This document contains a summary of a presentation on Hadoop given by Henk van der Valk and Jan Pieter Posthuma on 7/11/2013. The presentation covered an introduction to Hadoop and its core components like HDFS, MapReduce, Hive and how to access data from and to Hadoop using tools like PolyBase. It also provided examples of using Hive and Excel to query and extract data from Hadoop and loading data between Hadoop and SQL Server Parallel Data Warehouse.
Presentation on 2013-06-27, Workshop on the future of Big Data management, discussing hadoop for a science audience that are either HPC/grid users or people suddenly discovering that their data is accruing towards PB.
The other talks were on GPFS, LustreFS and Ceph, so rather than just do beauty-contest slides, I decided to raise the question of "what is a filesystem?", whether the constraints imposed by the Unix metaphor and API are becoming limits on scale and parallelism (both technically and, for GPFS and Lustre Enterprise in cost).
Then: HDFS as the foundation for the Hadoop stack.
All the other FS talks did emphasise their Hadoop integration, with the Intel talk doing the most to assert performance improvements of LustreFS over HDFSv1 in dfsIO and Terasort (no gridmix?), which showed something important: Hadoop is the application that add DFS developers have to have a story for
This presentation about Hadoop architecture will help you understand the architecture of Apache Hadoop in detail. In this video, you will learn what is Hadoop, components of Hadoop, what is HDFS, HDFS architecture, Hadoop MapReduce, Hadoop MapReduce example, Hadoop YARN and finally, a demo on MapReduce. Apache Hadoop offers a versatile, adaptable and reliable distributed computing big data framework for a group of systems with capacity limit and local computing power. After watching this video, you will also understand the Hadoop Distributed File System and its features along with the practical implementation.
Below are the topics covered in this Hadoop Architecture presentation:
1. What is Hadoop?
2. Components of Hadoop
3. What is HDFS?
4. HDFS Architecture
5. Hadoop MapReduce
6. Hadoop MapReduce Example
7. Hadoop YARN
8. Demo on MapReduce
What are the course objectives?
This course will enable you to:
1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Who should take up this Big Data and Hadoop Certification Training Course?
Big Data career opportunities are on the rise, and Hadoop is quickly becoming a must-know technology for the following professionals:
1. Software Developers and Architects
2. Analytics Professionals
3. Senior IT professionals
4. Testing and Mainframe professionals
5. Data Management Professionals
6. Business Intelligence Professionals
7. Project Managers
8. Aspiring Data Scientists
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
Presentation on 2013-06-27, Workshop on the future of Big Data management, discussing hadoop for a science audience that are either HPC/grid users or people suddenly discovering that their data is accruing towards PB.
The other talks were on GPFS, LustreFS and Ceph, so rather than just do beauty-contest slides, I decided to raise the question of "what is a filesystem?", whether the constraints imposed by the Unix metaphor and API are becoming limits on scale and parallelism (both technically and, for GPFS and Lustre Enterprise in cost).
Then: HDFS as the foundation for the Hadoop stack.
All the other FS talks did emphasise their Hadoop integration, with the Intel talk doing the most to assert performance improvements of LustreFS over HDFSv1 in dfsIO and Terasort (no gridmix?), which showed something important: Hadoop is the application that add DFS developers have to have a story for
This presentation about Hadoop architecture will help you understand the architecture of Apache Hadoop in detail. In this video, you will learn what is Hadoop, components of Hadoop, what is HDFS, HDFS architecture, Hadoop MapReduce, Hadoop MapReduce example, Hadoop YARN and finally, a demo on MapReduce. Apache Hadoop offers a versatile, adaptable and reliable distributed computing big data framework for a group of systems with capacity limit and local computing power. After watching this video, you will also understand the Hadoop Distributed File System and its features along with the practical implementation.
Below are the topics covered in this Hadoop Architecture presentation:
1. What is Hadoop?
2. Components of Hadoop
3. What is HDFS?
4. HDFS Architecture
5. Hadoop MapReduce
6. Hadoop MapReduce Example
7. Hadoop YARN
8. Demo on MapReduce
What are the course objectives?
This course will enable you to:
1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Who should take up this Big Data and Hadoop Certification Training Course?
