Hadoop admin training
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This document outlines the course content for a Hadoop Administration course. It covers topics such as introducing Big Data concepts, understanding Hadoop and HDFS, the MapReduce framework, planning and maintaining Hadoop clusters, installing Hadoop ecosystem tools, managing jobs, monitoring clusters, troubleshooting issues, and populating HDFS from external sources. Contact arun87532@gmail.com for inquiries about hadoop development, administration, testing, or advanced Hadoop topics.
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1. What is Hadoop ecosystem?
1. Pig (Scripting)
2. Hive (SQL queries)
3. Apache Spark (Real-time data analysis)
4. Mahout (Machine learning)
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What are the course objectives?
This course will enable you to:
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6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
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This training course provides an in-depth overview of Hadoop and Amazon Web Services MapReduce. It covers Hadoop architecture and components like HDFS, MapReduce, YARN and Hadoop ecosystem projects. Exercises include hands-on with Hadoop cluster setup in single and multi-node modes, loading and processing data using MapReduce, Hive, Pig and HBase. The course also discusses deploying Hadoop clusters, administration, use cases, cloud computing with Hadoop and certification preparation.
Hadoop and aws map reducecourse
This document outlines the details of a training course on Hadoop and Amazon Web Services MapReduce that covers topics such as Hadoop architecture, HDFS, MapReduce programming, Hadoop ecosystem projects, Hadoop deployment, administration, and use cases. The course includes exercises in configuring Hadoop clusters, loading and processing data, and Amazon MapReduce setup. Contact information is provided for those interested in the Hadoop certification training.
Hadoop and aws short
This document outlines the details of a Hadoop and Amazon Web Services MapReduce training course, including exercises on Hadoop virtual machine setup, HDFS data loading and unloading, Amazon MapReduce setup, and Pig, Hive, HBase, and MapReduce examples. The course covers Hadoop architecture, HDFS, MapReduce programming, Hadoop streaming, Amazon MapReduce, the Hadoop ecosystem, deployment, administration, use cases, and integration with cloud computing. It provides exam preparation help and certification materials for Cloudera certifications, and lists contact information for the training provider.
Hadoop and aws short
This document outlines the details of a Hadoop and Amazon Web Services MapReduce training course, including exercises on Hadoop virtual machine setup, HDFS data loading and unloading, Amazon MapReduce setup, and Pig, Hive, HBase, and MapReduce examples. The course covers Hadoop architecture, HDFS, MapReduce programming, Hadoop streaming, Amazon MapReduce, the Hadoop ecosystem, deployment, administration, use cases, and integration with cloud computing. It provides exam preparation help and certification materials for Cloudera certifications, and lists contact information for the training provider.
Design of Hadoop Distributed File System
This presentation discusses the following topics:
Hadoop Distributed File System (HDFS)
How does HDFS work?
HDFS Architecture
Features of HDFS
Benefits of using HDFS
Examples: Target Marketing
HDFS data replication
Hadoop file
The document discusses the Hadoop File System (HDFS), which is an Apache project that uses an open-source distributed file system across large clusters of nodes. HDFS consists of two types of daemons - the NameNode, which acts as the primary administrator, and DataNodes, which are on each node in the distributed file system. Condor can manage the HDFS daemons and allow Hadoop jobs to directly access HDFS through the job configuration file.
Cred_hadoop_presenatation
This document discusses big data and how Hadoop solves big data problems. It first describes where big data comes from and the challenges of big data's size, unstructured nature, and analysis. It then explains how Hadoop uses a cluster of machines to handle unstructured data at scale through horizontal scaling. Finally, it provides examples of how Hadoop can help solve problems in retail and manufacturing by analyzing large amounts of diverse data.
Hadoop Developer
In YARN, the functionality of JobTracker has been replaced by ResourceManager and ApplicationMaster.
The ResourceManager replaces the JobTracker and manages the resources across the cluster. It schedules the applications on the nodes based on their resource requirements and availability.
The ApplicationMaster coordinates and manages the execution of individual applications submitted to YARN, such as MapReduce jobs. It negotiates resources from the ResourceManager and works with the NodeManagers to execute and monitor the tasks.
So in summary, the JobTracker's functionality is replaced by:
- ResourceManager (for resource management and scheduling)
- ApplicationMaster (for coordinating individual application execution)
HDFS
The report discusses the key components and objectives of HDFS, including data replication for fault tolerance, HDFS architecture with a NameNode and DataNodes, and HDFS properties like large data sets, write once read many model, and commodity hardware. It provides an overview of HDFS and its design to reliably store and retrieve large volumes of distributed data.
