HDFS is a Java-based file system that provides scalable and reliable data storage, and it was designed to span large clusters of commodity servers. HDFS has demonstrated production scalability of up to 200 PB of storage and a single cluster of 4500 servers, supporting close to a billion files and blocks.
A MapReduce job usually splits the input data-set into independent chunks which are processed by the map tasks in a completely parallel manner. The framework sorts the outputs of the maps, which are then input to the reduce tasks. Typically both the input and the output of the job are stored in a file-system.
This presentation discusses the follow topics
What is Hadoop?
Need for Hadoop
History of Hadoop
Hadoop Overview
Advantages and Disadvantages of Hadoop
Hadoop Distributed File System
Comparing: RDBMS vs. Hadoop
Advantages and Disadvantages of HDFS
Hadoop frameworks
Modules of Hadoop frameworks
Features of 'Hadoop‘
Hadoop Analytics Tools
This presentation about Hadoop architecture will help you understand the architecture of Apache Hadoop in detail. In this video, you will learn what is Hadoop, components of Hadoop, what is HDFS, HDFS architecture, Hadoop MapReduce, Hadoop MapReduce example, Hadoop YARN and finally, a demo on MapReduce. Apache Hadoop offers a versatile, adaptable and reliable distributed computing big data framework for a group of systems with capacity limit and local computing power. After watching this video, you will also understand the Hadoop Distributed File System and its features along with the practical implementation.
Below are the topics covered in this Hadoop Architecture presentation:
1. What is Hadoop?
2. Components of Hadoop
3. What is HDFS?
4. HDFS Architecture
5. Hadoop MapReduce
6. Hadoop MapReduce Example
7. Hadoop YARN
8. Demo on MapReduce
What are the course objectives?
This course will enable you to:
1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Who should take up this Big Data and Hadoop Certification Training Course?
Big Data career opportunities are on the rise, and Hadoop is quickly becoming a must-know technology for the following professionals:
1. Software Developers and Architects
2. Analytics Professionals
3. Senior IT professionals
4. Testing and Mainframe professionals
5. Data Management Professionals
6. Business Intelligence Professionals
7. Project Managers
8. Aspiring Data Scientists
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
A MapReduce job usually splits the input data-set into independent chunks which are processed by the map tasks in a completely parallel manner. The framework sorts the outputs of the maps, which are then input to the reduce tasks. Typically both the input and the output of the job are stored in a file-system.
This presentation discusses the follow topics
What is Hadoop?
Need for Hadoop
History of Hadoop
Hadoop Overview
Advantages and Disadvantages of Hadoop
Hadoop Distributed File System
Comparing: RDBMS vs. Hadoop
Advantages and Disadvantages of HDFS
Hadoop frameworks
Modules of Hadoop frameworks
Features of 'Hadoop‘
Hadoop Analytics Tools
This presentation about Hadoop architecture will help you understand the architecture of Apache Hadoop in detail. In this video, you will learn what is Hadoop, components of Hadoop, what is HDFS, HDFS architecture, Hadoop MapReduce, Hadoop MapReduce example, Hadoop YARN and finally, a demo on MapReduce. Apache Hadoop offers a versatile, adaptable and reliable distributed computing big data framework for a group of systems with capacity limit and local computing power. After watching this video, you will also understand the Hadoop Distributed File System and its features along with the practical implementation.
Below are the topics covered in this Hadoop Architecture presentation:
1. What is Hadoop?
2. Components of Hadoop
3. What is HDFS?
4. HDFS Architecture
5. Hadoop MapReduce
6. Hadoop MapReduce Example
7. Hadoop YARN
8. Demo on MapReduce
What are the course objectives?
This course will enable you to:
1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Who should take up this Big Data and Hadoop Certification Training Course?
Big Data career opportunities are on the rise, and Hadoop is quickly becoming a must-know technology for the following professionals:
1. Software Developers and Architects
2. Analytics Professionals
3. Senior IT professionals
4. Testing and Mainframe professionals
5. Data Management Professionals
6. Business Intelligence Professionals
7. Project Managers
8. Aspiring Data Scientists
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
Hadoop is an open-source software framework for storing data and running applications on clusters of commodity hardware.
It provides massive storage for any kind of data, enormous processing power and the ability to handle virtually limitless concurrent tasks or jobs. The core of Apache Hadoop consists of a storage part (HDFS) and a processing part (MapReduce).