Big Data career opportunities are on the rise, and Hadoop is quickly becoming a must-know technology for the following professionals:
1. Software Developers and Architects
2. Analytics Professionals
3. Senior IT professionals
4. Testing and Mainframe professionals
5. Data Management Professionals
6. Business Intelligence Professionals
7. Project Managers
8. Aspiring Data Scientists
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
This presentation will make reader understand about the flow mechanism of data in the HDFS cluster with some basic points discussed on Resource Management.
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.
This Hadoop will help you understand the different tools present in the Hadoop ecosystem. This Hadoop video will take you through an overview of the important tools of Hadoop ecosystem which include Hadoop HDFS, Hadoop Pig, Hadoop Yarn, Hadoop Hive, Apache Spark, Mahout, Apache Kafka, Storm, Sqoop, Apache Ranger, Oozie and also discuss the architecture of these tools. It will cover the different tasks of Hadoop such as data storage, data processing, cluster resource management, data ingestion, machine learning, streaming and more. Now, let us get started and understand each of these tools in detail.
Below topics are explained in this Hadoop ecosystem presentation:
1. What is Hadoop ecosystem?
1. Pig (Scripting)
2. Hive (SQL queries)
3. Apache Spark (Real-time data analysis)
4. Mahout (Machine learning)
5. Apache Ambari (Management and monitoring)
6. Kafka & Storm
7. Apache Ranger & Apache Knox (Security)
8. Oozie (Workflow system)
9. Hadoop MapReduce (Data processing)
10. Hadoop Yarn (Cluster resource management)
11. Hadoop HDFS (Data storage)
12. Sqoop & Flume (Data collection and ingestion)
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Learn Spark SQL, creating, transforming, and querying Data frames
14. Understand the common use-cases of Spark and the various interactive algorithms
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training.
Hadoop Institutes : kelly technologies is the best Hadoop Training Institutes in Hyderabad. Providing Hadoop training by real time faculty in Hyderabad.
The Apache™ Hadoop® project develops open-source software for reliable, scalable, distributed computing.
The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high-availability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-available service on top of a cluster of computers, each of which may be prone to failures.
Hadoop Training | Hadoop Training For Beginners | Hadoop Architecture | Hadoo...Simplilearn
This presentation about Hadoop training will help you understand the need for Hadoop, what is Hadoop and concepts including Hadoop ecosystem, Hadoop features, how HDFS works, what is MapReduce and how YARN works. Finally, we will implement a banking case study using Hadoop. To solve the issue of rapidly increasing data, we need big data technologies such as Hadoop, Spark, Storm, Cassandra and many more. Hadoop can store and process vast volumes of data. You will understand the architecture of HDFS, MapReduce workflow and the architecture of YARN. In the demo, you will learn in detail on how to export data from RDBMS (MySQL) into HDFS using Sqoop commands. Now, let us get started and gain expertise with Hadoop training video.
Below topics are explained in this Hadoop training presentation:
1. Need for Hadoop
2. What is Hadoop
3. Hadoop ecosystem
4. Hadoop features
5. What is HDFS
6. What is MapReduce
7. What is YARN
8. Bank case study
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
These slides cover the very basics of Hadoop architecture, in particular HDFS. This was my presentation in the first Delhi Hadoop User Group (DHUG) meetup held at Gurgaon on 10th September 2011. Loved the positive feedback. I'll also upload a more elaborate version covering Hadoop mapreduce architecture as well soon. Most of the stuff covered in these slides can be found in Tom White's book as well (See the last slide)
There's a big shift in both at the architecture and api level from Hadoop 1 vs Hadoop 2, particularly YARN and we had our first meetup to talk about this (http://www.meetup.com/Atlanta-YARN-User-Group/) on 10/13/2013.
WANdisco is a provider of non-stop software for global enterprises to meet the challenges of Big Data and distributed software development.
KEY HIGHLIGHTS, Session 1: Tuesday, Feb. 26, 5:15 p.m.-6 p.m.
Hadoop and HBase on the Cloud: A Case Study on Performance and Isolation
Cloud infrastructure is a flexible tool to orchestrate multiple Hadoop and HBase clusters, which provides strict isolation of data and compute resources for multiple customers. Most importantly, our benchmarks show that virtualized environment allows for higher average utilization of per-node resources. For more session information, visit http://na.apachecon.com/schedule/presentation/131/.
