The document discusses the challenges of managing large volumes of data from different sources. Traditional approaches of separating data into isolated data silos are no longer effective. The emerging solution is to bring all data together into a unified platform like Hadoop that can store, process, and analyze large amounts of diverse data in a distributed manner. This allows organizations to gain deeper insights by asking new questions of all their combined data.
2014 feb 24_big_datacongress_hadoopsession2_moderndataarchitectureAdam Muise
An introduction to Hadoop's core components as well as the core Hadoop use case: the Data Lake. This deck was delivered at Big Data Congress 2014 in Saint John, NB on Feb 24.
What is Hadoop brief intro for Georgian Partners CTO Conference. This outlines the origins of Open Source Apache Hadoop and how Hortonworks fits into this picture. There is also a brief introduction to YARN, the new resource negotiation layer.
2014 feb 24_big_datacongress_hadoopsession2_moderndataarchitectureAdam Muise
An introduction to Hadoop's core components as well as the core Hadoop use case: the Data Lake. This deck was delivered at Big Data Congress 2014 in Saint John, NB on Feb 24.
What is Hadoop brief intro for Georgian Partners CTO Conference. This outlines the origins of Open Source Apache Hadoop and how Hortonworks fits into this picture. There is also a brief introduction to YARN, the new resource negotiation layer.
Hadoop Tutorial | What is Hadoop | Hadoop Project on Reddit | EdurekaEdureka!
This Edureka Hadoop Tutorial ( Hadoop Tutorial Blog Series: https://goo.gl/zndT2V ) helps you understand Big Data and Hadoop in detail. This Hadoop Tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Hadoop concepts.
This Edureka Hadoop Tutorial provides knowledge on:
1) What are the driving factors of Big Data and what are its challenges?
2) How Hadoop solves Big Data storage and processing challenges with Facebook use-case?
3) The overview of Hadoop YARN Architecture and its Components.
4) A real-life implementation of a complete end to end Hadoop Project on a Reddit use case on a Hadoop Cluster.
Check our complete Hadoop playlist here: https://goo.gl/ExJdZs
Hadoop Training For Beginners | Hadoop Tutorial | Big Data Training |EdurekaEdureka!
This Edureka Hadoop Training tutorial ( Hadoop Blog series: https://goo.gl/LFesy8 ) will help you to understand how Big Data emerged as a problem and how Hadoop solved that problem. This tutorial will be discussing about Hadoop Architecture, HDFS & it's architecture, YARN and MapReduce with a practical Aadhar use-case. Below are the topics covered in this tutorial:
1) What is Big Data?
2) Big Data in Different Domains
3) Problems Associated with Big Data
4) What is Hadoop?
5) HDFS
6) YARN
7) MapReduce
8) Hadoop Ecosystem
9) Aadhar Use-case
10) Edureka Big Data & Hadoop Training
Hadoop Administration Training | Hadoop Administration Tutorial | Hadoop Admi...Edureka!
This Edureka Hadoop Administration Training tutorial will help you understand the functions of all the Hadoop daemons and what are the configuration parameters involved with them. It will also take you through a step by step Multi-Node Hadoop Installation and will discuss all the configuration files in detail. Below are the topics covered in this tutorial:
1) What is Big Data?
2) Hadoop Ecosystem
3) Hadoop Core Components: HDFS & YARN
4) Hadoop Core Configuration Files
5) Multi Node Hadoop Installation
6) Tuning Hadoop using Configuration Files
7) Commissioning and Decommissioning the DataNode
8) Hadoop Web UI Components
9) Hadoop Job Responsibilities
What is Hadoop | Introduction to Hadoop | Hadoop Tutorial | Hadoop Training |...Edureka!
This Edureka "What is Hadoop" Tutorial (check our hadoop blog series here: https://goo.gl/lQKjL8) will help you understand all the basics of Hadoop. Learn about the differences in traditional and hadoop way of storing and processing data in detail. Below are the topics covered in this tutorial:
1) Traditional Way of Processing - SEARS
2) Big Data Growth Drivers
3) Problem Associated with Big Data
4) Hadoop: Solution to Big Data Problem
5) What is Hadoop?
