Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It uses HDFS for data storage and MapReduce as a programming model for distributed computing. HDFS stores data reliably across machines in a Hadoop cluster as blocks and achieves high fault tolerance through replication. MapReduce allows processing of large datasets in parallel by dividing the work into independent tasks called Maps and Reduces. Hadoop has seen widespread adoption for applications involving massive datasets and is used by companies like Yahoo!, Facebook and Amazon.
In KDD2011, Vijay Narayanan (Yahoo!) and Milind Bhandarkar (Greenplum Labs, EMC) conducted a tutorial on "Modeling with Hadoop". This is the first half of the tutorial.
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 Hadoop Hive Tutorial will unravel the complete Introduction to Hive, Hive Architecture, Hive Commands, Hive Fundamentals & HiveQL. In addition to this, even fundamental concepts of BIG Data & Hadoop are extensively covered.
At the end, you'll have a strong knowledge regarding Hadoop Hive Basics.
PPT Agenda
✓ Introduction to BIG Data & Hadoop
✓ What is Hive?
✓ Hive Data Flows
✓ Hive Programming
----------
What is Apache Hive?
Apache Hive is a data warehousing infrastructure built over Hadoop which is targeted towards SQL programmers. Hive permits SQL programmers to directly enter the Hadoop ecosystem without any pre-requisites in Java or other programming languages. HiveQL is similar to SQL, it is utilized to process Hadoop & MapReduce operations by managing & querying data.
----------
Hive has the following 5 Components:
1. Driver
2. Compiler
3. Shell
4. Metastore
5. Execution Engine
----------
Applications of Hive
1. Data Mining
2. Document Indexing
3. Business Intelligence
4. Predictive Modelling
5. Hypothesis Testing
----------
Skillspeed is a live e-learning company focusing on high-technology courses. We provide live instructor led training in BIG Data & Hadoop featuring Realtime Projects, 24/7 Lifetime Support & 100% Placement Assistance.
Email: sales@skillspeed.com
Website: https://www.skillspeed.com
Technological Geeks Video 13 :-
Video Link :- https://youtu.be/mfLxxD4vjV0
FB page Link :- https://www.facebook.com/bitwsandeep/
Contents :-
Hive Architecture
Hive Components
Limitations of Hive
Hive data model
Difference with traditional RDBMS
Type system in Hive
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
Introduction to Microsoft’s Hadoop solution (HDInsight)James Serra
Did you know Microsoft provides a Hadoop Platform-as-a-Service (PaaS)? It’s called Azure HDInsight and it deploys and provisions managed Apache Hadoop clusters in the cloud, providing a software framework designed to process, analyze, and report on big data with high reliability and availability. HDInsight uses the Hortonworks Data Platform (HDP) Hadoop distribution that includes many Hadoop components such as HBase, Spark, Storm, Pig, Hive, and Mahout. Join me in this presentation as I talk about what Hadoop is, why deploy to the cloud, and Microsoft’s solution.
Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It uses HDFS for data storage and MapReduce as a programming model for distributed computing. HDFS stores data reliably across machines in a Hadoop cluster as blocks and achieves high fault tolerance through replication. MapReduce allows processing of large datasets in parallel by dividing the work into independent tasks called Maps and Reduces. Hadoop has seen widespread adoption for applications involving massive datasets and is used by companies like Yahoo!, Facebook and Amazon.
In KDD2011, Vijay Narayanan (Yahoo!) and Milind Bhandarkar (Greenplum Labs, EMC) conducted a tutorial on "Modeling with Hadoop". This is the first half of the tutorial.
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 Hadoop Hive Tutorial will unravel the complete Introduction to Hive, Hive Architecture, Hive Commands, Hive Fundamentals & HiveQL. In addition to this, even fundamental concepts of BIG Data & Hadoop are extensively covered.
At the end, you'll have a strong knowledge regarding Hadoop Hive Basics.
PPT Agenda
✓ Introduction to BIG Data & Hadoop
✓ What is Hive?
✓ Hive Data Flows
✓ Hive Programming
----------
What is Apache Hive?
Apache Hive is a data warehousing infrastructure built over Hadoop which is targeted towards SQL programmers. Hive permits SQL programmers to directly enter the Hadoop ecosystem without any pre-requisites in Java or other programming languages. HiveQL is similar to SQL, it is utilized to process Hadoop & MapReduce operations by managing & querying data.
----------
Hive has the following 5 Components:
1. Driver
2. Compiler
3. Shell
4. Metastore
5. Execution Engine
----------
Applications of Hive
1. Data Mining
2. Document Indexing
3. Business Intelligence
4. Predictive Modelling
5. Hypothesis Testing
----------
Skillspeed is a live e-learning company focusing on high-technology courses. We provide live instructor led training in BIG Data & Hadoop featuring Realtime Projects, 24/7 Lifetime Support & 100% Placement Assistance.
Email: sales@skillspeed.com
Website: https://www.skillspeed.com
Technological Geeks Video 13 :-
Video Link :- https://youtu.be/mfLxxD4vjV0
FB page Link :- https://www.facebook.com/bitwsandeep/
Contents :-
Hive Architecture
Hive Components
Limitations of Hive
Hive data model
Difference with traditional RDBMS
Type system in Hive
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
Introduction to Microsoft’s Hadoop solution (HDInsight)James Serra
Did you know Microsoft provides a Hadoop Platform-as-a-Service (PaaS)? It’s called Azure HDInsight and it deploys and provisions managed Apache Hadoop clusters in the cloud, providing a software framework designed to process, analyze, and report on big data with high reliability and availability. HDInsight uses the Hortonworks Data Platform (HDP) Hadoop distribution that includes many Hadoop components such as HBase, Spark, Storm, Pig, Hive, and Mahout. Join me in this presentation as I talk about what Hadoop is, why deploy to the cloud, and Microsoft’s solution.
This document provides an introduction to big data, including its key characteristics of volume, velocity, and variety. It describes different types of big data technologies like Hadoop, MapReduce, HDFS, Hive, and Pig. Hadoop is an open source software framework for distributed storage and processing of large datasets across clusters of computers. MapReduce is a programming model used for processing large datasets in a distributed computing environment. HDFS provides a distributed file system for storing large datasets across clusters. Hive and Pig provide data querying and analysis capabilities for data stored in Hadoop clusters using SQL-like and scripting languages respectively.
Big Data Tutorial For Beginners | What Is Big Data | Big Data Tutorial | Hado...Edureka!