Hadoop Training | Hadoop Training For Beginners | Hadoop Architecture | Hadoo...Simplilearn
This presentation about Hadoop training will help you understand the need for Hadoop, what is Hadoop and concepts including Hadoop ecosystem, Hadoop features, how HDFS works, what is MapReduce and how YARN works. Finally, we will implement a banking case study using Hadoop. To solve the issue of rapidly increasing data, we need big data technologies such as Hadoop, Spark, Storm, Cassandra and many more. Hadoop can store and process vast volumes of data. You will understand the architecture of HDFS, MapReduce workflow and the architecture of YARN. In the demo, you will learn in detail on how to export data from RDBMS (MySQL) into HDFS using Sqoop commands. Now, let us get started and gain expertise with Hadoop training video.
Below topics are explained in this Hadoop training presentation:
1. Need for Hadoop
2. What is Hadoop
3. Hadoop ecosystem
4. Hadoop features
5. What is HDFS
6. What is MapReduce
7. What is YARN
8. Bank case study
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
Hadoop Tutorial For Beginners | Apache Hadoop Tutorial For Beginners | Hadoop...Simplilearn
This presentation about Hadoop for beginners will help you understand what is Hadoop, why Hadoop, what is Hadoop HDFS, Hadoop MapReduce, Hadoop YARN, a use case of Hadoop and finally a demo on HDFS (Hadoop Distributed File System), MapReduce and YARN. Big Data is a massive amount of data which cannot be stored, processed, and analyzed using traditional systems. To overcome this problem, we use Hadoop. Hadoop is a framework which stores and handles Big Data in a distributed and parallel fashion. Hadoop overcomes the challenges of Big Data. Hadoop has three components HDFS, MapReduce, and YARN. HDFS is the storage unit of Hadoop, MapReduce is its processing unit, and YARN is the resource management unit of Hadoop. In this video, we will look into these units individually and also see a demo on each of these units.
Below topics are explained in this Hadoop presentation:
1. What is Hadoop
2. Why Hadoop
3. Big Data generation
4. Hadoop HDFS
5. Hadoop MapReduce
6. Hadoop YARN
7. Use of Hadoop
8. Demo on HDFS, MapReduce and YARN
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
Hive Tutorial | Hive Architecture | Hive Tutorial For Beginners | Hive In Had...Simplilearn
This presentation about Hive will help you understand the history of Hive, what is Hive, Hive architecture, data flow in Hive, Hive data modeling, Hive data types, different modes in which Hive can run on, differences between Hive and RDBMS, features of Hive and a demo on HiveQL commands. Hive is a data warehouse system which is used for querying and analyzing large datasets stored in HDFS. Hive uses a query language called HiveQL which is similar to SQL. Hive issues SQL abstraction to integrate SQL queries (like HiveQL) into Java without the necessity to implement queries in the low-level Java API. Now, let us get started and understand Hadoop Hive in detail
Below topics are explained in this Hive presetntation:
1. History of Hive
2. What is Hive?
3. Architecture of Hive
4. Data flow in Hive
5. Hive data modeling
6. Hive data types
7. Different modes of Hive
8. Difference between Hive and RDBMS
9. Features of Hive
10. Demo on HiveQL
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
Apache HBase™ is the Hadoop database, a distributed, salable, big data store.Its a column-oriented database management system that runs on top of HDFS.
Apache HBase is an open source NoSQL database that provides real-time read/write access to those large data sets. ... HBase is natively integrated with Hadoop and works seamlessly alongside other data access engines through YARN.
This Hadoop will help you understand the different tools present in the Hadoop ecosystem. This Hadoop video will take you through an overview of the important tools of Hadoop ecosystem which include Hadoop HDFS, Hadoop Pig, Hadoop Yarn, Hadoop Hive, Apache Spark, Mahout, Apache Kafka, Storm, Sqoop, Apache Ranger, Oozie and also discuss the architecture of these tools. It will cover the different tasks of Hadoop such as data storage, data processing, cluster resource management, data ingestion, machine learning, streaming and more. Now, let us get started and understand each of these tools in detail.
Below topics are explained in this Hadoop ecosystem presentation:
1. What is Hadoop ecosystem?
1. Pig (Scripting)
2. Hive (SQL queries)
3. Apache Spark (Real-time data analysis)
4. Mahout (Machine learning)
5. Apache Ambari (Management and monitoring)
6. Kafka & Storm
7. Apache Ranger & Apache Knox (Security)
8. Oozie (Workflow system)
9. Hadoop MapReduce (Data processing)
10. Hadoop Yarn (Cluster resource management)
11. Hadoop HDFS (Data storage)
12. Sqoop & Flume (Data collection and ingestion)
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Learn Spark SQL, creating, transforming, and querying Data frames
14. Understand the common use-cases of Spark and the various interactive algorithms
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training.