6) HDFS
7) MapReduce
8) Hadoop Ecosystem
9) Demo: Hadoop Case Study - Orbitz
Subscribe to our channel to get updates.
Check our complete Hadoop playlist here: https://goo.gl/4OyoTW
Big Data Analytics Tutorial | Big Data Analytics for Beginners | Hadoop Tutor...Edureka!
This Edureka Big Data Analytics Tutorial will help you to understand the basics of Big Data domain. Learn how to analyze Big Data in this tutorial. Below are the topics covered in this tutorial:
1) Big Data Introduction
2) What is Big Data Analytics?
3) Why Big Data Analytics?
4) Stages in Big Data Analytics
5) Big Data Analytics Domains
6) Big Data Analytics Use Cases
Subscribe to our channel to get updates.
Check our complete Hadoop playlist here: https://goo.gl/4OyoTW
Webinar : Talend : The Non-Programmer's Swiss Knife for Big DataEdureka!
Talend Open Studio (TOS) is a wonderful open source Data Integration (DI) tool used to build end-to-end ETL solutions. This course will not only help the beginners to understand the art of data integration but also equip them with Big Data skills in the smart way. This course also aims to educate you about Big Data through Talend's powerful product "Talend for Big Data" (the first Hadoop-based data integration platform). The topics covered in the presentation are:
1. Why ETL is still essential and arrival of Big Data is not the doom of ETL era
2.How and why ETL is using Talend
3.Talend complementing Hadoop Ecosystem? Adopting to ETL-Big Data industry
4.Learn Big Data not in months but in Minutes! Sounds too good?
Apache Hadoop Tutorial | Hadoop Tutorial For Beginners | Big Data Hadoop | Ha...Edureka!
This Edureka "Hadoop tutorial For Beginners" ( Hadoop Blog series: https://goo.gl/LFesy8 ) will help you to understand the problem with traditional system while processing Big Data and how Hadoop solves it. This tutorial will provide you a comprehensive idea about HDFS and YARN along with their architecture that has been explained in a very simple manner using examples and practical demonstration. At the end, you will get to know how to analyze Olympic data set using Hadoop and gain useful insights.
Below are the topics covered in this tutorial:
1. Big Data Growth Drivers
2. What is Big Data?
3. Hadoop Introduction
4. Hadoop Master/Slave Architecture
5. Hadoop Core Components
6. HDFS Data Blocks
7. HDFS Read/Write Mechanism
8. What is MapReduce
9. MapReduce Program
10. MapReduce Job Workflow
11. Hadoop Ecosystem
12. Hadoop Use Case: Analyzing Olympic Dataset
Hadoop is an open source software framework that supports data-intensive distributed applications. Hadoop is licensed under the Apache v2 license. It is therefore generally known as Apache Hadoop. Hadoop has been developed, based on a paper originally written by Google on MapReduce system and applies concepts of functional programming. Hadoop is written in the Java programming language and is the highest-level Apache project being constructed and used by a global community of contributors. Hadoop was developed by Doug Cutting and Michael J. Cafarella. And just don't overlook the charming yellow elephant you see, which is basically named after Doug's son's toy elephant!
The topics covered in presentation are:
1. Big Data Learning Path
2.Big Data Introduction
3. Hadoop and its Eco-system
4.Hadoop Architecture
5.Next Step on how to setup Hadoop
This Hadoop tutorial on MapReduce Example ( Mapreduce Tutorial Blog Series: https://goo.gl/w0on2G ) will help you understand how to write a MapReduce program in Java. You will also get to see multiple mapreduce examples on Analytics and Testing.
Check our complete Hadoop playlist here: https://goo.gl/ExJdZs
Below are the topics covered in this tutorial:
1) MapReduce Way
2) Classes and Packages in MapReduce
3) Explanation of a Complete MapReduce Program
4) MapReduce Examples on Analytics
5) MapReduce Example on Testing - MRUnit
Changes Expected in Hadoop 3 | Getting to Know Hadoop 3 Alpha | Upcoming Hado...Edureka!