CO-PRESENTERS, Dr. Konstantin V. Shvachko, Chief Architect, Big Data, WANdisco and Jagane Sundar, CTO/VP Engineering, Big Data, WANdisco
A veteran Hadoop developer and respected author, Konstantin Shvachko is a technical expert specializing in efficient data structures and algorithms for large-scale distributed storage systems. Konstantin joined WANdisco through the AltoStor acquisition and before that he was founder and Chief Scientist at AltoScale, a Hadoop and HBase-as-a-Platform company acquired by VertiCloud. Konstantin played a lead architectural role at eBay, building two generations of the organization's Hadoop platform. At Yahoo!, he worked on the Hadoop Distributed File System (HDFS). Konstantin has dozens of publications and presentations to his credit and is currently a member of the Apache Hadoop PMC. Konstantin has a Ph.D. in Computer Science and M.S. in Mathematics from Moscow State University, Russia.
Jagane Sundar has extensive big data, cloud, virtualization, and networking experience and joined WANdisco through its AltoStor acquisition. Before AltoStor, Jagane was founder and CEO of AltoScale, a Hadoop and HBase-as-a-Platform company acquired by VertiCloud. His experience with Hadoop began as Director of Hadoop Performance and Operability at Yahoo! Jagane has such accomplishments to his credit as the creation of Livebackup, development of a user mode TCP Stack for Precision I/O, development of the NFS and PPP clients and parts of the TCP stack for JavaOS for Sun MicroSystems, and more. Jagane received his B.E. in Electronics and Communications Engineering from Anna University.
About WANdisco
WANdisco ( LSE : WAND ) is a provider of enterprise-ready, non-stop software solutions that enable globally distributed organizations to meet today's data challenges of secure storage, scalability and availability. WANdisco's products are differentiated by the company's patented, active-active data replication technology, serving crucial high availability (HA) requirements, including Hadoop Big Data and Application Lifecycle Management (ALM). Fortune Global 1000 companies including AT&T, Motorola, Intel and Halliburton rely on WANdisco for performance, reliability, security and availability. For additional information, please visit www.wandisco.com.
This presentation will give you Information about :
1. What is Hadoop,
2. History of Hadoop,
3. Building Blocks – Hadoop Eco-System,
4. Who is behind Hadoop?,
5. What Hadoop is good for and why it is Good?,
World of Watson 2016 - Information InsecurityKeith Redman
We call it security, however we’re really dealing with our insecurities, especially around our information.
The recent Yahoo announcement is astonishing, not because it happened or the number of people potentially exposed, but for the time it took to realize it had happened – approximately 2 years(?)! Information is the lifeblood of Analytics. We need it and we need to protect it. Check out these sessions to see what’s new in addressing our Insecurities about our Information.
This presentation will make reader understand about the flow mechanism of data in the HDFS cluster with some basic points discussed on Resource Management.
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.
This Hadoop will help you understand the different tools present in the Hadoop ecosystem. This Hadoop video will take you through an overview of the important tools of Hadoop ecosystem which include Hadoop HDFS, Hadoop Pig, Hadoop Yarn, Hadoop Hive, Apache Spark, Mahout, Apache Kafka, Storm, Sqoop, Apache Ranger, Oozie and also discuss the architecture of these tools. It will cover the different tasks of Hadoop such as data storage, data processing, cluster resource management, data ingestion, machine learning, streaming and more. Now, let us get started and understand each of these tools in detail.
Below topics are explained in this Hadoop ecosystem presentation:
1. What is Hadoop ecosystem?
1. Pig (Scripting)
2. Hive (SQL queries)
3. Apache Spark (Real-time data analysis)
4. Mahout (Machine learning)
5. Apache Ambari (Management and monitoring)
6. Kafka & Storm
7. Apache Ranger & Apache Knox (Security)
8. Oozie (Workflow system)
9. Hadoop MapReduce (Data processing)
10. Hadoop Yarn (Cluster resource management)
11. Hadoop HDFS (Data storage)
12. Sqoop & Flume (Data collection and ingestion)
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Learn Spark SQL, creating, transforming, and querying Data frames
14. Understand the common use-cases of Spark and the various interactive algorithms
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training.
Hadoop Institutes : kelly technologies is the best Hadoop Training Institutes in Hyderabad. Providing Hadoop training by real time faculty in Hyderabad.
The Apache™ Hadoop® project develops open-source software for reliable, scalable, distributed computing.
The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high-availability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-available service on top of a cluster of computers, each of which may be prone to failures.
