of java, hadoop • Configurations of hadoop • Hadoop Processes ( NN, SNN, JT, DN, TT) • Temporary directory • UI • Common errors when running hadoop cluster, solutions 5. HDFS- Hadoop distributed File System • HDFS Design and Architecture • HDFS Concepts • Interacting HDFS using command line • Interacting HDFS using Java APIs • Dataflow • Blocks • Replica 6. Hadoop Processes • Name node • Secondary name node • Job tracker • Task tracker • Data node 7. Map Reduce • Developing Map Reduce Application • Phases in Map Reduce Framework • Map Reduce Input and Output Formats • Advanced Concepts • Sample Applications • Combiner 8. Joining datasets in Mapreduce jobs • Map-side join • Reduce-Side join 9. Map reduce – customization • Custom Input format class • Hash Partitioner • Custom Partitioner • Sorting techniques • Custom Output format class 10. Hadoop Programming Languages :- I.HIVE • Introduction • Installation and Configuration • Interacting HDFS using HIVE • Map Reduce Programs through HIVE • HIVE Commands • Loading, Filtering, Grouping…. • Data types, Operators….. • Joins, Groups…. • Sample programs in HIVE II. PIG • Basics • Installation and Configurations • Commands….

HADOOP Training in Hyderabad @Sadguru Technologies Course Objective Summary During this course, you will learn: • Introduction to Big Data and Analytics • Introduction to Hadoop • Hadoop ecosystem - Concepts • Hadoop Map-reduce concepts and features • Developing the map-reduce Applications • Pig concepts • Hive concepts • Sqoop concepts • Flume Concepts • Oozie workflow concepts • Impala Concepts • Hue Concepts • HBASE Concepts • ZooKeeper Concepts • Real Life Use Cases Reporting Tool • Tableau 1. Virtualbox/VM Ware • Basics • Installations • Backups • Snapshots 2. Linux • Basics • Installations • Commands 3. Hadoop • Why Hadoop? • Scaling • Distributed Framework • Hadoop v/s RDBMS • Brief history of hadoop 4. Setup hadoop • Pseudo mode • Cluster mode • Ipv6 • Ssh • Installation of java, hadoop • Configurations of hadoop • Hadoop Processes ( NN, SNN, JT, DN, TT) • Temporary directory • UI • Common errors when running hadoop cluster, solutions 5. HDFS- Hadoop distributed File System • HDFS Design and Architecture • HDFS Concepts • Interacting HDFS using command line • Interacting HDFS using Java APIs • Dataflow • Blocks • Replica 6. Hadoop Processes • Name node • Secondary name node • Job tracker • Task tracker • Data node 7. Map Reduce • Developing Map Reduce Application • Phases in Map Reduce Framework • Map Reduce Input and Output Formats • Advanced Concepts • Sample Applications • Combiner 8. Joining datasets in Mapreduce jobs • Map-side join • Reduce-Side join 9. Map reduce – customization • Custom Input format class • Hash Partitioner • Custom Partitioner • Sorting techniques • Custom Output format class 10. Hadoop Programming Languages :- I.HIVE • Introduction • Installation and Configuration • Interacting HDFS using HIVE • Map Reduce Programs through HIVE • HIVE Commands • Loading, Filtering,

HADOOP Training in Hyderabad @Sadguru Technologies Course Objective Summary During this course, you will learn: • Introduction to Big Data and Analytics • Introduction to Hadoop • Hadoop ecosystem - Concepts • Hadoop Map-reduce concepts and features • Developing the map-reduce Applications • Pig concepts • Hive concepts • Sqoop concepts • Flume Concepts • Oozie workflow concepts • Impala Concepts • Hue Concepts • HBASE Concepts • ZooKeeper Concepts • Real Life Use Cases Reporting Tool • Tableau 1. Virtualbox/VM Ware • Basics • Installations • Backups • Snapshots 2. Linux • Basics • Installations • Commands 3. Hadoop • Why Hadoop? • Scaling • Distributed Framework • Hadoop v/s RDBMS • Brief history of hadoop 4. Setup hadoop • Pseudo mode • Cluster mode • Ipv6 • Ssh • Installation of java, hadoop • Configurations of hadoop • Hadoop Processes ( NN, SNN, JT, DN, TT) • Temporary directory • UI • Common errors when running hadoop cluster, solutions 5. HDFS- Hadoop distributed File System • HDFS Design and Architecture • HDFS Concepts • Interacting HDFS using command line • Interacting HDFS using Java APIs • Dataflow • Blocks • Replica 6. Hadoop Processes • Name node • Secondary name node • Job tracker • Task tracker • Data node 7. Map Reduce • Developing Map Reduce Application • Phases in Map Reduce Framework • Map Reduce Input and Output Formats • Advanced Concepts • Sample Applications • Combiner 8. Joining datasets in Mapreduce jobs • Map-side join • Reduce-Side join 9. Map reduce – customization • Custom Input format class • Hash Partitioner • Custom Partitioner • Sorting techniques • Custom Output format class 10. Hadoop Programming Languages :- I.HIVE • Introduction • Installation and Configuration • Interacting HDFS using HIVE