This Edureka tutorial on Hadoop 3 ( Hadoop Blog series: https://goo.gl/LFesy8 ) will help you to focus on the changes that are expected in Hadoop 3, as it's still in alpha phase. Apache community has incorporated many changes in Apache Hadoop 3 and is still working on some of them. So, we will be taking a broader look at the expected changes in Hadoop 3:
1. Support For Erasure Encoding In HDFS
2. YARN Timeline Service V.2
3. Shell Script Rewrite
4. Shaded Client Jars
5. Support For Opportunistic Containers
6. Mapreduce Task-level Native Optimization
7. Support For More Than 2 Passive Namenodes
8. Default Ports Of Multiple Services Have Been Changed
9. Intra-DataNode Balancer
Big Data and Hadoop training course is designed to provide knowledge and skills to become a successful Hadoop Developer. In-depth knowledge of concepts such as Hadoop Distributed File System, Setting up the Hadoop Cluster, Map-Reduce,PIG, HIVE, HBase, Zookeeper, SQOOP etc. will be covered in the course.
Big Data and Hadoop training course is designed to provide knowledge and skills to become a successful Hadoop Developer. In-depth knowledge of concepts such as Hadoop Distributed File System, Setting up the Hadoop Cluster, Map-Reduce,PIG, HIVE, HBase, Zookeeper, SQOOP etc. will be covered in the course.
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 Interview Questions And Answers Part-1 | Big Data Interview Questions ...Simplilearn
This video on Hadoop interview questions part-1 will take you through the general Hadoop questions and questions on HDFS, MapReduce and YARN, which are very likely to be asked in any Hadoop interview. It covers all the topics on the major components of Hadoop. This Hadoop tutorial will give you an idea about the different scenario-based questions you could face and some multiple-choice questions as well. Now, let us dive into this Hadoop interview questions video and gear up for youe next Hadoop Interview.
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
Database is the new black. Ever the backbone of information architectures, database technology continually evolves to meet growing and changing business needs. New types of data and applications make the database more important than ever, and understanding which technology best serves your use case is paramount to building durable systems. These days, the choices are many, so users should be careful when deciding which direction to go. Register for this Exploratory Webcast to hear veteran database Analyst Dr. Robin Bloor explain why the database market has exploded in recent years. He'll outline the current database landscape, and provide insights about which kinds of technologies are suitable for the growing variety of business needs today. He'll also focus on key auxiliary technologies that enable modern databases to do perform efficiently.
Hadoop Tutorial | What is Hadoop | Hadoop Project on Reddit | EdurekaEdureka!
This Edureka Hadoop Tutorial ( Hadoop Tutorial Blog Series: https://goo.gl/zndT2V ) helps you understand Big Data and Hadoop in detail. This Hadoop Tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Hadoop concepts.
This Edureka Hadoop Tutorial provides knowledge on:
1) What are the driving factors of Big Data and what are its challenges?
2) How Hadoop solves Big Data storage and processing challenges with Facebook use-case?
3) The overview of Hadoop YARN Architecture and its Components.
4) A real-life implementation of a complete end to end Hadoop Project on a Reddit use case on a Hadoop Cluster.
Check our complete Hadoop playlist here: https://goo.gl/ExJdZs
Hadoop Training For Beginners | Hadoop Tutorial | Big Data Training |EdurekaEdureka!
This Edureka Hadoop Training tutorial ( Hadoop Blog series: https://goo.gl/LFesy8 ) will help you to understand how Big Data emerged as a problem and how Hadoop solved that problem. This tutorial will be discussing about Hadoop Architecture, HDFS & it's architecture, YARN and MapReduce with a practical Aadhar use-case. Below are the topics covered in this tutorial:
1) What is Big Data?
2) Big Data in Different Domains
3) Problems Associated with Big Data
4) What is Hadoop?
5) HDFS
6) YARN
7) MapReduce
8) Hadoop Ecosystem
9) Aadhar Use-case
10) Edureka Big Data & Hadoop Training
Hadoop Administration Training | Hadoop Administration Tutorial | Hadoop Admi...Edureka!
This Edureka Hadoop Administration Training tutorial will help you understand the functions of all the Hadoop daemons and what are the configuration parameters involved with them. It will also take you through a step by step Multi-Node Hadoop Installation and will discuss all the configuration files in detail. Below are the topics covered in this tutorial:
1) What is Big Data?