How Hadoop Revolutionized Data Warehousing at Yahoo and Facebook
Hadoop was developed to solve problems with data warehousing systems at Yahoo and Facebook that were limited in processing large amounts of raw data in real-time. Hadoop uses HDFS for scalable storage and MapReduce for distributed processing. It allows for agile access to raw data at scale for ad-hoc queries, data mining and analytics without being constrained by traditional database schemas. Hadoop has been widely adopted for large-scale data processing and analytics across many companies.
Managing Big data with Hadoop
The data management industry has matured over the last three decades, primarily based on relational database management system(RDBMS) technology. Since the amount of data collected, and analyzed in enterprises has increased several folds in volume, variety and velocityof generation and consumption, organisations have started struggling with architectural limitations of traditional RDBMS architecture. As a result a new class of systems had to be designed and implemented, giving rise to the new phenomenon of “Big Data”. In this paper we will trace the origin of new class of system called Hadoop to handle Big data.
Hadoop file
The document discusses the Hadoop File System (HDFS), which is an Apache project that uses an open-source distributed file system across large clusters of nodes. HDFS consists of two types of daemons - the NameNode, which acts as the primary administrator, and DataNodes, which are on each node in the distributed file system. Condor can manage the HDFS daemons and allow Hadoop jobs to directly access HDFS through the job configuration file.
Hadoop
Hadoop is a framework that allows storing and processing of big data in a distributed environment. It consists of HDFS for storage of large amounts of data across clusters and YARN for managing resources and parallel processing of the stored data. HDFS stores data in blocks across data nodes and replicates the blocks for reliability. YARN schedules tasks across resource managers and node managers. Hadoop provides solutions for challenges of big data like storing large and varied data types, and processing the data faster by moving computation to the data rather than moving data to a central processor.
Hadoop online training
This document outlines the topics and exercises covered in a Hadoop and Amazon Web Service-Map Reduce Complete Training course. The course covers: 1) an introduction to Hadoop and its architecture; 2) HDFS and MapReduce programming; 3) Hadoop Streaming and Amazon MapReduce; 4) the Hadoop ecosystem including Hive, Pig, HBase, Sqoop, Flume, and Oozie; 5) Hadoop deployment, administration, and cluster configuration; 6) business cases for using and not using Hadoop; 7) Hadoop and cloud computing on AWS; and 8) certification exam preparation. Hands-on exercises include Hadoop VM setup, HDFS data loading/unloading, AWS MapReduce
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Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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Hadoop admin training
- 1. For hadoopdevelopment/admin/testing/advancedHadoopcontactarun87532@gmail.com HADOOP Administration Course content Introduction to Big Data What is Big Data ? Big Data Facts The Three V’s of Big Data Understanding Hadoop What is Hadoop ? Why learn Hadoop ? Relational Databases Vs. Hadoop Motivation for Hadoop 6 Key Hadoop Data Types The Hadoop Distributed File system(HDFS) What is HDFS ? HDFS components Understanding Block storage The Name Node The Data Nodes Data Node Failures HDFS Commands HDFS File Permissions The MapReduce Framework Overview of MapReduce Understanding MapReduce The Map Phase The Reduce Phase WordCount in MapReduce Running MapReduce Job Planning Your Hadoop Cluster Single Node Cluster Configuration Multi-Node Cluster Configuration Cluster Maintenance Checking HDFS Status Breaking the cluster Copying Data Between Clusters Adding and Removing Cluster Nodes
- 2. For hadoopdevelopment/admin/testing/advancedHadoopcontactarun87532@gmail.com Rebalancing the cluster Name Node Metadata Backup Cluster Upgrading Installing and Managing Hadoop Ecosystem Sqoop Flume Hive Pig HBase Oozie Zookeeper Spark Hue Managing and Scheduling Jobs Managing Jobs The FIFO Scheduler The Fair Schedule How to stop and start jobs running on the cluster Cluster Monitoring, Troubleshooting, and Optimizing General System conditions to Monitor Name Node and Job Tracker Web Uis View and Manage Hadoop’s Log files Ganglia Monitoring Tool Common cluster issues and their resolutions Benchmark your cluster’s performance Populating HDFS from External Sources How to use Sqoop to import data from RDBMS to HDFS How to gather logs from multiple systems using Flume Features of Hive, Hbase and Pig How to populate HDFS from external Sources