Hadoop Training | Hadoop Training For Beginners | Hadoop Architecture | Hadoo...Simplilearn
This presentation about Hadoop training will help you understand the need for Hadoop, what is Hadoop and concepts including Hadoop ecosystem, Hadoop features, how HDFS works, what is MapReduce and how YARN works. Finally, we will implement a banking case study using Hadoop. To solve the issue of rapidly increasing data, we need big data technologies such as Hadoop, Spark, Storm, Cassandra and many more. Hadoop can store and process vast volumes of data. You will understand the architecture of HDFS, MapReduce workflow and the architecture of YARN. In the demo, you will learn in detail on how to export data from RDBMS (MySQL) into HDFS using Sqoop commands. Now, let us get started and gain expertise with Hadoop training video.
Below topics are explained in this Hadoop training presentation:
1. Need for Hadoop
2. What is Hadoop
3. Hadoop ecosystem
4. Hadoop features
5. What is HDFS
6. What is MapReduce
7. What is YARN
8. Bank case study
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
These slides cover the very basics of Hadoop architecture, in particular HDFS. This was my presentation in the first Delhi Hadoop User Group (DHUG) meetup held at Gurgaon on 10th September 2011. Loved the positive feedback. I'll also upload a more elaborate version covering Hadoop mapreduce architecture as well soon. Most of the stuff covered in these slides can be found in Tom White's book as well (See the last slide)
There's a big shift in both at the architecture and api level from Hadoop 1 vs Hadoop 2, particularly YARN and we had our first meetup to talk about this (http://www.meetup.com/Atlanta-YARN-User-Group/) on 10/13/2013.
WANdisco is a provider of non-stop software for global enterprises to meet the challenges of Big Data and distributed software development.
KEY HIGHLIGHTS, Session 1: Tuesday, Feb. 26, 5:15 p.m.-6 p.m.
Hadoop and HBase on the Cloud: A Case Study on Performance and Isolation
Cloud infrastructure is a flexible tool to orchestrate multiple Hadoop and HBase clusters, which provides strict isolation of data and compute resources for multiple customers. Most importantly, our benchmarks show that virtualized environment allows for higher average utilization of per-node resources. For more session information, visit http://na.apachecon.com/schedule/presentation/131/.
CO-PRESENTERS, Dr. Konstantin V. Shvachko, Chief Architect, Big Data, WANdisco and Jagane Sundar, CTO/VP Engineering, Big Data, WANdisco
A veteran Hadoop developer and respected author, Konstantin Shvachko is a technical expert specializing in efficient data structures and algorithms for large-scale distributed storage systems. Konstantin joined WANdisco through the AltoStor acquisition and before that he was founder and Chief Scientist at AltoScale, a Hadoop and HBase-as-a-Platform company acquired by VertiCloud. Konstantin played a lead architectural role at eBay, building two generations of the organization's Hadoop platform. At Yahoo!, he worked on the Hadoop Distributed File System (HDFS). Konstantin has dozens of publications and presentations to his credit and is currently a member of the Apache Hadoop PMC. Konstantin has a Ph.D. in Computer Science and M.S. in Mathematics from Moscow State University, Russia.
Jagane Sundar has extensive big data, cloud, virtualization, and networking experience and joined WANdisco through its AltoStor acquisition. Before AltoStor, Jagane was founder and CEO of AltoScale, a Hadoop and HBase-as-a-Platform company acquired by VertiCloud. His experience with Hadoop began as Director of Hadoop Performance and Operability at Yahoo! Jagane has such accomplishments to his credit as the creation of Livebackup, development of a user mode TCP Stack for Precision I/O, development of the NFS and PPP clients and parts of the TCP stack for JavaOS for Sun MicroSystems, and more. Jagane received his B.E. in Electronics and Communications Engineering from Anna University.
About WANdisco
WANdisco ( LSE : WAND ) is a provider of enterprise-ready, non-stop software solutions that enable globally distributed organizations to meet today's data challenges of secure storage, scalability and availability. WANdisco's products are differentiated by the company's patented, active-active data replication technology, serving crucial high availability (HA) requirements, including Hadoop Big Data and Application Lifecycle Management (ALM). Fortune Global 1000 companies including AT&T, Motorola, Intel and Halliburton rely on WANdisco for performance, reliability, security and availability. For additional information, please visit www.wandisco.com.