2) Hadoop Ecosystem
3) Hadoop Core Components: HDFS & YARN
4) Hadoop Core Configuration Files
5) Multi Node Hadoop Installation
6) Tuning Hadoop using Configuration Files
7) Commissioning and Decommissioning the DataNode
8) Hadoop Web UI Components
9) Hadoop Job Responsibilities
What is Hadoop | Introduction to Hadoop | Hadoop Tutorial | Hadoop Training |...Edureka!
This Edureka "What is Hadoop" Tutorial (check our hadoop blog series here: https://goo.gl/lQKjL8) will help you understand all the basics of Hadoop. Learn about the differences in traditional and hadoop way of storing and processing data in detail. Below are the topics covered in this tutorial:
1) Traditional Way of Processing - SEARS
2) Big Data Growth Drivers
3) Problem Associated with Big Data
4) Hadoop: Solution to Big Data Problem
5) What is Hadoop?
6) HDFS
7) MapReduce
8) Hadoop Ecosystem
9) Demo: Hadoop Case Study - Orbitz
Subscribe to our channel to get updates.
Check our complete Hadoop playlist here: https://goo.gl/4OyoTW
Big Data Analytics Tutorial | Big Data Analytics for Beginners | Hadoop Tutor...Edureka!
This Edureka Big Data Analytics Tutorial will help you to understand the basics of Big Data domain. Learn how to analyze Big Data in this tutorial. Below are the topics covered in this tutorial:
1) Big Data Introduction
2) What is Big Data Analytics?
3) Why Big Data Analytics?
4) Stages in Big Data Analytics
5) Big Data Analytics Domains
6) Big Data Analytics Use Cases
Subscribe to our channel to get updates.
Check our complete Hadoop playlist here: https://goo.gl/4OyoTW
Webinar : Talend : The Non-Programmer's Swiss Knife for Big DataEdureka!
Talend Open Studio (TOS) is a wonderful open source Data Integration (DI) tool used to build end-to-end ETL solutions. This course will not only help the beginners to understand the art of data integration but also equip them with Big Data skills in the smart way. This course also aims to educate you about Big Data through Talend's powerful product "Talend for Big Data" (the first Hadoop-based data integration platform). The topics covered in the presentation are:
1. Why ETL is still essential and arrival of Big Data is not the doom of ETL era
2.How and why ETL is using Talend
3.Talend complementing Hadoop Ecosystem? Adopting to ETL-Big Data industry
4.Learn Big Data not in months but in Minutes! Sounds too good?
Apache Hadoop Tutorial | Hadoop Tutorial For Beginners | Big Data Hadoop | Ha...Edureka!
This Edureka "Hadoop tutorial For Beginners" ( Hadoop Blog series: https://goo.gl/LFesy8 ) will help you to understand the problem with traditional system while processing Big Data and how Hadoop solves it. This tutorial will provide you a comprehensive idea about HDFS and YARN along with their architecture that has been explained in a very simple manner using examples and practical demonstration. At the end, you will get to know how to analyze Olympic data set using Hadoop and gain useful insights.
Below are the topics covered in this tutorial:
1. Big Data Growth Drivers
2. What is Big Data?
3. Hadoop Introduction
4. Hadoop Master/Slave Architecture
5. Hadoop Core Components
6. HDFS Data Blocks
7. HDFS Read/Write Mechanism
8. What is MapReduce
9. MapReduce Program
10. MapReduce Job Workflow
11. Hadoop Ecosystem
12. Hadoop Use Case: Analyzing Olympic Dataset
Hadoop is an open source software framework that supports data-intensive distributed applications. Hadoop is licensed under the Apache v2 license. It is therefore generally known as Apache Hadoop. Hadoop has been developed, based on a paper originally written by Google on MapReduce system and applies concepts of functional programming. Hadoop is written in the Java programming language and is the highest-level Apache project being constructed and used by a global community of contributors. Hadoop was developed by Doug Cutting and Michael J. Cafarella. And just don't overlook the charming yellow elephant you see, which is basically named after Doug's son's toy elephant!
