A session focused on ramping you up on what Hadoop is, how its works and what it's capable of. We will also look at what Hadoop 2.x and YARN brings to the table and some future projects in the Hadoop space to keep an eye on.
Hadoop: Past, Present and Future - v2.1 - SQLSaturday #340Big Data Joe™ Rossi
This document discusses the past, present, and future of Hadoop. It describes how Hadoop 1.0 consisted of HDFS for storage and MapReduce for processing. Hadoop 2.0 introduced YARN to replace MapReduce and allow various processing engines. YARN provides a framework for multiple applications to run on the same Hadoop cluster and access the same data. The future of Hadoop includes SQL interfaces like Hive on Tez/Spark, dynamic HBase clusters on YARN, and machine learning frameworks like REEF.
This document provides an overview of Hadoop past, present and future. It discusses the components of Hadoop 1.x including HDFS and MapReduce. It then covers the new features in Hadoop 2.x including YARN which replaces MapReduce and allows multiple data processing engines. Finally, it outlines the future roadmap of Hadoop including projects to enable interactive query, machine learning, and heterogeneous storage support in HDFS.
YARN - Hadoop Next Generation Compute PlatformBikas Saha
The presentation emphasizes the new mental model of YARN being the cluster OS where one can write and run different applications in Hadoop in a cooperative multi-tenant cluster
Vinod Kumar Vavilapalli presented on Apache Hadoop YARN: Present and Future. He discussed how YARN improved on Hadoop 1 by separating resource management from processing, allowing multiple types of applications on the same platform. He summarized recent Hadoop releases including YARN enhancements like high availability and preemption. Future plans include improved isolation, multi-dimensional scheduling, and supporting long-running services. YARN aims to be a general resource management platform powering a growing ecosystem of applications beyond just MapReduce.
Operating multi-tenant clusters requires careful planning of capacity for on-time launch of big data projects and applications within expected budget and with appropriate SLA guarantees. Making such guarantees with a set of standard hardware configurations is key to operate big data platforms as a hosted service for your organization.
This talk highlights the tools, techniques and methodology applied on a per-project or user basis across three primary multi-tenant deployments in the Apache Hadoop ecosystem, namely MapReduce/YARN and HDFS, HBase, and Storm due to the significance of capital investments with increasing scale in data nodes, region servers, and supervisor nodes respectively. We will demo the estimation tools developed for these deployments that can be used for capital planning and forecasting, and cluster resource and SLA management, including making latency and throughput guarantees to individual users and projects.
As we discuss the tools, we will share considerations that got incorporated to come up with the most appropriate calculation across these three primary deployments. We will discuss the data sources for calculations, resource drivers for different use cases, and how to plan for optimum capacity allocation per project with respect to given standard hardware configurations.
Scale 12 x Efficient Multi-tenant Hadoop 2 Workloads with YarnDavid Kaiser
Hadoop is about so much more than batch processing. With the recent release of Hadoop 2, there have been significant changes to how a Hadoop cluster uses resources. YARN, the new resource management component, allows for a more efficient mix of workloads across hardware resources, and enables new applications and new processing paradigms such as stream-processing. This talk will discuss the new design and components of Hadoop 2, and examples of Modern Data Architectures that leverage Hadoop for maximum business efficiency.
Hadoop: Past, Present and Future - v2.1 - SQLSaturday #340Big Data Joe™ Rossi
This document discusses the past, present, and future of Hadoop. It describes how Hadoop 1.0 consisted of HDFS for storage and MapReduce for processing. Hadoop 2.0 introduced YARN to replace MapReduce and allow various processing engines. YARN provides a framework for multiple applications to run on the same Hadoop cluster and access the same data. The future of Hadoop includes SQL interfaces like Hive on Tez/Spark, dynamic HBase clusters on YARN, and machine learning frameworks like REEF.
This document provides an overview of Hadoop past, present and future. It discusses the components of Hadoop 1.x including HDFS and MapReduce. It then covers the new features in Hadoop 2.x including YARN which replaces MapReduce and allows multiple data processing engines. Finally, it outlines the future roadmap of Hadoop including projects to enable interactive query, machine learning, and heterogeneous storage support in HDFS.