This presentation will give you Information about :
1. What is Hadoop,
2. History of Hadoop,
3. Building Blocks – Hadoop Eco-System,
4. Who is behind Hadoop?,
5. What Hadoop is good for and why it is Good?,
World of Watson 2016 - Information InsecurityKeith Redman
We call it security, however we’re really dealing with our insecurities, especially around our information.
The recent Yahoo announcement is astonishing, not because it happened or the number of people potentially exposed, but for the time it took to realize it had happened – approximately 2 years(?)! Information is the lifeblood of Analytics. We need it and we need to protect it. Check out these sessions to see what’s new in addressing our Insecurities about our Information.
Presentation given for the SQLPass community at SQLBits XIV in Londen. The presentation is an overview about the performance improvements provided to Hive with the Stinger initiative.
There is a fundamental shift underway in IT to include open, software defined, distributed systems like Hadoop. As a result, every Oracle professional should strive to learn these new technologies or risk being left behind. This session is designed specifically for Oracle database professionals so they can better understand SQL on Hadoop and the benefits it brings to the enterprise. Attendees will see how SQL on Hadoop compares to Oracle in areas such as data storage, data ingestion, and SQL processing. Various live demos will provide attendees with a first-hand look at these new world technologies. Presented at Collaborate 18.
Cloudera's open-source Apache Hadoop distribution, CDH (Cloudera Distribution Including Apache Hadoop), targets enterprise-class deployments of that technology. Cloudera says that more than 50% of its engineering output is donated upstream to the various Apache-licensed open source projects.
https://www.pass4sureexam.com/ccD-410.html
I have studied on Big Data analysis and found Hadoop is the best technology and most popular as well for it's distributed data processing approaches. I have gathered all possible information about various Hadoop distributions available in the market and tried to describe most important tools and their functionality in the Hadoop echosystems in this slide show. I have also tried to discuss about connectivity with language R interm of data analysis and visualization perspective. Hope you will be enjoying the whole!
Big Data and New Challenges for DBAs (Michael Naumov, LivePerson)
Hadoop has become a popular platform for managing large datasets of structured and unstructured data. It does not replace existing infrastructures, but instead augments them. Most companies will still use relational databases for transactional processing and low-latency queries, but can benefit from Hadoop for reporting, machine learning or ETL. This session will cover:
What is Hadoop and why do I care?
What do people do with Hadoop?
How can SQL Server DBAs add Hadoop to their architecture?
Slides for the Usergroup meeting for the Manchester Power BI User Group on June 27th, 2019.
Subject: Power BI for Developers about Power BI Embedded and Power BI Custom Visuals
Presentation giving as part of the Global Azure Bootcamp 2017, April 22, 2017. Subject: one-day hands-on workshop about the Cortana Intelligence Suite.
Presentation given at SQL Saturday Denmark (#541), October 15, 2016 about the Power BI REST API and Power BI Embedded.
Demo's are available at https://github.com/liprec/demos
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.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
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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.
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In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
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Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
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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.
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Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
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Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
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See how to accelerate model training and optimize model performance with active learning
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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
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During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
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Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
JMeter webinar - integration with InfluxDB and Grafana
SQLRally Amsterdam 2013 - Hadoop
1. Henk van der Valk
Technical Sales Professional
Jan Pieter Posthuma
Microsoft BI Consultant
7/11/2013
Hadoop
2. Access to online
training content
JOIN THE PASS
COMMUNITY
Become a PASS member for free
and join the world‟s biggest SQL
Server Community.
Join Local
Chapters
Personalize your PASS website experience
Access to events at
discounted rates
Join Virtual
Chapters
2
4. Introduction Henk
•
•
•
•
•
10 years of Unisys-EMEA Performance Center
2002- Largest SQL DWH in the world (SQL2000)
Project Real – (SQL 2005)
ETL WR - loading 1TB within 30 mins (SQL 2008)
Contributed to various SQL whitepapers
•
•
•
Schuberg Philis-100% uptime for mission critical applications
Since april 1st, 2011 – Microsoft SQL PDW - Western Europe
SQLPass speaker & volunteer since 2005
4
5. Introduction
Alerts, Notifications
SQL Server
StreamInsight
Big Data Sources
(Raw, Unstructured)
Data & Compute Intensive
Application
Business
Insights
SQL Server FTDW Data
Marts
Sensors
Load
SQL Server Reporting
Services
Fast
Devices
Summarize &
Load
HDInsight on
Windows Azure
Bots
HDInsight on
Windows Server
SQL Server Parallel Data
Warehouse
Historical Data
(Beyond Active Window)
Interactive
Reports
Integrate/Enrich
SQL Server Analysis
Server
Crawlers
Performance
Scorecards
Azure Market Place
Enterprise ETL with
SSIS, DQS, MDS
ERP
CRM
LOB
APPS
Source Systems
5
6. Introduction Jan Pieter Posthuma
Jan Pieter Posthuma
• Technical Lead Microsoft BI and
Big Data consultant
• Inter Access, local consultancy firm in the
Netherlands
• Architect role at multiple projects
• Analysis Service, Reporting Service,
PerformancePoint Service, Big Data,
HDInsight, Cloud BI
http://twitter.com/jppp
http://linkedin.com/jpposthuma
jan.pieter.posthuma@interaccess.nl
6
7. Hadoop
• Hadoop is a collection of software to create a data-intensive
distributed cluster running on commodity hardware.