The topics covered in presentation are:
1. Big Data Learning Path
2.Big Data Introduction
3. Hadoop and its Eco-system
4.Hadoop Architecture
5.Next Step on how to setup Hadoop
This Hadoop tutorial on MapReduce Example ( Mapreduce Tutorial Blog Series: https://goo.gl/w0on2G ) will help you understand how to write a MapReduce program in Java. You will also get to see multiple mapreduce examples on Analytics and Testing.
Check our complete Hadoop playlist here: https://goo.gl/ExJdZs
Below are the topics covered in this tutorial:
1) MapReduce Way
2) Classes and Packages in MapReduce
3) Explanation of a Complete MapReduce Program
4) MapReduce Examples on Analytics
5) MapReduce Example on Testing - MRUnit
Changes Expected in Hadoop 3 | Getting to Know Hadoop 3 Alpha | Upcoming Hado...Edureka!
This Edureka tutorial on Hadoop 3 ( Hadoop Blog series: https://goo.gl/LFesy8 ) will help you to focus on the changes that are expected in Hadoop 3, as it's still in alpha phase. Apache community has incorporated many changes in Apache Hadoop 3 and is still working on some of them. So, we will be taking a broader look at the expected changes in Hadoop 3:
1. Support For Erasure Encoding In HDFS
2. YARN Timeline Service V.2
3. Shell Script Rewrite
4. Shaded Client Jars
5. Support For Opportunistic Containers
6. Mapreduce Task-level Native Optimization
7. Support For More Than 2 Passive Namenodes
8. Default Ports Of Multiple Services Have Been Changed
9. Intra-DataNode Balancer
Big Data and Hadoop training course is designed to provide knowledge and skills to become a successful Hadoop Developer. In-depth knowledge of concepts such as Hadoop Distributed File System, Setting up the Hadoop Cluster, Map-Reduce,PIG, HIVE, HBase, Zookeeper, SQOOP etc. will be covered in the course.
Big Data and Hadoop training course is designed to provide knowledge and skills to become a successful Hadoop Developer. In-depth knowledge of concepts such as Hadoop Distributed File System, Setting up the Hadoop Cluster, Map-Reduce,PIG, HIVE, HBase, Zookeeper, SQOOP etc. will be covered in the course.
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 Interview Questions And Answers Part-1 | Big Data Interview Questions ...Simplilearn
This video on Hadoop interview questions part-1 will take you through the general Hadoop questions and questions on HDFS, MapReduce and YARN, which are very likely to be asked in any Hadoop interview. It covers all the topics on the major components of Hadoop. This Hadoop tutorial will give you an idea about the different scenario-based questions you could face and some multiple-choice questions as well. Now, let us dive into this Hadoop interview questions video and gear up for youe next Hadoop Interview.
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
Database is the new black. Ever the backbone of information architectures, database technology continually evolves to meet growing and changing business needs. New types of data and applications make the database more important than ever, and understanding which technology best serves your use case is paramount to building durable systems. These days, the choices are many, so users should be careful when deciding which direction to go. Register for this Exploratory Webcast to hear veteran database Analyst Dr. Robin Bloor explain why the database market has exploded in recent years. He'll outline the current database landscape, and provide insights about which kinds of technologies are suitable for the growing variety of business needs today. He'll also focus on key auxiliary technologies that enable modern databases to do perform efficiently.
Why CxOs care about Data Governance; the roadblock to digital masteryCoert Du Plessis (杜康)
This talk covered how data governance are scaled in large organisations, defining self-sustaining ownership models, a mechanism for managing risk and delegating decisions to those with the most knowhow.
Denodo Data Innovation Award: The Largest Logical Data Warehouse on the Plane...Denodo
Watch full webinar here: https://bit.ly/3eBX4X8
In this session, Jan will showcase how Nedbank Enterprise Data Services has implemented Denodo as a data virtualisation solution since 2017. The initial use case was to create an abstracted access layer on the legacy Operational Data Store. Four years down the line, the Nedbank is virtualizing many non-warehouse data sources, the entire legacy data warehouse as well as the target state data lake and data marts. The views are dynamically kept up to date when table changes occur. By 2021 Nedbank is expected to have a hybrid cloud Denodo solution in place and reporting which sources from Denodo enabled APIs and will be available via the Nedbank API marketplace.