This Edureka Big Data tutorial helps you to understand Big Data in detail. This tutorial will be discussing about evolution of Big Data, factors associated with Big Data, different opportunities in Big Data. Further it will discuss about problems associated with Big Data and how Hadoop emerged as a solution. Below are the topics covered in this tutorial:
1) Evolution of Data
2) What is Big Data?
3) Big Data as an Opportunity
4) Problems in Encasing Big Data Opportunity
5) Hadoop as a Solution
6) Hadoop Ecosystem
7) Edureka Big Data & Hadoop Training
Apache Hive is a data warehouse software built on top of Hadoop that allows users to query data stored in various databases and file systems using an SQL-like interface. It provides a way to summarize, query, and analyze large datasets stored in Hadoop distributed file system (HDFS). Hive gives SQL capabilities to analyze data without needing MapReduce programming. Users can build a data warehouse by creating Hive tables, loading data files into HDFS, and then querying and analyzing the data using HiveQL, which Hive then converts into MapReduce jobs.
This document provides an overview of Hadoop and its core components. Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It uses MapReduce as its programming model and the Hadoop Distributed File System (HDFS) for storage. HDFS stores data redundantly across nodes for reliability. The core subprojects of Hadoop include MapReduce, HDFS, Hive, HBase, and others.
Apache Hive is a data warehouse software that allows querying and managing large datasets stored in Hadoop's HDFS. It provides tools for easy extract, transform, and load of data. Hive supports a SQL-like language called HiveQL and big data analytics using MapReduce. Data in Hive is organized into databases, tables, partitions, and buckets. Hive supports various data types, operators, and functions for data analysis. Some advantages of Hive include its ability to handle large datasets using Hadoop's reliability and performance. However, Hive does not support all SQL features and transactions.
The document summarizes a technical seminar on Hadoop. It discusses Hadoop's history and origin, how it was developed from Google's distributed systems, and how it provides an open-source framework for distributed storage and processing of large datasets. It also summarizes key aspects of Hadoop including HDFS, MapReduce, HBase, Pig, Hive and YARN, and how they address challenges of big data analytics. The seminar provides an overview of Hadoop's architecture and ecosystem and how it can effectively process large datasets measured in petabytes.
The document discusses Hadoop, an open-source software framework that allows distributed processing of large datasets across clusters of computers. It describes Hadoop as having two main components - the Hadoop Distributed File System (HDFS) which stores data across infrastructure, and MapReduce which processes the data in a parallel, distributed manner. HDFS provides redundancy, scalability, and fault tolerance. Together these components provide a solution for businesses to efficiently analyze the large, unstructured "Big Data" they collect.
This document discusses NoSQL and the CAP theorem. It begins with an introduction of the presenter and an overview of topics to be covered: What is NoSQL and the CAP theorem. It then defines NoSQL, provides examples of major NoSQL categories (document, graph, key-value, and wide-column stores), and explains why NoSQL is used, including to handle large, dynamic, and distributed data. The document also explains the CAP theorem, which states that a distributed data store can only satisfy two of three properties: consistency, availability, and partition tolerance. It provides examples of how to choose availability over consistency or vice versa. Finally, it concludes that both SQL and NoSQL have valid use cases and a combination
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/
The document provides an overview of big data analytics using Hadoop. It discusses how Hadoop allows for distributed processing of large datasets across computer clusters. The key components of Hadoop discussed are HDFS for storage, and MapReduce for parallel processing. HDFS provides a distributed, fault-tolerant file system where data is replicated across multiple nodes. MapReduce allows users to write parallel jobs that process large amounts of data in parallel on a Hadoop cluster. Examples of how companies use Hadoop for applications like customer analytics and log file analysis are also provided.
What Is Hadoop? | What Is Big Data & Hadoop | Introduction To Hadoop | Hadoop...Simplilearn
This presentation about Hadoop will help you understand what is Big Data, what is Hadoop, how Hadoop came into existence, what are the various components of Hadoop and an explanation on Hadoop use case. In the current time, there is a lot of data being generated every day and this massive amount of data cannot be stored, processed and analyzed using the traditional ways. That is why Hadoop can into existence as a solution for Big Data. Hadoop is a framework that manages Big Data storage in a distributed way and processes it parallelly. Now, let us get started and understand the importance of Hadoop and why we actually need it.
Below topics are explained in this Hadoop presentation:
1. The rise of Big Data
2. What is Big Data?
3. Big Data and its challenges
4. Hadoop as a solution
5. What is Hadoop?
6. Components of Hadoop
7. Use case of Hadoop
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
Hadoop MapReduce is an open source framework for distributed processing of large datasets across clusters of computers. It allows parallel processing of large datasets by dividing the work across nodes. The framework handles scheduling, fault tolerance, and distribution of work. MapReduce consists of two main phases - the map phase where the data is processed key-value pairs and the reduce phase where the outputs of the map phase are aggregated together. It provides an easy programming model for developers to write distributed applications for large scale processing of structured and unstructured data.
Facing trouble in distinguishing Big Data, Hadoop & NoSQL as well as finding connection among them? This slide of Savvycom team can definitely help you.
Enjoy reading!
The document is a seminar report on the Hadoop framework. It provides an introduction to Hadoop and describes its key technologies including MapReduce, HDFS, and programming model. MapReduce allows distributed processing of large datasets across clusters. HDFS is the distributed file system used by Hadoop to reliably store large amounts of data across commodity hardware.
There are patterns for things such as domain-driven design, enterprise architectures, continuous delivery, microservices, and many others.
But where are the data science and data engineering patterns?
Sometimes, data engineering reminds me of cowboy coding - many workarounds, immature technologies and lack of market best practices.
WebHDFS x HttpFS are common source of confusion. This slideset highlights differences and similarities between these two Web interfaces for accessing an HDFS cluster.
Worldranking universities final documentationBhadra Gowdra
With the upcoming data deluge of semantic data, the fast growth of ontology bases has brought significant challenges in performing efficient and scalable reasoning. Traditional centralized reasoning methods are not sufficient to process large ontologies. Distributed searching methods are thus required to improve the scalability and performance of inferences. This paper proposes an incremental and distributed inference method for large-scale ontologies by using Map reduce, which realizes high-performance reasoning and runtime searching, especially for incremental knowledge base. By constructing transfer inference forest and effective assertion triples, the storage is largely reduced and the search process is simplified and accelerated. We propose an incremental and distributed inference method (IDIM) for large-scale RDF datasets via Map reduce. The choice of Map reduce is motivated by the fact that it can limit data exchange and alleviate load balancing problems by dynamically scheduling jobs on computing nodes. In order to store the incremental RDF triples more efficiently, we present two novel concepts, i.e., transfer inference forest (TIF) and effective assertion triples (EAT). Their use can largely reduce the storage and simplify the reasoning process. Based on TIF/EAT, we need not compute and store RDF closure, and the reasoning time so significantly decreases that a user’s online query can be answered timely, which is more efficient than existing methods to our best knowledge. More importantly, the update of TIF/EAT needs only minimum computation since the relationship between new triples and existing ones is fully used, which is not found in the existing literature. In order to store the incremental RDF triples more efficiently, we present two novel concepts, transfer inference forest and effective assertion triples. Their use can largely reduce the storage and simplify the searching process.