HADOOP Training in Hyderabad @Sadguru Technologies Course Objective Summary During this course, you will learn: • Introduction to Big Data and Analytics • Introduction to Hadoop • Hadoop ecosystem - Concepts • Hadoop Map-reduce concepts and features • Developing the map-reduce Applications • Pig concepts • Hive concepts • Sqoop concepts • Flume Concepts • Oozie workflow concepts • Impala Concepts • Hue Concepts • HBASE Concepts • ZooKeeper Concepts • Real Life Use Cases Reporting Tool • Tableau 1. Virtualbox/VM Ware • Basics • Installations • Backups • Snapshots 2. Linux • Basics • Installations • Commands 3. Hadoop • Why Hadoop? • Scaling • Distributed Framework • Hadoop v/s RDBMS • Brief history of hadoop 4. Setup hadoop • Pseudo mode • Cluster mode • Ipv6 • Ssh • Installation of java, hadoop • Configurations of hadoop • Hadoop Processes ( NN, SNN, JT, DN, TT) • Temporary directory • UI • Common errors when running hadoop cluster, solutions 5. HDFS- Hadoop distributed File System • HDFS Design and Architecture • HDFS Concepts • Interacting HDFS using command line • Interacting HDFS using Java APIs • Dataflow • Blocks • Replica 6. Hadoop Processes • Name node • Secondary name node • Job tracker • Task tracker

HADOOP Training in Hyderabad @Sadguru Technologies Course Objective Summary During this course, you will learn: • Introduction to Big Data and Analytics • Introduction to Hadoop • Hadoop ecosystem - Concepts • Hadoop Map-reduce concepts and features • Developing the map-reduce Applications • Pig concepts • Hive concepts • Sqoop concepts • Flume Concepts • Oozie workflow concepts • Impala Concepts • Hue Concepts • HBASE Concepts • ZooKeeper Concepts • Real Life Use Cases Reporting Tool • Tableau 1. Virtualbox/VM Ware • Basics • Installations • Backups • Snapshots 2. Linux • Basics • Installations • Commands 3. Hadoop • Why Hadoop? • Scaling • Distributed Framework • Hadoop v/s RDBMS • Brief history of hadoop 4. Setup hadoop • Pseudo mode • Cluster mode • Ipv6 • Ssh • Installation of java, hadoop • Configurations of hadoop • Hadoop Processes ( NN, SNN, JT, DN, TT) • Temporary directory • UI • Common errors when running hadoop cluster, solutions 5. HDFS- Hadoop distributed File System • HDFS Design and Architecture • HDFS Concepts • Interacting HDFS using command line • Interacting HDFS using Java APIs • Dataflow • Blocks

HADOOP Training in Hyderabad @Sadguru Technologies Course Objective Summary During this course, you will learn: • Introduction to Big Data and Analytics • Introduction to Hadoop • Hadoop ecosystem - Concepts • Hadoop Map-reduce concepts and features • Developing the map-reduce Applications • Pig concepts • Hive concepts • Sqoop concepts • Flume Concepts • Oozie workflow concepts • Impala Concepts • Hue Concepts • HBASE Concepts • ZooKeeper Concepts • Real Life Use Cases Reporting Tool • Tableau 1. Virtualbox/VM Ware • Basics • Installations • Backups • Snapshots 2. Linux • Basics • Installations • Commands 3. Hadoop • Why Hadoop? • Scaling • Distributed Framework • Hadoop v/s RDBMS • Brief history of hadoop 4. Setup hadoop • Pseudo mode • Cluster mode • Ipv6 • Ssh • Installation of java, hadoop • Configurations of hadoop • Hadoop Processes ( NN, SNN, JT, DN, TT) • Temporary directory • UI • Common errors when running hadoop cluster, solutions 5. HDFS- Hadoop distributed File System • HDFS Design and Architecture • HDFS Concepts • Interacting HDFS using command line • Interacting HDFS using Java APIs • Dataflow • Blocks • Replica 6. Hadoop Processes • Name node • Secondary name node • Job tracker • Task tracker • Data node 7. Map Reduce • Developing Map Reduce Application • Phases in Map Reduce Framework • Map Reduce Input and Output Formats • Advanced Concepts • Sample Applications • Combiner 8. Joining datasets in Mapreduce jobs • Map-side join • Reduce-Side join 9. Map reduce – customization • Custom Input format class • Hash Partitioner • Custom Partitioner • Sorting techniques • Custom