Is It A Right Time For Me To Learn Hadoop. Find out ?Edureka!
Forrester predicts, CIOs who are late to the Hadoop game will finally make the platform a priority in 2015. Hadoop has evolved as a must-to-know technology and has been a reason for better career, salary and job opportunities for many professionals.
Hadoop was born out of the need to process Big Data.Today data is being generated liked never before and it is becoming difficult to store and process this enormous volume and large variety of data, In order to cope this Big Data technology comes in.Today Hadoop software stack is go-to framework for large scale,data intensive storage and compute solution for Big Data Analytics Applications.The beauty of Hadoop is that it is designed to process large volume of data in clustered commodity computers work in parallel.Distributing the data that is too large across the nodes in clusters solves the problem of having too large data sets to be processed onto the single machine.
INTRODUCTION TO BIG DATA AND HADOOP
9
Introduction to Big Data, Types of Digital Data, Challenges of conventional systems - Web data, Evolution of analytic processes and tools, Analysis Vs reporting - Big Data Analytics, Introduction to Hadoop - Distributed Computing
Challenges - History of Hadoop, Hadoop Eco System - Use case of Hadoop – Hadoop Distributors – HDFS – Processing Data with Hadoop – Map Reduce.
This is a power point presentation on Hadoop and Big Data. This covers the essential knowledge one should have when stepping into the world of Big Data.
This course is available on hadoop-skills.com for free!
This course builds a basic fundamental understanding of Big Data problems and Hadoop as a solution. This course takes you through:
• This course builds Understanding of Big Data problems with easy to understand examples and illustrations.
• History and advent of Hadoop right from when Hadoop wasn’t even named Hadoop and was called Nutch
• What is Hadoop Magic which makes it so unique and powerful.
• Understanding the difference between Data science and data engineering, which is one of the big confusions in selecting a carrier or understanding a job role.
• And most importantly, demystifying Hadoop vendors like Cloudera, MapR and Hortonworks by understanding about them.
This course is available for free on hadoop-skills.com
Incorporating the Data Lake into Your Analytic ArchitectureCaserta
Joe Caserta, President at Caserta Concepts presented at the 3rd Annual Enterprise DATAVERSITY conference. The emphasis of this year's agenda is on the key strategies and architecture necessary to create a successful, modern data analytics organization.
Joe Caserta presented Incorporating the Data Lake into Your Analytics Architecture.
For more information on the services offered by Caserta Concepts, visit out website at http://casertaconcepts.com/.
20th Athens Big Data Meetup - 1st Talk - Druid: the open source, performant, ...Athens Big Data
Title: Druid: the open source, performant, real-time, analytical datastore
Speaker: Peter Marshall (https://linkedin.com/in/amillionbytes/)
Date: Tuesday, January 28, 2020
Event: https://meetup.com/Athens-Big-Data/events/266900242/
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
Similar to What is Hadoop? Nov 20 2013 - IRMAC (20)
2015 nov 27_thug_paytm_rt_ingest_brief_finalAdam Muise
Paytm Labs provides a quick overview of their Hadoop data ingest platform. We cover our journey from a batch focused ingest system with SQOOP to a streaming ingest supported by Kafka, Confluent.io, Hadoop, Cassandra, and Spark Streaming. This presentation also provides an overview of our complete data platform including our feature creation template
Moving to a data-centric architecture: Toronto Data Unconference 2015Adam Muise
Why use a datalake? Why use lambda? A conversation starter for Toronto Data Unconference 2015. We will discuss technologies such as Hadoop, Kafka, Spark Streaming, and Cassandra.
Creating a Data Science Team from an Architect's perspective. This is about team building on how to support a data science team with the right staff, including data engineers and devops.
An overview of securing Hadoop. Content primarily by Balaji Ganesan, one of the leaders of the Apache Argus project. Presented on Sept 4, 2014 at the Toronto Hadoop User Group by Adam Muise.
Sept 17 2013 - THUG - HBase a Technical IntroductionAdam Muise
HBase Technical Introduction. This deck includes a description of memory design, write path, read path, some operational tidbits, SQL on HBase (Phoenix and Hive), as well as HOYA (HBase on YARN).