This Hadoop HDFS Tutorial will unravel the complete Hadoop Distributed File System including HDFS Internals, HDFS Architecture, HDFS Commands & HDFS Components - Name Node & Secondary Node. Not only this, even Mapreduce & practical examples of HDFS Applications are showcased in the presentation. At the end, you'll have a strong knowledge regarding Hadoop HDFS Basics.
Session Agenda:
✓ Introduction to BIG Data & Hadoop
✓ HDFS Internals - Name Node & Secondary Node
✓ MapReduce Architecture & Components
✓ MapReduce Dataflows
----------
What is HDFS? - Introduction to HDFS
The Hadoop Distributed File System provides high-performance access to data across Hadoop clusters. It forms the crux of the entire Hadoop framework.
----------
What are HDFS Internals?
HDFS Internals are:
1. Name Node – This is the master node from where all data is accessed across various directores. When a data file has to be pulled out & manipulated, it is accessed via the name node.
2. Secondary Node – This is the slave node where all data is stored.
----------
What is MapReduce? - Introduction to MapReduce
MapReduce is a programming framework for distributed processing of large data-sets via commodity computing clusters. It is based on the principal of parallel data processing, wherein data is broken into smaller blocks rather than processed as a single block. This ensures a faster, secure & scalable solution. Mapreduce commands are based in Java.
----------
What are HDFS Applications?
1. Data Mining
2. Document Indexing
3. Business Intelligence
4. Predictive Modelling
5. Hypothesis Testing
----------
Skillspeed is a live e-learning company focusing on high-technology courses. We provide live instructor led training in BIG Data & Hadoop featuring Realtime Projects, 24/7 Lifetime Support & 100% Placement Assistance.
Email: sales@skillspeed.com
Website: https://www.skillspeed.com
This document provides an introduction to big data, including its key characteristics of volume, velocity, and variety. It describes different types of big data technologies like Hadoop, MapReduce, HDFS, Hive, and Pig. Hadoop is an open source software framework for distributed storage and processing of large datasets across clusters of computers. MapReduce is a programming model used for processing large datasets in a distributed computing environment. HDFS provides a distributed file system for storing large datasets across clusters. Hive and Pig provide data querying and analysis capabilities for data stored in Hadoop clusters using SQL-like and scripting languages respectively.
Big Data Tutorial For Beginners | What Is Big Data | Big Data Tutorial | Hado...Edureka!
This Edureka Big Data tutorial helps you to understand Big Data in detail. This tutorial will be discussing about evolution of Big Data, factors associated with Big Data, different opportunities in Big Data. Further it will discuss about problems associated with Big Data and how Hadoop emerged as a solution. Below are the topics covered in this tutorial:
1) Evolution of Data
2) What is Big Data?
3) Big Data as an Opportunity
4) Problems in Encasing Big Data Opportunity
5) Hadoop as a Solution
6) Hadoop Ecosystem
7) Edureka Big Data & Hadoop Training
Apache Hive is a data warehouse software built on top of Hadoop that allows users to query data stored in various databases and file systems using an SQL-like interface. It provides a way to summarize, query, and analyze large datasets stored in Hadoop distributed file system (HDFS). Hive gives SQL capabilities to analyze data without needing MapReduce programming. Users can build a data warehouse by creating Hive tables, loading data files into HDFS, and then querying and analyzing the data using HiveQL, which Hive then converts into MapReduce jobs.
This document provides an overview of Hadoop and its core components. Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It uses MapReduce as its programming model and the Hadoop Distributed File System (HDFS) for storage. HDFS stores data redundantly across nodes for reliability. The core subprojects of Hadoop include MapReduce, HDFS, Hive, HBase, and others.
Apache Hive is a data warehouse software that allows querying and managing large datasets stored in Hadoop's HDFS. It provides tools for easy extract, transform, and load of data. Hive supports a SQL-like language called HiveQL and big data analytics using MapReduce. Data in Hive is organized into databases, tables, partitions, and buckets. Hive supports various data types, operators, and functions for data analysis. Some advantages of Hive include its ability to handle large datasets using Hadoop's reliability and performance. However, Hive does not support all SQL features and transactions.
The document summarizes a technical seminar on Hadoop. It discusses Hadoop's history and origin, how it was developed from Google's distributed systems, and how it provides an open-source framework for distributed storage and processing of large datasets. It also summarizes key aspects of Hadoop including HDFS, MapReduce, HBase, Pig, Hive and YARN, and how they address challenges of big data analytics. The seminar provides an overview of Hadoop's architecture and ecosystem and how it can effectively process large datasets measured in petabytes.
The document discusses Hadoop, an open-source software framework that allows distributed processing of large datasets across clusters of computers. It describes Hadoop as having two main components - the Hadoop Distributed File System (HDFS) which stores data across infrastructure, and MapReduce which processes the data in a parallel, distributed manner. HDFS provides redundancy, scalability, and fault tolerance. Together these components provide a solution for businesses to efficiently analyze the large, unstructured "Big Data" they collect.
This document discusses NoSQL and the CAP theorem. It begins with an introduction of the presenter and an overview of topics to be covered: What is NoSQL and the CAP theorem. It then defines NoSQL, provides examples of major NoSQL categories (document, graph, key-value, and wide-column stores), and explains why NoSQL is used, including to handle large, dynamic, and distributed data. The document also explains the CAP theorem, which states that a distributed data store can only satisfy two of three properties: consistency, availability, and partition tolerance. It provides examples of how to choose availability over consistency or vice versa. Finally, it concludes that both SQL and NoSQL have valid use cases and a combination
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/
The document provides an overview of big data analytics using Hadoop. It discusses how Hadoop allows for distributed processing of large datasets across computer clusters. The key components of Hadoop discussed are HDFS for storage, and MapReduce for parallel processing. HDFS provides a distributed, fault-tolerant file system where data is replicated across multiple nodes. MapReduce allows users to write parallel jobs that process large amounts of data in parallel on a Hadoop cluster. Examples of how companies use Hadoop for applications like customer analytics and log file analysis are also provided.