HADOOP Training in Hyderabad @Sadguru Technologies Course Objective Summary During this course, you will learn: • Introduction to Big Data and Analytics • Introduction to Hadoop • Hadoop ecosystem - Concepts • Hadoop Map-reduce concepts and features • Developing the map-reduce Applications • Pig concepts • Hive concepts • Sqoop concepts • Flume Concepts • Oozie workflow concepts • Impala Concepts • Hue Concepts • HBASE Concepts • ZooKeeper Concepts • Real Life Use Cases Reporting Tool • Tableau 1. Virtualbox/VM Ware • Basics • Installations • Backups • Snapshots 2. Linux • Basics • Installations • Commands 3. Hadoop • Why Hadoop? • Scaling • Distributed Framework • Hadoop v/s RDBMS • Brief history of hadoop 4. Setup hadoop • Pseudo mode • Cluster mode • Ipv6 • Ssh • Installation of java, hadoop • Configurations of hadoop • Hadoop Processes ( NN, SNN, JT, DN, TT) • Temporary directory • UI • Common errors when running hadoop cluster, solutions 5. HDFS- Hadoop distributed File System • HDFS Design and Architecture • HDFS Concepts • Interacting HDFS using command line • Interacting HDFS using Java APIs • Dataflow • Blocks • Replica 6. Hadoop Processes • Name node • Secondary name node • Job tracker • Task tracker • Data node 7. Map Reduce • Developing Map Reduce Application • Phases in Map Reduce Framework • Map Reduce Input and Output Formats • Advanced Concepts • Sample Applications • Combiner 8. Joining datasets in Mapreduce jobs • Map-side join • Reduce-Side join and...

HADOOP Training in Hyderabad @Sadguru Technologies Course Objective Summary During this course, you will learn: • Introduction to Big Data and Analytics • Introduction to Hadoop • Hadoop ecosystem - Concepts • Hadoop Map-reduce concepts and features • Developing the map-reduce Applications • Pig concepts • Hive concepts • Sqoop concepts • Flume Concepts • Oozie workflow concepts • Impala Concepts • Hue Concepts • HBASE Concepts • ZooKeeper Concepts • Real Life Use Cases Reporting Tool • Tableau 1. Virtualbox/VM Ware • Basics • Installations • Backups • Snapshots 2. Linux • Basics • Installations • Commands 3. Hadoop • Why Hadoop? • Scaling • Distributed Framework • Hadoop v/s RDBMS • Brief history of hadoop 4. Setup hadoop • Pseudo mode • Cluster mode • Ipv6 • Ssh • Installation of java, hadoop • Configurations of hadoop • Hadoop Processes ( NN, SNN, JT, DN, TT) • Temporary directory • UI • Common errors when running hadoop cluster, solutions 5. HDFS- Hadoop distributed File System • HDFS Design and Architecture • HDFS Concepts • Interacting HDFS using command line • Interacting HDFS using Java APIs • Dataflow • Blocks • Replica 6. Hadoop Processes • Name node • Secondary name node • Job tracker • Task tracker • Data node 7. Map Reduce • Developing Map Reduce Application • Phases in Map Reduce Framework • Map Reduce Input and Output Formats • Advanced Concepts • Sample Applications • Combiner 8. Joining datasets in Mapreduce jobs • Map-side join • Reduce

HADOOP Training in Hyderabad @Sadguru Technologies Course Objective Summary During this course, you will learn: • Introduction to Big Data and Analytics • Introduction to Hadoop • Hadoop ecosystem - Concepts • Hadoop Map-reduce concepts and features • Developing the map-reduce Applications • Pig concepts • Hive concepts • Sqoop concepts • Flume Concepts • Oozie workflow concepts • Impala Concepts • Hue Concepts • HBASE Concepts • ZooKeeper Concepts • Real Life Use Cases Reporting Tool • Tableau 1. Virtualbox/VM Ware • Basics • Installations • Backups • Snapshots 2. Linux • Basics • Installations • Commands 3. Hadoop • Why Hadoop? • Scaling • Distributed Framework • Hadoop v/s RDBMS • Brief history of hadoop 4. Setup hadoop • Pseudo mode • Cluster mode • Ipv6 • Ssh • Installation of java, hadoop • Configurations of hadoop • Hadoop Processes ( NN, SNN, JT, DN, TT) • Temporary directory • UI • Common errors when running hadoop cluster, solutions 5. HDFS- Hadoop distributed File System • HDFS Design and Architecture • HDFS Concepts