All about Big Data components and the best tools to ingest, process, store and visualize the data.
This is a keynote from the series "by Developer for Developers" powered by eSolutionsGrup.
The presentation covers following topics: 1) Hadoop Introduction 2) Hadoop nodes and daemons 3) Architecture 4) Hadoop best features 5) Hadoop characteristics. For more further knowledge of Hadoop refer the link: http://data-flair.training/blogs/hadoop-tutorial-for-beginners/
Introduction to Big Data Hadoop Training Online by www.itjobzone.bizITJobZone.biz
Want to learn Hadoop online? This PPT give you Introduction to Big Data Hadoop Training Online by expert trainers at ITJobZone.biz - Start your Hadoop Online training with this Presentation.
Hadoop is an open-source software framework for storing data and running applications on clusters of commodity hardware.
It provides massive storage for any kind of data, enormous processing power and the ability to handle virtually limitless concurrent tasks or jobs. The core of Apache Hadoop consists of a storage part (HDFS) and a processing part (MapReduce).
Hadoop Training | Hadoop Training For Beginners | Hadoop Architecture | Hadoo...Simplilearn
This presentation about Hadoop training will help you understand the need for Hadoop, what is Hadoop and concepts including Hadoop ecosystem, Hadoop features, how HDFS works, what is MapReduce and how YARN works. Finally, we will implement a banking case study using Hadoop. To solve the issue of rapidly increasing data, we need big data technologies such as Hadoop, Spark, Storm, Cassandra and many more. Hadoop can store and process vast volumes of data. You will understand the architecture of HDFS, MapReduce workflow and the architecture of YARN. In the demo, you will learn in detail on how to export data from RDBMS (MySQL) into HDFS using Sqoop commands. Now, let us get started and gain expertise with Hadoop training video.
Below topics are explained in this Hadoop training presentation:
1. Need for Hadoop
2. What is Hadoop
3. Hadoop ecosystem
4. Hadoop features
5. What is HDFS
6. What is MapReduce
7. What is YARN
8. Bank case study
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
Hadoop Tutorial For Beginners | Apache Hadoop Tutorial For Beginners | Hadoop...Simplilearn
This presentation about Hadoop for beginners will help you understand what is Hadoop, why Hadoop, what is Hadoop HDFS, Hadoop MapReduce, Hadoop YARN, a use case of Hadoop and finally a demo on HDFS (Hadoop Distributed File System), MapReduce and YARN. Big Data is a massive amount of data which cannot be stored, processed, and analyzed using traditional systems. To overcome this problem, we use Hadoop. Hadoop is a framework which stores and handles Big Data in a distributed and parallel fashion. Hadoop overcomes the challenges of Big Data. Hadoop has three components HDFS, MapReduce, and YARN. HDFS is the storage unit of Hadoop, MapReduce is its processing unit, and YARN is the resource management unit of Hadoop. In this video, we will look into these units individually and also see a demo on each of these units.
Below topics are explained in this Hadoop presentation:
1. What is Hadoop
2. Why Hadoop
3. Big Data generation
4. Hadoop HDFS
5. Hadoop MapReduce
6. Hadoop YARN
7. Use of Hadoop
8. Demo on HDFS, MapReduce and YARN
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
Hive Tutorial | Hive Architecture | Hive Tutorial For Beginners | Hive In Had...Simplilearn
This presentation about Hive will help you understand the history of Hive, what is Hive, Hive architecture, data flow in Hive, Hive data modeling, Hive data types, different modes in which Hive can run on, differences between Hive and RDBMS, features of Hive and a demo on HiveQL commands. Hive is a data warehouse system which is used for querying and analyzing large datasets stored in HDFS. Hive uses a query language called HiveQL which is similar to SQL. Hive issues SQL abstraction to integrate SQL queries (like HiveQL) into Java without the necessity to implement queries in the low-level Java API. Now, let us get started and understand Hadoop Hive in detail
Below topics are explained in this Hive presetntation:
1. History of Hive
2. What is Hive?
3. Architecture of Hive
4. Data flow in Hive
5. Hive data modeling
6. Hive data types
7. Different modes of Hive
8. Difference between Hive and RDBMS
9. Features of Hive
10. Demo on HiveQL
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
Apache HBase™ is the Hadoop database, a distributed, salable, big data store.Its a column-oriented database management system that runs on top of HDFS.