What Is Hadoop? | What Is Big Data & Hadoop | Introduction To Hadoop | Hadoop...Simplilearn
This presentation about Hadoop will help you understand what is Big Data, what is Hadoop, how Hadoop came into existence, what are the various components of Hadoop and an explanation on Hadoop use case. In the current time, there is a lot of data being generated every day and this massive amount of data cannot be stored, processed and analyzed using the traditional ways. That is why Hadoop can into existence as a solution for Big Data. Hadoop is a framework that manages Big Data storage in a distributed way and processes it parallelly. Now, let us get started and understand the importance of Hadoop and why we actually need it.
Below topics are explained in this Hadoop presentation:
1. The rise of Big Data
2. What is Big Data?
3. Big Data and its challenges
4. Hadoop as a solution
5. What is Hadoop?
6. Components of Hadoop
7. Use case of Hadoop
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
Hadoop MapReduce is an open source framework for distributed processing of large datasets across clusters of computers. It allows parallel processing of large datasets by dividing the work across nodes. The framework handles scheduling, fault tolerance, and distribution of work. MapReduce consists of two main phases - the map phase where the data is processed key-value pairs and the reduce phase where the outputs of the map phase are aggregated together. It provides an easy programming model for developers to write distributed applications for large scale processing of structured and unstructured data.
Facing trouble in distinguishing Big Data, Hadoop & NoSQL as well as finding connection among them? This slide of Savvycom team can definitely help you.
Enjoy reading!
The document is a seminar report on the Hadoop framework. It provides an introduction to Hadoop and describes its key technologies including MapReduce, HDFS, and programming model. MapReduce allows distributed processing of large datasets across clusters. HDFS is the distributed file system used by Hadoop to reliably store large amounts of data across commodity hardware.
There are patterns for things such as domain-driven design, enterprise architectures, continuous delivery, microservices, and many others.
But where are the data science and data engineering patterns?
Sometimes, data engineering reminds me of cowboy coding - many workarounds, immature technologies and lack of market best practices.
WebHDFS x HttpFS are common source of confusion. This slideset highlights differences and similarities between these two Web interfaces for accessing an HDFS cluster.
Worldranking universities final documentationBhadra Gowdra
With the upcoming data deluge of semantic data, the fast growth of ontology bases has brought significant challenges in performing efficient and scalable reasoning. Traditional centralized reasoning methods are not sufficient to process large ontologies. Distributed searching methods are thus required to improve the scalability and performance of inferences. This paper proposes an incremental and distributed inference method for large-scale ontologies by using Map reduce, which realizes high-performance reasoning and runtime searching, especially for incremental knowledge base. By constructing transfer inference forest and effective assertion triples, the storage is largely reduced and the search process is simplified and accelerated. We propose an incremental and distributed inference method (IDIM) for large-scale RDF datasets via Map reduce. The choice of Map reduce is motivated by the fact that it can limit data exchange and alleviate load balancing problems by dynamically scheduling jobs on computing nodes. In order to store the incremental RDF triples more efficiently, we present two novel concepts, i.e., transfer inference forest (TIF) and effective assertion triples (EAT). Their use can largely reduce the storage and simplify the reasoning process. Based on TIF/EAT, we need not compute and store RDF closure, and the reasoning time so significantly decreases that a user’s online query can be answered timely, which is more efficient than existing methods to our best knowledge. More importantly, the update of TIF/EAT needs only minimum computation since the relationship between new triples and existing ones is fully used, which is not found in the existing literature. In order to store the incremental RDF triples more efficiently, we present two novel concepts, transfer inference forest and effective assertion triples. Their use can largely reduce the storage and simplify the searching process.
This Hadoop HDFS Tutorial will unravel the complete Hadoop Distributed File System including HDFS Internals, HDFS Architecture, HDFS Commands & HDFS Components - Name Node & Secondary Node. Not only this, even Mapreduce & practical examples of HDFS Applications are showcased in the presentation. At the end, you'll have a strong knowledge regarding Hadoop HDFS Basics.
Session Agenda:
✓ Introduction to BIG Data & Hadoop
✓ HDFS Internals - Name Node & Secondary Node
✓ MapReduce Architecture & Components
✓ MapReduce Dataflows
----------
What is HDFS? - Introduction to HDFS
The Hadoop Distributed File System provides high-performance access to data across Hadoop clusters. It forms the crux of the entire Hadoop framework.
----------
What are HDFS Internals?
HDFS Internals are:
1. Name Node – This is the master node from where all data is accessed across various directores. When a data file has to be pulled out & manipulated, it is accessed via the name node.
2. Secondary Node – This is the slave node where all data is stored.
----------
What is MapReduce? - Introduction to MapReduce
MapReduce is a programming framework for distributed processing of large data-sets via commodity computing clusters. It is based on the principal of parallel data processing, wherein data is broken into smaller blocks rather than processed as a single block. This ensures a faster, secure & scalable solution. Mapreduce commands are based in Java.
----------
What are HDFS Applications?
1. Data Mining
2. Document Indexing
3. Business Intelligence
4. Predictive Modelling
5. Hypothesis Testing
----------
Skillspeed is a live e-learning company focusing on high-technology courses. We provide live instructor led training in BIG Data & Hadoop featuring Realtime Projects, 24/7 Lifetime Support & 100% Placement Assistance.
Email: sales@skillspeed.com
Website: https://www.skillspeed.com
How To Become A Big Data Engineer? EdurekaEdureka!
** Big Data Masters Training Program: https://www.edureka.co/masters-program/big-data-architect-training **
This edureka PPT on "How to become a Big Data Engineer" is a complete career guide for aspiring Big Data Engineers. It includes the following topics:
Who is a Big Data Engineer?
What does a Big Data Engineer do?
Big Data Engineer Responsibilities
Big Data Engineer Skills
Big Data Engineering Learning Path
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Revanth Technologies provides a 35-hour online training course on Hadoop that covers the fundamentals and core components of Hadoop including HDFS, MapReduce, Pig, Hive, HBase, ZooKeeper and Sqoop. The course is divided into 16 sections that introduce concepts like building Hadoop clusters, configuring HDFS, developing MapReduce jobs, using Pig Latin, performing CRUD operations in HBase, and best practices for distributed Hadoop installations. Students will learn how to install and configure Hadoop components, develop applications using MapReduce and Pig, and integrate other tools into their Hadoop environment.
Learn Big data and Hadoop online at Easylearning Guru. We are offer Instructor led online training and Life Time LMS (Learning Management System). Join Our Free Live Demo Classes of Big Data Hadoop .