YARN - Hadoop Next Generation Compute PlatformBikas Saha
The presentation emphasizes the new mental model of YARN being the cluster OS where one can write and run different applications in Hadoop in a cooperative multi-tenant cluster
Vinod Kumar Vavilapalli presented on Apache Hadoop YARN: Present and Future. He discussed how YARN improved on Hadoop 1 by separating resource management from processing, allowing multiple types of applications on the same platform. He summarized recent Hadoop releases including YARN enhancements like high availability and preemption. Future plans include improved isolation, multi-dimensional scheduling, and supporting long-running services. YARN aims to be a general resource management platform powering a growing ecosystem of applications beyond just MapReduce.
Operating multi-tenant clusters requires careful planning of capacity for on-time launch of big data projects and applications within expected budget and with appropriate SLA guarantees. Making such guarantees with a set of standard hardware configurations is key to operate big data platforms as a hosted service for your organization.
This talk highlights the tools, techniques and methodology applied on a per-project or user basis across three primary multi-tenant deployments in the Apache Hadoop ecosystem, namely MapReduce/YARN and HDFS, HBase, and Storm due to the significance of capital investments with increasing scale in data nodes, region servers, and supervisor nodes respectively. We will demo the estimation tools developed for these deployments that can be used for capital planning and forecasting, and cluster resource and SLA management, including making latency and throughput guarantees to individual users and projects.
As we discuss the tools, we will share considerations that got incorporated to come up with the most appropriate calculation across these three primary deployments. We will discuss the data sources for calculations, resource drivers for different use cases, and how to plan for optimum capacity allocation per project with respect to given standard hardware configurations.
Scale 12 x Efficient Multi-tenant Hadoop 2 Workloads with YarnDavid Kaiser
Hadoop is about so much more than batch processing. With the recent release of Hadoop 2, there have been significant changes to how a Hadoop cluster uses resources. YARN, the new resource management component, allows for a more efficient mix of workloads across hardware resources, and enables new applications and new processing paradigms such as stream-processing. This talk will discuss the new design and components of Hadoop 2, and examples of Modern Data Architectures that leverage Hadoop for maximum business efficiency.
This document provides a summary of improvements made to Hive's performance through the use of Apache Tez and other optimizations. Some key points include:
- Hive was improved to use Apache Tez as its execution engine instead of MapReduce, reducing latency for interactive queries and improving throughput for batch queries.
- Statistics collection was optimized to gather column-level statistics from ORC file footers, speeding up statistics gathering.
- The cost-based optimizer Optiq was added to Hive, allowing it to choose better execution plans.
- Vectorized query processing, broadcast joins, dynamic partitioning, and other optimizations improved individual query performance by over 100x in some cases.
The document discusses YARN (Yet Another Resource Negotiator), which is the cluster resource management layer of Hadoop. It describes the limitations of the previous Hadoop 1.0 architecture where MapReduce was responsible for both data processing and resource management. YARN was created to address these limitations by separating resource management from data processing. It discusses the components of YARN including the Resource Manager, Node Manager, Containers, and Application Master. It also provides examples of workloads that can run on YARN beyond MapReduce and describes the YARN architecture and how applications run on the YARN framework.
The Future of Hadoop: MapR VP of Product Management, Tomer ShiranMapR Technologies
(1) The amount of data in the world is growing exponentially, with unstructured data making up over 80% of collected data by 2020. (2) Apache Drill provides data agility for Hadoop by enabling self-service data exploration through a flexible data model and schema discovery. (3) Drill allows business users to rapidly query diverse data sources like files, HBase tables, and Hive without requiring IT, through a simple SQL interface.
Apache Hadoop: design and implementation. Lecture in the Big data computing course (http://twiki.di.uniroma1.it/twiki/view/BDC/WebHome), Department of Computer Science, Sapienza University of Rome.
A session focused on ramping you up on what Hadoop is, how its works and what it's capable of. We will also look at what Hadoop 2.x and YARN brings to the table and some future projects in the Hadoop space to keep an eye on.
This document discusses the integration of Apache Pig with Apache Tez. Pig provides a procedural scripting language for data processing workflows, while Tez is a framework for executing directed acyclic graphs (DAGs) of tasks. Migrating Pig to use Tez as its execution engine provides benefits like reduced resource usage, improved performance, and container reuse compared to Pig's default MapReduce execution. The document outlines the design changes needed to compile Pig scripts to Tez DAGs and provides examples and performance results. It also discusses ongoing work to achieve full feature parity with MapReduce and further optimize performance.