Apache HBase is an open source NoSQL database that provides real-time read/write access to those large data sets. ... HBase is natively integrated with Hadoop and works seamlessly alongside other data access engines through YARN.
This Hadoop will help you understand the different tools present in the Hadoop ecosystem. This Hadoop video will take you through an overview of the important tools of Hadoop ecosystem which include Hadoop HDFS, Hadoop Pig, Hadoop Yarn, Hadoop Hive, Apache Spark, Mahout, Apache Kafka, Storm, Sqoop, Apache Ranger, Oozie and also discuss the architecture of these tools. It will cover the different tasks of Hadoop such as data storage, data processing, cluster resource management, data ingestion, machine learning, streaming and more. Now, let us get started and understand each of these tools in detail.
Below topics are explained in this Hadoop ecosystem presentation:
1. What is Hadoop ecosystem?
1. Pig (Scripting)
2. Hive (SQL queries)
3. Apache Spark (Real-time data analysis)
4. Mahout (Machine learning)
5. Apache Ambari (Management and monitoring)
6. Kafka & Storm
7. Apache Ranger & Apache Knox (Security)
8. Oozie (Workflow system)
9. Hadoop MapReduce (Data processing)
10. Hadoop Yarn (Cluster resource management)
11. Hadoop HDFS (Data storage)
12. Sqoop & Flume (Data collection and ingestion)
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Learn Spark SQL, creating, transforming, and querying Data frames
14. Understand the common use-cases of Spark and the various interactive algorithms
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training.
All about Big Data components and the best tools to ingest, process, store and visualize the data.
This is a keynote from the series "by Developer for Developers" powered by eSolutionsGrup.
The presentation covers following topics: 1) Hadoop Introduction 2) Hadoop nodes and daemons 3) Architecture 4) Hadoop best features 5) Hadoop characteristics. For more further knowledge of Hadoop refer the link: http://data-flair.training/blogs/hadoop-tutorial-for-beginners/
Introduction to Big Data Hadoop Training Online by www.itjobzone.bizITJobZone.biz
Want to learn Hadoop online? This PPT give you Introduction to Big Data Hadoop Training Online by expert trainers at ITJobZone.biz - Start your Hadoop Online training with this Presentation.
Vikram Andem Big Data Strategy @ IATA Technology Roadmap IT Strategy Group
Vikram Andem, Senior Manager, United Airlines, A case for Bigdata Program and Strategy @ IATA Technology Roadmap 2014, October 13th, 2014, Montréal, Canada
Voldemort & Hadoop @ Linkedin, Hadoop User Group Jan 2010Bhupesh Bansal
Jan 22nd, 2010 Hadoop meetup presentation on project voldemort and how it plays well with Hadoop at linkedin. The talk focus on Linkedin Hadoop ecosystem. How linkedin manage complex workflows, data ETL , data storage and online serving of 100GB to TB of data.
this presentation describes the company from where I did my summer training and what is bigdata why we use big data, big data challenges, the issue in big data, the solution of big data issues, hadoop, docker , Ansible etc.
Hadoop Online Training : kelly technologies is the bestHadoop online Training Institutes in Bangalore. ProvidingHadoop online Training by real time faculty in Bangalore.
Fundamentals of Big Data, Hadoop project design and case study or Use case
General planning consideration and most necessaries in Hadoop ecosystem and Hadoop projects
This will provide the basis for choosing the right Hadoop implementation, Hadoop technologies integration, adoption and creating an infrastructure.
Building applications using Apache Hadoop with a use-case of WI-FI log analysis has real life example.
Construindo Data Lakes - Visão Prática com Hadoop e BigDataMarco Garcia
Minha apresentação sobre construção de data lakes para bigdata usando hadoop como plataforma de dados. Conheça mais sobre nossos trabalhos de consultoria e treinamento em Hadoop Hortonworks, BigData, Data Warehousing e Business Intelligence
Apache Pig is a high-level platform for creating programs that runs on Apache Hadoop. The language for this platform is called Pig Latin. Pig can execute its Hadoop jobs in MapReduce, Apache Tez, or Apache Spark.