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex ProofsAlex Pruden
This paper presents Reef, a system for generating publicly verifiable succinct non-interactive zero-knowledge proofs that a committed document matches or does not match a regular expression. We describe applications such as proving the strength of passwords, the provenance of email despite redactions, the validity of oblivious DNS queries, and the existence of mutations in DNA. Reef supports the Perl Compatible Regular Expression syntax, including wildcards, alternation, ranges, capture groups, Kleene star, negations, and lookarounds. Reef introduces a new type of automata, Skipping Alternating Finite Automata (SAFA), that skips irrelevant parts of a document when producing proofs without undermining soundness, and instantiates SAFA with a lookup argument. Our experimental evaluation confirms that Reef can generate proofs for documents with 32M characters; the proofs are small and cheap to verify (under a second).
Paper: https://eprint.iacr.org/2023/1886
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
The Metaverse and AI: how can decision-makers harness the Metaverse for their...Jen Stirrup
The Metaverse is popularized in science fiction, and now it is becoming closer to being a part of our daily lives through the use of social media and shopping companies. How can businesses survive in a world where Artificial Intelligence is becoming the present as well as the future of technology, and how does the Metaverse fit into business strategy when futurist ideas are developing into reality at accelerated rates? How do we do this when our data isn't up to scratch? How can we move towards success with our data so we are set up for the Metaverse when it arrives?
How can you help your company evolve, adapt, and succeed using Artificial Intelligence and the Metaverse to stay ahead of the competition? What are the potential issues, complications, and benefits that these technologies could bring to us and our organizations? In this session, Jen Stirrup will explain how to start thinking about these technologies as an organisation.
Welcome to the first live UiPath Community Day Dubai! Join us for this unique occasion to meet our local and global UiPath Community and leaders. You will get a full view of the MEA region's automation landscape and the AI Powered automation technology capabilities of UiPath. Also, hosted by our local partners Marc Ellis, you will enjoy a half-day packed with industry insights and automation peers networking.
📕 Curious on our agenda? Wait no more!
10:00 Welcome note - UiPath Community in Dubai
Lovely Sinha, UiPath Community Chapter Leader, UiPath MVPx3, Hyper-automation Consultant, First Abu Dhabi Bank
10:20 A UiPath cross-region MEA overview
Ashraf El Zarka, VP and Managing Director MEA, UiPath
10:35: Customer Success Journey
Deepthi Deepak, Head of Intelligent Automation CoE, First Abu Dhabi Bank
11:15 The UiPath approach to GenAI with our three principles: improve accuracy, supercharge productivity, and automate more
Boris Krumrey, Global VP, Automation Innovation, UiPath
12:15 To discover how Marc Ellis leverages tech-driven solutions in recruitment and managed services.
Brendan Lingam, Director of Sales and Business Development, Marc Ellis
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
9. Put
it
away,
delete
it,
tweet
it,
compress
it,
shred
it,
wikileak-‐it,
put
it
in
a
database,
put
it
in
SAN/NAS,
put
it
in
the
cloud,
hide
it
in
tape…
10. You
are
obsessive
compulsive
about
collec=ng
and
structuring
your
data.
19. Another
EDW
Analy=cal
DB
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
The
solu=on?
EDW
Data
Data
Data
Data
Data
Data
Data
Data
Data
OLTP
Data
Data
Data
Data
Data
Data
Data
Data
Data
Yet
Another
EDW
Data
Data
Data
Data
Data
Data
Data
Data
Data
20. Another
EDW
Analy=cal
DB
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
OLTP
Ummm…you
dropped
something
EDW
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Yet
Another
EDW
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
23. Wait,
you’ve
seen
this
before.
Data
Data
Data
…
Sausage
Factory
Data
Data
Data
Data
Data
Data
Data
Data
Data
…
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
26. “Prices,
Stupid
passwords,
and
Boring
Sta=s=cs.”
-‐
Hans
Rosling
h"p://www.youtube.com/watch?v=hVimVzgtD6w
27. Your
data
silos
are
lonely
places.
EDW
Accounts
Customers
Web
Proper=es
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
28. …
Data
likes
to
be
together.