Hadoop was originally designed for running large batch jobs, but users wanted to share clusters for better utilization and lower costs. Sharing requires a scheduler that provides guaranteed capacity for production jobs while also giving interactive jobs good response times. The Fair Scheduler was developed to address this by assigning jobs to pools that each get a minimum share of resources, with excess allocated fairly between pools. However, strictly following queues can hurt data locality. Delay Scheduling improves locality by relaxing the queues for a short time to allow more data-local scheduling opportunities.
MapR M7: Providing an enterprise quality Apache HBase APImcsrivas
The document provides an overview of MapR M7, an integrated system for structured and unstructured data. M7 combines aspects of LSM trees and B-trees to provide faster reads and writes compared to Apache HBase. It achieves instant recovery from failures through its use of micro write-ahead logs and parallel region recovery. Benchmark results show MapR M7 providing 5-11x faster performance than HBase for common operations like reads, updates, and scans.
Arun C Murthy, Founder and Architect at Hortonworks Inc., talks about the upcoming Next Generation Apache Hadoop MapReduce framework at the Hadoop Summit, 2011.
- The document discusses Apache Hadoop YARN, including its past, present, and future.
- In the past, YARN started as a sub-project of Hadoop and had several alpha and beta releases before the first stable release in 2013.
- Currently, YARN enables rolling upgrades, long running services, node labels, and improved cluster management features like preemption scheduling and fine-grained resource isolation.
This document introduces MapR and Hadoop. It provides an overview of Hadoop, including how MapReduce works and the Hadoop ecosystem of tools. It explains that MapR is mostly compatible with Hadoop but aims to improve reliability, performance, and management compared to other Hadoop distributions through its architecture and features. The objectives are to explain why Hadoop is important for big data, describe MapReduce jobs, identify Hadoop tools, and compare MapR to other Hadoop distributions.
YARN (Yet Another Resource Negotiator) is a resource management framework for Hadoop clusters that improves on the scalability limitations of the original MapReduce framework. YARN separates resource management from job scheduling to allow multiple data processing engines like MapReduce, Spark, and Storm to share common cluster resources. It introduces a new architecture with a ResourceManager to allocate resources among applications and per-application ApplicationMasters to manage containers and scheduling within an application. This provides improved scalability, utilization, and multi-tenancy for a variety of workloads compared to the original Hadoop architecture.
This document discusses challenges faced with running Hive at large scale at Yahoo. It describes how Yahoo runs Hive on 18 Hadoop clusters with over 400,000 nodes and 580PB of data. Even with optimizations like Tez, ORC, and vectorization, Yahoo encountered slow queries, out of memory errors, and slow partition pruning for queries on tables with millions of partitions. Fixes involved throwing more hardware at the metastore, client-side tuning, and addressing memory leaks and inefficiencies in the metastore and filesystem cache.
Hadoop Summit San Jose 2015: Towards SLA-based Scheduling on YARN Clusters Sumeet Singh
In this talk, we look at YARN scheduler choices available today for Apache Hadoop 2 and discuss their pros and cons. We dive deeper into Capacity Scheduler by providing a comprehensive overview of its various settings with examples from real large-scale Hadoop clusters to promoter a broader understanding of schedulers’ current state and best practices in place today when it comes to queue nomenclature, planning, allocations, and ongoing management. We present detailed cluster, queue, and job behaviors from several different capacity management philosophies.
We then propose practical solutions without any change to the scheduler or core Hadoop that allows managing queue creations and capacity allocations while optimizing for cluster utilization and maintaining SLA guarantees. A unified queue nomenclature, admission and capacity re-allocation policies across BUs, applications, and clusters make service automation possible. Transparency in resources consumed allows for defining realistic SLA expectation. Finally, consistent application tagging completes the feedback loop with SLAs observed through application level reporting.
Hadoop Summit San Jose 2014: Costing Your Big Data Operations Sumeet Singh
As organizations begin to make use of large data sets, approaches to understand and manage true costs of big data will become an important facet with increasing scale of operations.
Whether an on-premise or cloud-based platform is used for storing, processing and analyzing data, our approach explains how to calculate the total cost of ownership (TCO), develop a deeper understanding of compute and storage resources, and run the big data operations with its own P&L, full transparency in costs, and with metering and billing provisions. While our approach is generic, we will illustrate the methodology with three primary deployments in the Apache Hadoop ecosystem, namely MapReduce and HDFS, HBase, and Storm due to the significance of capital investments with increasing scale in data nodes, region servers, and supervisor nodes respectively.