Apache Sqoop efficiently transfers bulk data between Apache Hadoop and structured datastores such as relational databases. Sqoop helps offload certain tasks (such as ETL processing) from the EDW to Hadoop for efficient execution at a much lower cost. Sqoop can also be used to extract data from Hadoop and export it into external structured datastores. Sqoop works with relational databases such as Teradata, Netezza, Oracle, MySQL, Postgres, and HSQLDB
Apache Hive is a data warehouse infrastructure built on top of Hadoop for providing data summarization, query, and analysis. While developed by Facebook.
MongoDB is an open-source document database, and the leading NoSQL database. Written in C++.
MongoDB has official drivers for a variety of popular programming languages and development environments. There are also a large number of unofficial or community-supported drivers for other programming languages and frameworks.
SonarQube is an open platform to manage code quality. It has got a very efficient way of navigating, a balance between high-level view, dashboard, TimeMachine and defect hunting tools.
SonarQube tool is a web-based application. Rules, alerts, thresholds, exclusions, settings… can be configured online.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
2. What is Big Data?
Limitations of the existing solutions
Solving the problem with Hadoop
Introduction to Hadoop
Hadoop Eco-System
Hadoop Core Components
HDFS Architecture
Anatomy of a File Write and Read
Topics of the Day
Slide 2
3. What is Big Data?
Slide 3
Lots of Data (Terabytes or Petabytes)
Big data is the term for a collection of data sets so
large and complex that it becomes difficult to
process using on-hand database management
tools or traditional data processing applications.
The challenges include capture, curation,
storage, search, sharing, transfer, analysis, and
visualization.
cloud
tools
statistics
No SQL
compression
storage
support
database
analyze
information
terabytes
mobile
processing
Big Data
4. What is Big Data?
Slide 4
Systems / Enterprises generate huge amount of data from Terabytes to and even Petabytes of information.
NYSE generates about one terabyte of new
trade data per day to perform stock trading
analytics to determine trends for optimal
trades.
6. IBM’s Definition – Big Data Characteristics
http://www-01.ibm.com/software/data/bigdata/
Web
logs
Videos
Images
Audios
Sensor
Data
Slide 6
Volume Velocity Variety
IBM’s Definition
7. Web and e-tailing
Recommendation Engines
Ad Targeting
Search Quality
Abuse and Click Fraud Detection
Telecommunications
Customer Churn Prevention
Network Performance Optimization
Calling Data Record (CDR) Analysis
Analyzing Network to Predict Failure
Common Big Data Customer Scenarios
Slide 7
8. Government
Fraud Detection and Cyber Security
Welfare Schemes
Justice
Healthcare & Life Sciences
Health Information Exchange
Gene Sequencing
Serialization
Healthcare Service Quality Improvements
Drug Safety
Common Big Data Customer Scenarios(Contd.)
Slide 8
9. Common Big Data Customer Scenarios Contd.)
Slide 9
Banks and Financial services
Modeling True Risk
Threat Analysis
Fraud Detection
Trade Surveillance
Credit Scoring and Analysis
Retail
Point of Sales Transaction Analysis
Customer Churn Analysis
Sentiment Analysis
10. Hidden Treasure
Case Study: Sears Holding Corporation
X
*Sears was using traditional systems such as Oracle Exadata,
Teradata and SAS etc. to store and process the customer activity
and sales data.
Slide 17
Insight into data can provide Business Advantage.
Some key early indicators can mean Fortunes to Business.
More Precise Analysis with more data.
12. *Sears moved to a 300-Node Hadoop cluster to keep 100% of its data available for processing rather
than a meagre 10% as was the case with existing Non-Hadoop solutions.
RDBMS (Aggregated Data)
No Data
Archiving
1. Data Exploration &
Advanced analytics
2. Scalable throughput for ETL &
aggregation
3. Keep data alive
forever
Mostly Append
Collection
Instrumentation
BI Reports + Interactive Apps
Hadoop : Storage + Compute Grid
Entire ~2PB
Data is
available for
processing
Both
Storage
And
Processing
Solution: A Combined Storage Computer Layer
Slide 12
14. RDBMS EDW
Hadoop – It’s about Scale and Structure
Slide 14
MPP NoSQL HADOOP
Structured Data Types Multi and Unstructured
Limited, No Data Processing Processing Processing coupled with Data
Standards & Structured Governance Loosely Structured
Required On write Schema Required On Read
Software License Cost Support Only
Known Entity Resources Growing, Complexities, Wide
Interactive OLAP Analytics
Operational Data Store
Best Fit Use Massive Storage/Processing
15. Read 1 TB Data
1 Machine
4 I/O Channels
Each Channel – 100 MB/s
10 Machines
4 I/O Channels
Each Channel – 100 MB/s
Why DFS?