EDW
Accounts
Customers
Data
Data
Web
Proper=es
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
29. CDR
Data
Data
Data
Machine
Data
Facebook
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Weather
Data
TwiYer
Data
Data
likes
to
socialize
too.
Data
Data
EDW
Data
Data
Data
Data
Data
Data
Accounts
Data
Web
Proper=es
Data
Data
Data
Customers
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
30. New
types
of
data
don’t
quite
fit
into
your
pris=ne
view
of
the
world.
Logs
Data
Data
Data
Data
Data
Data
Data
Machine
Data
Data
Data
Data
Data
Data
Data
Data
My
LiYle
Data
Empire
Data
?
Data
?
Data
Data
Data
Data
Data
?
?
Data
Data
31. To
resolve
this,
some
people
take
hints
from
Lord
Of
The
Rings...
33. ETL
Data
Data
Data
ETL
ETL
ETL
EDW
Data
Data
Data
Data
Data
Schema
Data
Data
Data
Data
…but
that
has
its
problems
too.
ETL
Data
Data
Data
ETL
ETL
ETL
EDW
Data
Data
Data
Data
Data
Schema
Data
Data
Data
Data
41. If
you
could
design
a
system
that
would
handle
this,
what
would
it
look
like?
42. It
would
probably
need
a
highly
resilient,
self-‐healing,
cost-‐efficient,
distributed
file
system…
Storage
Storage
Storage
Storage
Storage
Storage
Storage
Storage
Storage
43. It
would
probably
need
a
completely
parallel
processing
framework
that
took
tasks
to
the
data…
Processing
Processing
Processing
Storage
Storage
Storage
Processing
Processing
Processing
Storage
Storage
Storage
Processing
Processing
Processing
Storage
Storage
Storage
44. It
would
probably
run
on
commodity
hardware,
virtualized
machines,
and
common
OS
pladorms
Processing
Processing
Processing
Storage
Storage
Storage
Processing
Processing
Processing
Storage
Storage
Storage
Processing
Processing
Processing
Storage
Storage
Storage
45. It
would
probably
be
open
source
so
innova=on
could
happen
as
quickly
as
possible
48. HDFS
stores
data
in
blocks
and
replicates
those
blocks
block1
Processing
Processing
Processing
Storage
Storage
Storage
block2
block2
Processing
Processing
Processing
block1
Storage
Storage
Storage
block3
block2
Processing
Storage
block3
Processing
Processing
block1
Storage
Storage
block3
49. If
a
block
fails
then
HDFS
always
has
the
other
copies
and
heals
itself
block1
Processing
Processing
Processing
block3
Storage
Storage
Storage
block2
block2
Processing
Processing
Processing
block1
Storage
Storage
Storage
block3
block2
Processing
Storage
block3
Processing
Processing
block1
Storage
Storage
X
50. MapReduce
is
a
programming
paradigm
that
completely
parallel
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Mapper
Mapper
Mapper
Mapper
Mapper
Reducer
Data
Data
Data
Reducer
Data
Data
Data
Reducer
Data
Data
Data
51. MapReduce
has
three
phases:
Map,
Sort/Shuffle,
Reduce
Key,
Value
Key,
Value
Key,
Value
Key,
Value
Key,
Value
Key,
Value
Key,
Value
Key,
Value
Key,
Value
Mapper
Mapper
Key,
Value
Key,
Value
Key,
Value
Reducer
Key,
Value
Key,
Value
Key,
Value
Mapper
Reducer
Key,
Value
Key,
Value
Key,
Value
Key,
Value
Key,
Value
Key,
Value
Key,
Value
Key,
Value
Key,
Value
Mapper
Reducer
Key,
Value
Key,
Value
Key,
Value
Key,
Value
Key,
Value
Key,
Value
Mapper
Key,
Value
Key,
Value
Key,
Value
52. MapReduce
applies
to
a
lot
of
data
processing
problems
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Mapper
Mapper
Mapper
Mapper
Mapper
Reducer
Data
Data
Data
Reducer
Data
Data
Data
Reducer
Data
Data
Data
55. YARN
abstracts
resource
management
so
you
can
run
more
than
just
MapReduce
MapReduce
V2
MapReduce
V?
STORM
Giraph
Tez
YARN
HDFS2
MPI
HBase
…
and
more