• Original idea by Google (2003).
• Widely accepted by Database vendors as a solution for unstructured
data
• Microsoft partners with HortonWorks and delivers their Hadoop
Data Platform as Microsoft HDInsight
• Available as an Azure service and on premise
• HortonWorks Data Platform (HDP) 100% Open Source!
7
7
8. Hadoop
Map/
Reduce
HBase
HDFS
Poly
base
Avro (Serialization)
Zookeeper
• HDFS – distributed, fault tolerant file system
• MapReduce – framework for writing/executing distributed, fault
tolerant algorithms
• Hive & Pig – SQL-like declarative languages
• Sqoop/PolyBase – package
for moving data between HDFS
BI
ETL
RDBMS
Reporting Tools
and relational DB systems
• + Others…
Hive & Pig
• Hadoop 2.0
Sqoop /
8
10. HDFS
NameNode
HDFS was designed with the
expectation that failures (both
hardware and software) would
occur frequently
BackupNode
namespace backups
Hadoop 2.0 is more decentralized
• Interaction between DataNodes
• Less dependent on primary
NameNode
(heartbeat, balancing, replication, etc.)
DataNode
DataNode
DataNode
DataNode
nodes write to local disk
DataNode
11. Data access to HDFS
FTP – Upload your data files
Streaming – Via AVRO (RPC) or Flume
Hadoop command – hadoop fs -copyFromLocal
Windows Azure BLOB storage – HDInsight Service (Azure) uses BLOB
storage instead of local VM storage. Data can be uploaded without a
provisioned Hadoop cluster
• PolyBase – Feature of PDW 2012. Direct read/write data access to
the datanodes.
•
•
•
•
11
14. Map/Reduce
• MR: all functions in a batch oriented architecture
•
•
Map: Apply the logic to the data, eg page hits count.
Reduce: Reduces (aggregate) the results of the Mappers to one.
• YARN: split the JobTracker in to Resource Manager and Node
Manager. And MR in Hadoop 2.0 uses YARN as its JobTacker
14
16. Hive
•
•
•
•
•
•
•
•
•
Build for easy data retrieval
Uses Map/Reduce
Created by Facebook
HiveQL: SQL like language
Stores data in tables, which are stored as HDFS file(s)
Only initial INSERT supported, no UPDATE or DELETE
External tables possible on existing (CSV) file(s)
Extra language options to use benefits of Hadoop
Stinger initiative: Phase 1 (0.11) and Phase 2 (0.12).
Improve Hive 100x
16
17. Hive
Star schema join – (Based on TPC-DS Query 27)
SELECT
col5, avg(col6)
FROM
store_sales_fact ssf
41 GB
join item_dim on (ssf.col1 = item_dim.col1)
58 MB
join date_dim on (ssf.col2 = date_dim.col2)
11 MB
join custdmgrphcs_dim on (ssf.col3 = custdmgrphcs_dim.col3)
80 MB
join store_dim on (ssf.col4 = store_dim.col4)
106 KB
GROUP BY col5
ORDER BY col5
LIMIT 100;
Cluster: 6 Nodes (2 Name, 4 Compute – dual core, 14GB)
17
18. Hive
File Type
# MR jobs
Input Size
# Mappers
Time
Text / Hive 0.10
5
43.1 GB
179
21:00 min
Text / Hive 0.11
1
38.0 GB
151
4:06 min
RC / Hive 0.11
1
8.21 GB
76
2:16 min
ORC / Hive 0.11
1
2.83 GB
38
1:44 min
RC / Hive 0.11 /
Partitioned /
Bucketed
1
1.73 GB
19
1:44 min
ORC / Hive 0.11 /
Partitioned /
Bucketed
1
687 MB
27
01:19 min
Data: ~64x less data
Time; ~16x times faster
18
19. Data access from Hadoop
Excel
FTP
Hadoop command – hadoop fs -copyToLocal
ODBC[1] – Via Hive (HiveQL) data can be extracted.