Slide 15
18. What is Hadoop?
Slide 18
Apache Hadoop is a framework that allows for the distributed processing of large data sets across
clusters of commodity computers using a simple programming model.
It is an Open-source Data Management with scale-out storage & distributed processing.
19. Apache Oozie (Workflow)
MapReduce Framework
HBase
HDFS (Hadoop Distributed File System)
Flume Sqoop
Import Or Export
Pig Latin
Data Analysis
Mahout
Machine Learning
Hive
DW System
Unstructured or
Semi-Structured data Structured Data
Hadoop Eco-System
Slide 19
21. Hadoop Core Components
Slide 21
Hadoop is a system for large scale data processing.
It has two main components:
HDFS – Hadoop Distributed File System (Storage)
Distributed across “nodes”
Natively redundant
NameNode tracks locations.
MapReduce (Processing)
Splits a task across processors
“near” the data & assembles results
Self-Healing, High Bandwidth
Clustered storage
JobTracker manages the TaskTrackers
22. HDFS Definition
Slide 22
The Hadoop Distributed File System (HDFS) is a distributed file system designed to run on commodity hardware.
HDFS is a distributed, scalable, and portable filesystem written in Java for the Hadoop framework.
It has many similarities with existing distributed file systems.
Hadoop Distributed File System (HDFS™) is the primary storage system used by Hadoop applications.
HDFS creates multiple replicas of data blocks and distributes them on compute nodes throughout a cluster to
enable reliable, extremely rapid computations.
HDFS is highly fault-tolerant and is designed to be deployed on low-cost hardware.
HDFS provides high throughput access to application data and is suitable for applications that have large data sets
• HDFS consists of following components (daemons)
– Name Node
– Data Node
– Secondary Name Node
23. HDFS Components
Slide 23
Namenode:
NameNode, a master server, manages the file system namespace and regulates
access to files by clients.
Maintains and Manages the blocks which are present on the datanode.
Meta-data in Memory
– The entire metadata is in main memory
Types of Metadata
– List of files
– List of Blocks for each file
– List of DataNodes for each block
– File attributes, e.g creation time, replication factor
A Transaction Log
– Records file creations, file deletions. Etc
24. HDFS Components
Slide 24
Data Node: Slaves which are deployed on each mechine and provide
the actual storage.
DataNodes, one per node in the cluster, manages storage attached to the
nodes that they run on
A Block Server
– Stores data in the local file system (e.g. ext3)
– Stores meta-data of a block (e.g. CRC)
– Serves data and meta-data to Clients
– Block Report
– Periodically sends a report of all existing blocks to the NameNode
Facilitates Pipelining of Data
– Forwards data to other specified DataNodes
25. Understanding the File system
Block placement
• Current Strategy
One replica on local node
Second replica on a remote rack
Third replica on same remote rack
Additional replicas are randomly placed
• Clients read from nearest replica
Data Correctness
• Use Checksums to validate data
Use CRC32
• File Creation
Client computes checksum per 512 byte
DataNode stores the checksum
• File access
Client retrieves the data and checksum from DataNode
26. Understanding the File system
Data pipelining
Client retrieves a list of DataNodes on which to place replicas of a block
Client writes block to the first DataNode
The first DataNode forwards the data to the next DataNode in the Pipeline
When all replicas are written, the Client moves on to write the next block in file
Rebalancer
• Goal: % of disk occupied on Datanodes should be similar
Usually run when new Datanodes are added
Cluster is online when Rebalancer is active
Rebalancer is throttled to avoid network congestion
Command line tool
27. Secondary NameNode
Slide 27
Secondary NameNode:
Not a hot standby for the NameNode
Connects to NameNode every hour*
Housekeeping, backup of NemeNode metadata
Saved metadata can build a failed NameNode
32. Task TrackerTask Tracker
Task TrackerTask TrackerData Node
Task Tracker
Client
HDFS
Name Node
Task Tracker Task Tracker Data Node Data Node
Map Reduce
Job Tracker
Hadoop Cluster Architecture (Contd.)
Data Node
Data Node
Task Tracker
Data Node
Data Node Data Node
33. Data Node
RAM: 16GB
Hard disk: 6 X 2TB
Processor: Xenon with 2 cores.