Cloudera Academic Partnership: Teaching Hadoop to the Next Generation of Data...Cloudera, Inc.
The document discusses Cloudera's Academic Partnership (CAP) program which aims to address the growing demand for professionals with skills in Apache Hadoop and big data by partnering with universities to provide training materials and resources for teaching Hadoop. The partnership provides universities with dedicated courseware, discounted instructor training, access to Cloudera's distribution of Hadoop and management software, and certification opportunities for students to help them gain skills relevant to the job market. The goal is to train the next generation of data professionals and help close the talent gap in big data.
解讀雲端大數據新趨勢
2018-05-16 @ iThome Cloud Summit 2018
雲端運算、大數據、物聯網、人工智慧,這些熱門話題從 2008 年開始就陸續出現在媒體版面上。放眼過去十年 Apache Hadoop 技術在臺灣本土的應用,本次分享將為各位解讀這四個話題之間的關聯,並探討 Big Data Stack on the Cloud 背後的市場需求驅動力,最後分享 Big Data Stack on Kubernetes 的進展。
PRACE Autumn school 2021 - Big Data with Hadoop and Keras
27-30 September 2021
Fakulteta za strojništvo
Europe/Ljubljana
Data and scripts are available at: https://www.events.prace-ri.eu/event/1226/timetable/
Big Data Engineer Skills and Job Description | EdurekaEdureka!
YouTube Link - https://youtu.be/B4bVJ_U6CmE
** Big Data Masters Training Program: https://www.edureka.co/masters-program/big-data-architect-training **
This Edureka PPT on "Big Data Engineer Skills" will tell you the required skill sets to become a Big Data Engineer. It includes the following topics:
Who is a Big Data Engineer?
Big Data Engineer Responsibilities
Big Data Engineer Skills
How to acquire those skills?
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Research IT @ Illinois: Establishing Service Responsive to Investigator NeedsJohn Towns
Over the past two years, an ongoing effort has been underway to further develop the research support IT resources and services necessary to make our faculty more competitive in the granting process. During this discussion, we will first review a yearlong effort in gathering the needs of researchers and distilling a set of recommendations to address those identified needs. This will be followed by a review of elements of a proposal prepared for campus administration articulating a vision and plan to create a dynamic research support environment in which a broad portfolio of resources, services and support are easily discoverable and accessible to the campus research community.
[Azureビッグデータ関連サービスとHortonworks勉強会] Azure HDInsightNaoki (Neo) SATO
This document discusses deploying Hadoop in the cloud using Microsoft's Azure HDInsight solution. It provides an overview of why organizations deploy Hadoop to the cloud, citing advantages like speed, scale, lower costs and easier maintenance. It then introduces Azure HDInsight, Microsoft's Hadoop distribution for the cloud, which supports various Hadoop projects like Hive, HBase, Mahout and Storm. It also discusses how Azure HDInsight allows organizations to run Hadoop across more global data centers than other vendors and ensures high availability, security and performance. Finally, it provides information on how readers can get started with Azure HDInsight.
Big data appliance ecosystem - in memory db, hadoop, analytics, data mining, business intelligence with multiple data source charts, twitter support and analysis.
TUW-ASE-Summer 2014: Data as a Service – Concepts, Design & Implementation, a...Hong-Linh Truong
This document discusses concepts related to data as a service (DaaS), including data service units, DaaS design and implementation, and DaaS ecosystems. It defines data service units and how they can provide data capabilities in clouds and on the internet. It outlines characteristics of DaaS based on NIST cloud definitions and describes common DaaS service models and deployment models. The document also discusses patterns for designing and implementing DaaS, considering both functional and non-functional aspects, and provides examples of service units and architectures in DaaS ecosystems.
Learn Big data and Hadoop online at Easylearning Guru. We are offer Instructor led online training and Life Time LMS (Learning Management System). Join Our Free Live Demo Classes of Big Data Hadoop .
Ayush Gaur has extensive experience and skills in big data analytics, cloud computing, and data science. He holds an M.S. in Computer Science with a concentration in data science from UT Dallas and a B.E. in Computer Science from Chitkara University in India. He has professional experience as an instructor for big data and analytics and as a senior associate focusing on big data, analytics, and cloud computing at Infosys. He has strong technical skills in Apache Spark, Hadoop, Python, and cloud platforms like AWS.
This document contains the resume of Ravulapati Hareesh, who has over 4 years of experience in Hadoop administration, Linux/Unix administration, and business intelligence and big data analytics solutions. It provides details on his skills and experience in setting up and administering Hadoop clusters using distributions like Cloudera, Hortonworks, and MapR. It also lists his experience in administering tools like Spark, Splunk, Tableau, HP Autonomy IDOL, and IBM products. His work experience includes setting up Hadoop clusters for various clients and working as a senior solutions engineer at Tech Mahindra.
The document discusses cloud computing, big data, and big data analytics. It defines cloud computing as an internet-based technology that provides on-demand access to computing resources and data storage. Big data is described as large and complex datasets that are difficult to process using traditional databases due to their size, variety, and speed of growth. Hadoop is presented as an open-source framework for distributed storage and processing of big data using MapReduce. The document outlines the importance of analyzing big data using descriptive, diagnostic, predictive, and prescriptive analytics to gain insights.
Performance evaluation of Map-reduce jar pig hive and spark with machine lear...IJECEIAES
Big data is the biggest challenges as we need huge processing power system and good algorithms to make a decision. We need Hadoop environment with pig hive, machine learning and hadoopecosystem components. The data comes from industries. Many devices around us and sensor, and from social media sites. According to McKinsey There will be a shortage of 15000000 big data professionals by the end of 2020. There are lots of technologies to solve the problem of big data Storage and processing. Such technologies are Apache Hadoop, Apache Spark, Apache Kafka, and many more. Here we analyse the processing speed for the 4GB data on cloudx lab with Hadoop mapreduce with varing mappers and reducers and with pig script and Hive querries and spark environment along with machine learning technology and from the results we can say that machine learning with Hadoop will enhance the processing performance along with with spark, and also we can say that spark is better than Hadoop mapreduce pig and hive, spark with hive and machine learning will be the best performance enhanced compared with pig and hive, Hadoop mapreduce jar.
This document provides an agenda and overview for a presentation on leveraging big data to create value. The agenda includes sessions on Hadoop in the real world, Cisco servers for big data, and breakout brainstorming sessions. The presentation discusses how big data can be a competitive strategy, its financial benefits, and goals for applying it in ways that improve important business metrics. An overview of key big data technologies is presented, including Hadoop, NoSQL databases, and in-memory databases. The big data software stack and how big data expands the traditional data stack is also summarized.