Power Query – Is capable of extracting data directly from HDFS or
Azure BLOB storage
• PolyBase – Feature of PDW 2012. Direct read/write data access to
the datanodes.
•
•
•
•
•
[1] http://www.microsoft.com/en-us/download/details.aspx?id=40886
[2] Power BI Excel add-in – http://www.powerbi.com
19
22. PDW – Polybase
…
SQL Server
SQL Server
SQL Server
SQL Server
Sqoop
This is PDW!
DN
DN
DN
DN
DN
DN
DN
DN
DN
DN
DN
DN
Hadoop Cluster
22
22
23. PDW – External Tables
• An external table is PDW‟s representation of data residing in HDFS
• The “table” (metadata) lives in the context of a SQL Server database
• The actual table data resides in HDFS
• No support for DML operations
• No concurrency control or isolation level guarantees
CREATE EXTERNAL TABLE table_name ({<column_definition>} [,...n ])
{WITH (LOCATION =‘<URI>’,[FORMAT_OPTIONS = (<VALUES>)])}
[;]
Required to indicate
location of Hadoop cluster
Optional format options
associated with parsing of data
from HDFS (e.g. field delimiters
& reject-related thresholds)
23
24. PDW – Hadoop use cases & examples
[1] Retrieve data from HDFS with a PDW query
• Seamlessly join structured and semi-structured data
SELECT Username FROM ClickStream c, User u WHERE c.UserID = u.ID
AND c.URL=‘www.bing.com’;
[2] Import data from HDFS to PDW
• Parallelized CREATE TABLE AS SELECT (CTAS)
• External tables as the source
• PDW table, either replicated or distributed, as destination
CREATE TABLE ClickStreamInPDW WITH DISTRIBUTION = HASH(URL)
AS SELECT URL, EventDate, UserID FROM ClickStream;
[3] Export data from PDW to HDFS
• Parallelized CREATE EXTERNAL TABLE AS SELECT (CETAS)
• External table as the destination; creates a set of HDFS files
CREATE EXTERNAL TABLE ClickStream2 (URL, EventDate, UserID)
WITH (LOCATION =‘hdfs://MyHadoop:5000/joe’, FORMAT_OPTIONS (...)
AS SELECT URL, EventDate, UserID FROM ClickStreamInPDW;
26. Wrap up
Hadoop „just another data source‟ @ your fingertips!
Batch processing large datasets before loading into your DWH
Offloading DWH data, but still accessible for analysis/reporting
Integrate Hadoop via SQOOP, ODBC (Hive) or PolyBase
Near future: deeply integration between Hadoop and SQL PDW
Try Hadoop / HDInsight yourself:
Azure: http://www.windowsazure.com/en-us/pricing/free-trial/
Web PI: http://www.microsoft.com/web/downloads/platform.aspx
26
28. References
Microsoft Big Data
http://www.microsoft.com/bigdata
Windows Azure HDInsight Service (3 months free trail)
http://www.windowsazure.com/en-us/services/hdinsight/
SQL Server Parallel Data Warehouse (PDW) Landing Page
http://www.microsoft.com/PDW
http://www.upgradetopdw.com
Introduction to Polybase
http://www.microsoft.com/en-us/sqlserver/solutionstechnologies/data-warehousing/polybase.aspx
28
28
DEMO:Upload a local file with hadoop -copyFromLocal
- Hadoopcommand- CoudXplorer
DEMO: Total hit count W3C logs
- TotalHits MR job
‘Of the 150k jobs Facebook runs daily, only 500 are MapReduce jobs. The rest are is HiveQL’
Hive <0.11:stores data is plain text filesno join optimization.typical DHW query (star schema join) results in 6 MR jobsHive 0.11:introduces (O)RC files, loosely based on column store indexesjoin optimizationTypical DWH query result in 1 MR jobHive 0.12:- Uses Yarn and Tez, optimized for DWH queries and less overhead then MR
DEMO: Retrieving data via ODBC and Power Query in Excel