As we discuss our approach, we will share insights gathered from the exercise conducted on one of the largest data infrastructures in the world. We will illustrate how to organize cluster resources, compile data required and typical sources, develop TCO models tailored for individual situations, derive unit costs of usage, measure resources consumed, optimize for higher utilization and ROI, and benchmark the cost.
BIGDATA- Survey on Scheduling Methods in Hadoop MapReduceMahantesh Angadi
The document summarizes a technical seminar presentation on scheduling methods in the Hadoop MapReduce framework. The presentation covers the motivation for Hadoop and MapReduce, provides an introduction to big data and Hadoop, and describes HDFS and the MapReduce programming model. It then discusses challenges in MapReduce scheduling and surveys the literature on existing scheduling methods. The presentation surveys five papers on proposed MapReduce scheduling methods, summarizing the key points of each. It concludes that improving data locality can enhance performance and that future work could consider scheduling algorithms for heterogeneous clusters.
This presentation will give you Information about :
1.Configuring HDFS
2.Interacting With HDFS
3.HDFS Permissions and Security
4.Additional HDFS Tasks
HDFS Overview and Architecture
5.HDFS Installation
6.Hadoop File System Shell
7.File System Java API
Building enterprise advance analytics platformHaoran Du
Raymond Fu gave a presentation on building an enterprise analytics platform at the SoCal Data Science Conference. He has over 16 years of experience in big data, business intelligence, and enterprise architecture. He discussed how big data disrupts traditional architecture and requires new skills. Advanced analytics involves creating predictive models through machine learning to enable strategic and operational decisions. An enterprise analytics strategy involves data management, modernizing data platforms, and operationalizing advanced analytics models. Fu outlined the key capabilities needed for data management, analytics creation, and analytics operationalization. He provided examples of reference architectures and services that can be used to build an enterprise analytics platform.
This document provides materials and tips for preparing for a job interview at Trace-3, including:
1. Sample answers to common interview questions like discussing weaknesses, knowledge of the company, reasons for wanting to work there, how the applicant can contribute, and asking their own questions.
2. Suggestions for researching the company beforehand using their website, LinkedIn, and press releases.
3. Additional interview preparation resources like types of interviews, thank you letters, and common interview questions for different roles.
4. Other general tips on practicing, preparing questions for the employer, and researching job titles.
This document provides a summary of improvements made to Hive's performance through the use of Apache Tez and other optimizations. Some key points include:
- Hive was improved to use Apache Tez as its execution engine instead of MapReduce, reducing latency for interactive queries and improving throughput for batch queries.
- Statistics collection was optimized to gather column-level statistics from ORC file footers, speeding up statistics gathering.
- The cost-based optimizer Optiq was added to Hive, allowing it to choose better execution plans.
- Vectorized query processing, broadcast joins, dynamic partitioning, and other optimizations improved individual query performance by over 100x in some cases.
The document discusses YARN (Yet Another Resource Negotiator), which is the cluster resource management layer of Hadoop. It describes the limitations of the previous Hadoop 1.0 architecture where MapReduce was responsible for both data processing and resource management. YARN was created to address these limitations by separating resource management from data processing. It discusses the components of YARN including the Resource Manager, Node Manager, Containers, and Application Master. It also provides examples of workloads that can run on YARN beyond MapReduce and describes the YARN architecture and how applications run on the YARN framework.
The Future of Hadoop: MapR VP of Product Management, Tomer ShiranMapR Technologies
(1) The amount of data in the world is growing exponentially, with unstructured data making up over 80% of collected data by 2020. (2) Apache Drill provides data agility for Hadoop by enabling self-service data exploration through a flexible data model and schema discovery. (3) Drill allows business users to rapidly query diverse data sources like files, HBase tables, and Hive without requiring IT, through a simple SQL interface.
Apache Hadoop: design and implementation. Lecture in the Big data computing course (http://twiki.di.uniroma1.it/twiki/view/BDC/WebHome), Department of Computer Science, Sapienza University of Rome.
A session focused on ramping you up on what Hadoop is, how its works and what it's capable of. We will also look at what Hadoop 2.x and YARN brings to the table and some future projects in the Hadoop space to keep an eye on.