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
- - -
This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
1. Outline Introduction Big Data Sources of Big Data Tools HDFS Installation Configuration Starting & Stopping Map Reduc
Big Data & Hadoop
D. Praveen Kumar
Junior Research Fellow
Department of Computer Science & Engineering
Indian Institute of Technology (Indian School of Mines)
Dhanbad, Jharkhand, India
Head of IT & ITES, Skill Subsist Impels Ltd, Tirupati.
March 25, 2017
Sree Venkateswara College of Engineering, Nellore, A. P.
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1 Introduction
2 Big Data
3 Sources of Big Data
4 Tools
5 HDFS
6 Installation
7 Configuration
8 Starting & Stopping
9 Map Reduce
10 Execution
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Data
Data means a value or set of values.
Examples:
march 1st 2017
20, 30, 40
ΨΦϕ
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Information
Meaningful or preprocessed data we called as Information.
Examples:
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Data Types
The kind of data that may appear in a computer.
Examples: int
float
char
double
Abstract data types -user defined data types.
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Traditional approaches
Traditional approaches to store and process the data
1 File system
2 RDBMS (Relational Database Management Systems)
3 Data Warehouse & Mining Tools
4 Grid Computing
5 Volunteer Computing
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7. Outline Introduction Big Data Sources of Big Data Tools HDFS Installation Configuration Starting & Stopping Map Reduc
GUESTS =4
Transportation from railway station to your
home( one Auto/car is sufficient)
mom can prepare food or snacks without risk.
Your house is sufficient for Accommodation.
Facilities like bed, bathrooms, water and TV are
provided which you use.
You can talk to each other and crack jokes and
you can make them happy
Expenditure is nearly Rs.1000/-
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8. Outline Introduction Big Data Sources of Big Data Tools HDFS Installation Configuration Starting & Stopping Map Reduc
GUESTS =100
Transportation = 25 autos/car or two
buses
Food = catering.
Accommodation = Lodge.
Facilities = AC, TV, and all other facilities
Maintenance= somewhat difficult
Expenditure =nearly Rs. 90,000/-
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9. Outline Introduction Big Data Sources of Big Data Tools HDFS Installation Configuration Starting & Stopping Map Reduc
GUESTS =10000
Transportation = 2500 autos or 500 buses
Food = catering.
Accommodation = all Lodges, function
halls and cottages in the town.
Facilities = AC, TV, and all other
facilities are somewhat difficult to provide.
Maintenance= more difficult
Expenditure =nearly Rs. 2,00,000/-
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Grid Computing
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Volunteer Computing
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12. Outline Introduction Big Data Sources of Big Data Tools HDFS Installation Configuration Starting & Stopping Map Reduc
GUESTS =10000000
Transportation=how many autos=?
Food =?
Accommodation =?
Facilities =?
Maintenance=?
Cost =?
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13. Outline Introduction Big Data Sources of Big Data Tools HDFS Installation Configuration Starting & Stopping Map Reduc
Problems
Same we assume in computing environment
Difficult to handle a huge and ever growing amount of data
Processing of data can not be possible with few machines
distributing large data sets is difficult
Construction of online or offline models are very difficult
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Solution
A single solution to all these problems is
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15. Outline Introduction Big Data Sources of Big Data Tools HDFS Installation Configuration Starting & Stopping Map Reduc
What is Big Data?
Big data refers to voluminous amounts of structured or
unstructured data that organizations can potentially mine and
analyze.
Big data is huge amount of large data sets characterized by
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16. Outline Introduction Big Data Sources of Big Data Tools HDFS Installation Configuration Starting & Stopping Map Reduc
Data generation
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17. Outline Introduction Big Data Sources of Big Data Tools HDFS Installation Configuration Starting & Stopping Map Reduc
How Data generated
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18. Outline Introduction Big Data Sources of Big Data Tools HDFS Installation Configuration Starting & Stopping Map Reduc
Internet of Events
Internet is the main source to generating the wast amount of data.
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19. Outline Introduction Big Data Sources of Big Data Tools HDFS Installation Configuration Starting & Stopping Map Reduc
4 Internet of Events
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20. Outline Introduction Big Data Sources of Big Data Tools HDFS Installation Configuration Starting & Stopping Map Reduc
4 Questions of Data Analysts
1 What happened?
2 Why did it happen?
3 What will happen?
4 What is the best that can happen?
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Big Data Platforms and Analytical Software
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Hadoop
Here we go with
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Hadoop History
Hadoop was created by Doug Cutting, creator of Lucene.
He also involved in a project called Nutch. (It is basic version
of hadoop)
Nutch is a combination of MapReduce and NDFS (Nutch
Distributed File System)
Later Nutch renamed to Hadoop. (Mapreduce + HDFS
(Hadoop Distributed File System))
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24. Outline Introduction Big Data Sources of Big Data Tools HDFS Installation Configuration Starting & Stopping Map Reduc
Hadoop
Apache Hadoop is an open-source software framework for
distributed storage and distributed processing of very large data
sets on computer clusters built from commodity hardware.
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25. Outline Introduction Big Data Sources of Big Data Tools HDFS Installation Configuration Starting & Stopping Map Reduc
Hadoop
The base Apache Hadoop framework is composed of the following
modules:
Hadoop Common contains libraries and utilities needed by
other Hadoop modules
Hadoop Distributed File System (HDFS) a distributed
file-system that stores data
Hadoop YARN a resource-management platform
Hadoop MapReduce for large scale data processing.
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26. Outline Introduction Big Data Sources of Big Data Tools HDFS Installation Configuration Starting & Stopping Map Reduc
Hadoop Components
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27. Outline Introduction Big Data Sources of Big Data Tools HDFS Installation Configuration Starting & Stopping Map Reduc
Hadoop Components
Sree Venkateswara College of Engineering, Nellore, A. P.