Ethernet: 3 X 10 GB/s
OS: 64-bit CentOS
Name Node
RAM: 64 GB,
Hard disk: 1 TB
Processor: Xenon with 8 Cores
Ethernet: 3 X 10 GB/s
OS: 64-bit CentOS
Power: Redundant Power Supply
Secondary Name Node
RAM: 32 GB,
Hard disk: 1 TB
Processor: Xenon with 4 Cores
Ethernet: 3 X 10 GB/s
OS: 64-bit CentOS
Power: Redundant Power Supply
Data Node
RAM: 16GB
Hard disk: 6 X 2TB
Processor: Xenon with 2 cores.
Ethernet: 3 X 10 GB/s
OS: 64-bit CentOS
Hadoop Cluster: A Typical Use Case
35. Hadoop can run in any of the following three modes:
Standalone (or Local) Mode
No daemons, everything runs in a single JVM.
Suitable for running MapReduce programs during development.
Has no DFS.
Pseudo-Distributed Mode
Hadoop daemons run on the local machine.
Fully-Distributed Mode
Hadoop daemons run on a cluster of machines.
Hadoop Cluster Modes
37. Hadoop Configuration Files
Configuration
Filenames
Description of Log Files
hadoop-env.sh Environment variables that are used in the scripts to run Hadoop.
core-site.xml Configuration settings for Hadoop Core such as I/O settings that are common to HDFS and
MapReduce.
hdfs-site.xml Configuration settings for HDFS daemons, the namenode, the secondary namenode and the data
nodes.
mapred-site.xml Configuration settings for MapReduce daemons : the job-tracker and the task-trackers.
masters A list of machines (one per line) that each run a secondary namenode.
slaves A list of machines (one per line) that each run a datanode and a task-tracker.
38. core-site.xml and hdfs-site.xml
hdfs-site.xml core-site.xml
<?xml version - "1.0"?> <?xml version ="1.0"?>
<!--hdfs-site.xml--> <!--core-site.xml-->
<configuration> <configuration>
<property> <property>
<name>dfs.replication</name> <name>fs.default.name</name>
<value>1</value> <value>hdfs://localhost:8020/</value>
</property> </property>
</configuration> </configuration>
39. Defining HDFS Details In hdfs-site.xml
Property Value Description
dfs.data.dir <value>
/disk1/hdfs/data,
/disk2/hdfs/data
</value>
A list of directories where the datanode
stores blocks. Each block is stored in only
one of these directories.
${hadoop.tmp.dir}/dfs/data
fs.checkpoint.dir <value>
/disk1/hdfs/namesecondary,
/disk2/hdfs/namesecondary
</value>
A list of directories where the secondary
namenode stores checkpoints. It stores a
copy of the checkpoint in each directory in
the list
${hadoop.tmp.dir}/dfs/namesecondary
41. Defining mapred-site.xml
Property Value Description
mapred.job.tracker <value>
localhost:8021
</value>
The hostname and the port that the jobtracker
RPC server runs on. If set to the default value of
local, then the jobtracker runs in-process on
demand when you run a MapReduce job.
mapred.local.dir ${hadoop.tmp.dir}/mapred/local A list of directories where MapReduce stores
intermediate data for jobs. The data is cleared
out when the job ends.
mapred.system.dir ${hadoop.tmp.dir}/mapred/syste
m
The directory relative to fs.default.name where
shared files are stored, during a job run.
mapred.tasktracker.map.tasks.
maximum
2 The number of map tasks that may be run on a
tasktracker at any one time
mapred.tasktracker.reduce.tasks
.maximum
2 The number of reduce tasks tat may be run on a
tasktracker at any one time.
43. Masters
Contains a list of hosts, one per line, that are to host Secondary
NameNode servers.
Slaves and Masters
Two files are used by the startup and shutdown commands:
Slaves
Contains a list of hosts, one per line, that are to host DataNode and
TaskTracker servers.
44. This file also offers a way to provide custom parameters for each of the servers.
Hadoop-env.sh is sourced by all of the Hadoop Core scripts provided in the conf/ directory of the
installation.