Big Data & Hadoop
March 25, 2017 Slide: 27 / 60
28. Outline Introduction Big Data Sources of Big Data Tools HDFS Installation Configuration Starting & Stopping Map Reduc
HDFS- Goals
The design goals of HDFS
1 Very Large files
2 Streaming Data Access
3 Commodity Hardware
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29. Outline Introduction Big Data Sources of Big Data Tools HDFS Installation Configuration Starting & Stopping Map Reduc
HDFS- Failed in
HDFS is Not FIT for
1 Lots of small files
2 Low latency database access
3 Multiple writers, arbitrary file modifications
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Big Data & Hadoop
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30. Outline Introduction Big Data Sources of Big Data Tools HDFS Installation Configuration Starting & Stopping Map Reduc
HDFS- Concepts
1 Blocks
2 Namenodes
3 Datanodes
4 HDFS Federation
5 HDFS High Availability
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31. Outline Introduction Big Data Sources of Big Data Tools HDFS Installation Configuration Starting & Stopping Map Reduc
Requirements
Necessary
Java >= 7
ssh
Linux OS (Ubuntu >=
14.04)
Hadoop framework
Optional
Eclipse
Internet connection
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32. Outline Introduction Big Data Sources of Big Data Tools HDFS Installation Configuration Starting & Stopping Map Reduc
Java 7 & Installation
Hadoop requires a working Java installation. However, using
java 1.7 or more is recommended.
Following command is used to install java in linux platform
sudo apt-get install openjdk-7-jdk (or)
sudo apt-get install default-jdk
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33. Outline Introduction Big Data Sources of Big Data Tools HDFS Installation Configuration Starting & Stopping Map Reduc
Java PATH Setup
We need to set JAVA path
Open the .bashrc file located in home directory
gedit ~/.bashrc
Add below line at the end:
export JAVA HOME=/usr/lib/jvm/java−7−openjdk−amd64
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34. Outline Introduction Big Data Sources of Big Data Tools HDFS Installation Configuration Starting & Stopping Map Reduc
Installation & Configuration of SSH
Hadoop requires SSH(Secure Shell) access to manage its
nodes, i.e. remote machines plus your local machine if you
want to use Hadoop on it.
Install SSH using following command
sudo apt-get install ssh
First, we have to generate DSA an SSH key for user.
ssh-keygen -t dsa -P ’’ -f ~ /.ssh/id dsa
cat ~ /.ssh/id dsa.pub >> ~ /.ssh/authorized keys
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35. Outline Introduction Big Data Sources of Big Data Tools HDFS Installation Configuration Starting & Stopping Map Reduc
Download & Extract Hadoop
Download Hadoop from the Apache Download Mirrors
http://mirror.fibergrid.in/apache/hadoop/common/
Extract the contents of the Hadoop package to a location of your
choice. I picked /usr/local/hadoop.
$ cd /usr/local
$ sudo tar xzf hadoop-2.7.2.tar.gz
$ sudo mv hadoop-2.7.2 hadoop
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36. Outline Introduction Big Data Sources of Big Data Tools HDFS Installation Configuration Starting & Stopping Map Reduc
Add Hadoop configuration in .bashrc
Add Hadoop configuration in .bashrc in home directory.
export HADOOP INSTALL=/usr/local/hadoop
export PATH=$PATH:$HADOOP INSTALL/bin
export PATH=$PATH:$HADOOP INSTALL/sbin
export HADOOP MAPRED HOME=$HADOOP INSTALL
export HADOOP HDFS HOME=$HADOOP INSTALL
export HADOOP COMMON HOME=$HADOOP INSTALL
export YARN HOME=$HADOOP INSTALL
export HADOOP COMMON LIB NATIVE DIR=$HADOOP INSTALL/lib/native
export HADOOP OPTS="-Djava.library.path=$HADOOP INSTALL/lib"
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37. Outline Introduction Big Data Sources of Big Data Tools HDFS Installation Configuration Starting & Stopping Map Reduc
Create temp file, DataNode & NameNode
Execute below commands to create NameNode
mkdir -p /usr/local/hadoopdata/hdfs/namenode
Execute below commands to create DataNode
mkdir -p /usr/local/hadoopdata/hdfs/datanode
Execute below code to create the tmp directory in hadoop
sudo mkdir -p /app/hadoop/tmp
sudo chown hadoop1:hadoop1 /app/hadoop/tmp
sudo chmod 750 /app/hadoop/tmp
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38. Outline Introduction Big Data Sources of Big Data Tools HDFS Installation Configuration Starting & Stopping Map Reduc
Files to Configure
The following are the files we need to configure
core-site.xml
hadoop-env.sh
mapred-site.xml
hdfs-site.xml
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39. Outline Introduction Big Data Sources of Big Data Tools HDFS Installation Configuration Starting & Stopping Map Reduc
Add properties in /usr/local/hadoop/etc/core-site.xml
Add the following snippets between the
< configuration > ... < /configuration > tags in the core-site.xml
file.
Add below property to specify the location of tmp
< property >
< name > hadoop.tmp.dir < /name >
< value > /app/hadoop/tmp < /value >
< /property >
Add below property to specify the location of default file
system and its port number.
< property >
< name > fs.default.name < /name >
< value > hdfs : //localhost : 9000 < /value >
< /property >
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40. Outline Introduction Big Data Sources of Big Data Tools HDFS Installation Configuration Starting & Stopping Map Reduc
Add properties in /usr/local/hadoop/etc/hadoop-env.sh
Un-Comment the JAVA HOME and Give Correct Path For
Java.
export JAVA HOME=/usr/lib/jvm/java-7-openjdk-amd64
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41. Outline Introduction Big Data Sources of Big Data Tools HDFS Installation Configuration Starting & Stopping Map Reduc
Add property in
/usr/local/hadoop/etc/hadoop/mapred-site.xml
In file we add The host name and port that the MapReduce job
tracker runs at. Add following in mapred-site.xml :
< property >
< name > mapred.job.tracker < /name >
< value > localhost : 54311 < /value >
< /property >
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42. Outline Introduction Big Data Sources of Big Data Tools HDFS Installation Configuration Starting & Stopping Map Reduc
Add properties in ... etc/hadoop/hdfs-site.xml
In file hdfs-site.xml add following:
Add replication factor
< property >
< name > dfs.replication < /name >
< value > 1 < /value >
< /property >
Specify the NameNode
< property >
< name > dfs.namenode.name.dir < /name >
< value > file : /usr/local/hadoopdata/hdfs/namenode < /value >
< /property >
Specify the DataNode
< property >
< name > dfs.datanode.name.dir < /name >
< value > file : /usr/local/hadoopdata/hdfs/datanode < /value >
< /property >
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43. Outline Introduction Big Data Sources of Big Data Tools HDFS Installation Configuration Starting & Stopping Map Reduc
Formatting the HDFS filesystem via the NameNode
The first step to starting up your Hadoop installation is
Formatting the Hadoop file system
We need to do this the first time you set up a Hadoop.
Do not format a running Hadoop filesystem as you will lose all
the data currently in HDFS
To format the filesystem, run the command
hadoop namenode -format
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44. Outline Introduction Big Data Sources of Big Data Tools HDFS Installation Configuration Starting & Stopping Map Reduc
Starting single-node cluster
Run the command:
start-all.sh
This will startup a NameNode,SecondaryNameNode,
DataNode, ResourceManager and a NodeManager on your
machine.
A nifty tool for checking whether the expected Hadoop
processes are running is jps
hadoop1@hadoop1:/usr/local/hadoop$ jps
2598 NameNode
3112 ResourceManager
3523 Jps
2917 SecondaryNameNode
2727 DataNode
3242 NodeManager
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45. Outline Introduction Big Data Sources of Big Data Tools HDFS Installation Configuration Starting & Stopping Map Reduc
Stopping your single-node cluster
Run the command
stop-all.sh
To stop all the daemons running on your machine output will be
like this.
stopping NodeManager
localhost: stopping ResourceManager
stopping NameNode
localhost: stopping DataNode
localhost: stopping SecondaryNameNode
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46. Outline Introduction Big Data Sources of Big Data Tools HDFS Installation Configuration Starting & Stopping Map Reduc
Map-Reduce Framework
Map Reduce programming paradigm
It relies basically on two functions, Map and Reduce
Map Reduce used to manage many large-scale computations
The framework takes care of scheduling tasks, monitoring
them and re-executes the failed tasks.
The framework to effectively schedule tasks on the nodes
where data is already present
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47. Outline Introduction Big Data Sources of Big Data Tools HDFS Installation Configuration Starting & Stopping Map Reduc
Map-Reduce Computation Steps
The key-value pairs from each Map task are collected by a
master controller and sorted by key. The keys are divided
among all the Reduce tasks, so all key-value pairs with the
same key wind up at the same Reduce task.
The Reduce tasks work on one key at a time, and combine
all the values associated with that key in some way. The
manner of combination of values is determined by the code
written by the user for the Reduce function.
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48. Outline Introduction Big Data Sources of Big Data Tools HDFS Installation Configuration Starting & Stopping Map Reduc
Hadoop - MapReduce
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49. Outline Introduction Big Data Sources of Big Data Tools HDFS Installation Configuration Starting & Stopping Map Reduc
Hadoop - MapReduce (Word Count) Example
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50. Outline Introduction Big Data Sources of Big Data Tools HDFS Installation Configuration Starting & Stopping Map Reduc
MapReduce - WordCountMapper
In WordCountMapper class we perform the following operations
Read a line from file
Split line into Words
Assign Count 1 to each word
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51. Outline Introduction Big Data Sources of Big Data Tools HDFS Installation Configuration Starting & Stopping Map Reduc
WordCountMapper source code
public static class WordCountMapper
extends Mapper<Object, Text, Text, IntWritable>{
private final static IntWritable one = new IntWritable(1);
private Text word = new Text();
public void map(Object key, Text value, Context context ) throws
IOException, InterruptedException {
StringTokenizer itr = new StringTokenizer(value.toString());
while (itr.hasMoreTokens()) {
word.set(itr.nextToken());
context.write(word, one);
}
}
}
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52. Outline Introduction Big Data Sources of Big Data Tools HDFS Installation Configuration Starting & Stopping Map Reduc
MapReduce - WordCountReducer
In WordCountReducer class we perform the following operations
Sum the list of values
Assign sum to corresponding word
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53. Outline Introduction Big Data Sources of Big Data Tools HDFS Installation Configuration Starting & Stopping Map Reduc
WordCountReducer source code
public static class WordCountReducer
extends Reducer<Text,IntWritable,Text,IntWritable> {
private IntWritable result = new IntWritable();
public void reduce(Text key, Iterable<IntWritable> values,
Context context ) throws IOException, InterruptedException {
int sum = 0;
for (IntWritable val : values) {
sum += val.get();
}
result.set(sum);
context.write(key, result);
}
}
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54. Outline Introduction Big Data Sources of Big Data Tools HDFS Installation Configuration Starting & Stopping Map Reduc
WordCountJob
public class WordCountJob {
public static void main(String[] args) throws Exception {
Configuration conf = new Configuration();
Job job = new Job(conf, "word count");
job.setJarByClass(WordCountJob.class);
job.setMapperClass(WordCountMapper.class);
job.setCombinerClass(WordCountReducer.class);
job.setReducerClass(WordCountReducer.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
FileInputFormat.addInputPath(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}
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55. Outline Introduction Big Data Sources of Big Data Tools HDFS Installation Configuration Starting & Stopping Map Reduc
Header Files to include
import java.io.IOException;
import java.util.StringTokenizer;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.util.GenericOptionsParser;
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56. Outline Introduction Big Data Sources of Big Data Tools HDFS Installation Configuration Starting & Stopping Map Reduc
Execution of Hadoop Program in Eclipse
Step1:
1 Starting Hadoop in terminal using command:
$ Start-all.sh
2 Use JPS command to check all services of Hadoop are started
or not.
Step 2: open Eclipse
Step 3: Go to file ⇒ New ⇒ Project
Select Java Project and click on Next button
Write project name and click on Finish button
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57. Outline Introduction Big Data Sources of Big Data Tools HDFS Installation Configuration Starting & Stopping Map Reduc
Continue...
Step 4: Right side it creates a project
1 Right click on Project ⇒ New ⇒ Class
2 Write Name of Class and then Click Finish
3 Write MapReduce program in that class
Step 5: Write JAVA Program
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58. Outline Introduction Big Data Sources of Big Data Tools HDFS Installation Configuration Starting & Stopping Map Reduc
Continue...
Step 6: Importing JAR files
1 Right click on Project and select properties (Alt+Enter)
2 Select Java Build Path ⇒ Click on Libraries, then click on add
external JARS
3 Select the following jars from Hadoop library.
/usr/local/Hadoop/share/Hadoop/common/libs
/usr/local/Hadoop/share/Hadoop/hdfs/libs
/usr/local/Hadoop/share/Hadoop/httpfs/libs
/usr/local/Hadoop/share/Hadoop/mapreduce/libs
/usr/local/Hadoop/share/Hadoop/yarn/libs
/usr/local/Hadoop/share/Hadoop/tools/
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59. Outline Introduction Big Data Sources of Big Data Tools HDFS Installation Configuration Starting & Stopping Map Reduc
Continue ....
Step 7: Set input file path
1 Create folder in home dir
2 copy text files in to that
3 Select path of Input
Step 8: Set input and output path
1 right click on source ⇒ Run As ⇒ Run Configuration ⇒
Argument
2 Enter your input and out put path with a single space
3 click on Run
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60. Outline Introduction Big Data Sources of Big Data Tools HDFS Installation Configuration Starting & Stopping Map Reduc
thank You
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