Examples of environment variables that you can specify:
export HADOOP_DATANODE_HEAPSIZE="128"
export HADOOP_TASKTRACKER_HEAPSIZE="512"
Set parameter JAVA_HOME
JVMhadoop-env.sh
Per-Process Runtime Environment
47. Open a terminal window to the current working directory.
# /home/notroot
-----------------------------------------------------------------
# 1. Print the Hadoop version
hadoop version
-----------------------------------------------------------------
# 2. List the contents of the root directory in HDFS
hadoop fs -ls /
-----------------------------------------------------------------
# 3. Report the amount of space used and available on currently mounted filesystem
hadoop fs -df hdfs:/
-----------------------------------------------------------------
# 4. Count the number of directories,files and bytes under the paths that match the
specified file pattern
hadoop fs -count hdfs:/
48. 5. Run a DFS filesystem checking utility
hadoop fsck – /
-----------------------------------------------------------------
# 6. Run a cluster balancing utility
hadoop balancer
-----------------------------------------------------------------
# 7. Create a new directory named “hadoop” below the /user/training directory in
HDFS.
hadoop fs -mkdir /user/training/hadoop
-----------------------------------------------------------------
# 8. Add a sample text file from the local directory named “data” to the new
directory you created in HDFS
hadoop fs -put data/sample.txt /user/training/hadoop
49. 9. List the contents of this new directory in HDFS.
hadoop fs -ls /user/training/hadoop
-----------------------------------------------------------------
# 10. Add the entire local directory called “retail” to the /user/training directory in
HDFS.
hadoop fs -put data/retail /user/training/hadoop
-----------------------------------------------------------------
# 11. Since /user/training is your home directory in HDFS, any command that does not
have an absolute path is interpreted as relative to that directory.
# The next command will therefore list your home directory, and should show the
items you’ve just added there.
hadoop fs -ls
-----------------------------------------------------------------
# 12. See how much space this directory occupies in HDFS.
hadoop fs -du -s -h hadoop/retail
50. 13. Delete a file ‘customers’ from the “retail” directory.
hadoop fs -rm hadoop/retail/customers
-----------------------------------------------------------------
# 14. Ensure this file is no longer in HDFS.
hadoop fs -ls hadoop/retail/customers
-----------------------------------------------------------------
# 15. Delete all files from the “retail” directory using a wildcard.
hadoop fs -rm hadoop/retail/*
-----------------------------------------------------------------
# 16. To empty the trash
hadoop fs –expunge
-----------------------------------------------------------------
# 17. Finally, remove the entire retail directory and all of its contents in HDFS.
hadoop fs -rm -r hadoop/retail
51. # 18. Add the purchases.txt file from the local directory named “/home/training/” to
the hadoop directory you created in HDFS
hadoop fs -copyFromLocal /home/training/purchases.txt hadoop/
-----------------------------------------------------------------
# 19. To view the contents of your text file purchases.txt which is present in your
hadoop directory.
hadoop fs -cat hadoop/purchases.txt
-----------------------------------------------------------------
# 20. Move a directory from one location to other
hadoop fs -mv hadoop apache_hadoop
-----------------------------------------------------------------
# 21. Add the purchases.txt file from “hadoop” directory which is present in HDFS
directory to the directory “data” which is present in your local directory
hadoop fs -copyToLocal hadoop/purchases.txt /home/training/data
52. # 26. Default names of owner and group are training,training
# Use ‘-chown’ to change owner name and group name simultaneously
hadoop fs -ls hadoop/purchases.txt
sudo -u hdfs hadoop fs -chown root:root hadoop/purchases.txt
-----------------------------------------------------------------
# 27. Default name of group is training
# Use ‘-chgrp’ command to change group name
hadoop fs -ls hadoop/purchases.txt
sudo -u hdfs hadoop fs -chgrp training hadoop/purchases.txt
-----------------------------------------------------------------
# 28. Default replication factor to a file is 3.
# Use ‘-setrep’ command to change replication factor of a file
hadoop fs -setrep -w 2 apache_hadoop/sample.txt
53. 29. Copy a directory from one node in the cluster to another
# Use ‘-distcp’ command to copy,
# Use '-overwrite' option to overwrite in an existing files
# Use '-update' command to synchronize both directories
hadoop fs -distcp hdfs://namenodeA/apache_hadoop hdfs://namenodeB/hadoop
-----------------------------------------------------------------
# 30. Command to make the name node leave safe mode
hadoop fs -expunge
sudo -u hdfs hdfs dfsadmin -safemode leave
-----------------------------------------------------------------
# 31. List all the hadoop file system shell commands
hadoop fs
-----------------------------------------------------------------
# 33. Last but not least, always ask for help!
hadoop fs -help