This document discusses the integration of Apache Pig with Apache Tez. Pig provides a procedural scripting language for data processing workflows, while Tez is a framework for executing directed acyclic graphs (DAGs) of tasks. Migrating Pig to use Tez as its execution engine provides benefits like reduced resource usage, improved performance, and container reuse compared to Pig's default MapReduce execution. The document outlines the design changes needed to compile Pig scripts to Tez DAGs and provides examples and performance results. It also discusses ongoing work to achieve full feature parity with MapReduce and further optimize performance.
Hadoop was originally designed for running large batch jobs, but users wanted to share clusters for better utilization and lower costs. Sharing requires a scheduler that provides guaranteed capacity for production jobs while also giving interactive jobs good response times. The Fair Scheduler was developed to address this by assigning jobs to pools that each get a minimum share of resources, with excess allocated fairly between pools. However, strictly following queues can hurt data locality. Delay Scheduling improves locality by relaxing the queues for a short time to allow more data-local scheduling opportunities.
MapR M7: Providing an enterprise quality Apache HBase APImcsrivas
The document provides an overview of MapR M7, an integrated system for structured and unstructured data. M7 combines aspects of LSM trees and B-trees to provide faster reads and writes compared to Apache HBase. It achieves instant recovery from failures through its use of micro write-ahead logs and parallel region recovery. Benchmark results show MapR M7 providing 5-11x faster performance than HBase for common operations like reads, updates, and scans.
Arun C Murthy, Founder and Architect at Hortonworks Inc., talks about the upcoming Next Generation Apache Hadoop MapReduce framework at the Hadoop Summit, 2011.
- The document discusses Apache Hadoop YARN, including its past, present, and future.
- In the past, YARN started as a sub-project of Hadoop and had several alpha and beta releases before the first stable release in 2013.
- Currently, YARN enables rolling upgrades, long running services, node labels, and improved cluster management features like preemption scheduling and fine-grained resource isolation.
This document introduces MapR and Hadoop. It provides an overview of Hadoop, including how MapReduce works and the Hadoop ecosystem of tools. It explains that MapR is mostly compatible with Hadoop but aims to improve reliability, performance, and management compared to other Hadoop distributions through its architecture and features. The objectives are to explain why Hadoop is important for big data, describe MapReduce jobs, identify Hadoop tools, and compare MapR to other Hadoop distributions.
YARN (Yet Another Resource Negotiator) is a resource management framework for Hadoop clusters that improves on the scalability limitations of the original MapReduce framework. YARN separates resource management from job scheduling to allow multiple data processing engines like MapReduce, Spark, and Storm to share common cluster resources. It introduces a new architecture with a ResourceManager to allocate resources among applications and per-application ApplicationMasters to manage containers and scheduling within an application. This provides improved scalability, utilization, and multi-tenancy for a variety of workloads compared to the original Hadoop architecture.
This document discusses challenges faced with running Hive at large scale at Yahoo. It describes how Yahoo runs Hive on 18 Hadoop clusters with over 400,000 nodes and 580PB of data. Even with optimizations like Tez, ORC, and vectorization, Yahoo encountered slow queries, out of memory errors, and slow partition pruning for queries on tables with millions of partitions. Fixes involved throwing more hardware at the metastore, client-side tuning, and addressing memory leaks and inefficiencies in the metastore and filesystem cache.
Hadoop Summit San Jose 2015: Towards SLA-based Scheduling on YARN Clusters Sumeet Singh
In this talk, we look at YARN scheduler choices available today for Apache Hadoop 2 and discuss their pros and cons. We dive deeper into Capacity Scheduler by providing a comprehensive overview of its various settings with examples from real large-scale Hadoop clusters to promoter a broader understanding of schedulers’ current state and best practices in place today when it comes to queue nomenclature, planning, allocations, and ongoing management. We present detailed cluster, queue, and job behaviors from several different capacity management philosophies.
We then propose practical solutions without any change to the scheduler or core Hadoop that allows managing queue creations and capacity allocations while optimizing for cluster utilization and maintaining SLA guarantees. A unified queue nomenclature, admission and capacity re-allocation policies across BUs, applications, and clusters make service automation possible. Transparency in resources consumed allows for defining realistic SLA expectation. Finally, consistent application tagging completes the feedback loop with SLAs observed through application level reporting.
Hadoop Summit San Jose 2014: Costing Your Big Data Operations Sumeet Singh
As organizations begin to make use of large data sets, approaches to understand and manage true costs of big data will become an important facet with increasing scale of operations.
Whether an on-premise or cloud-based platform is used for storing, processing and analyzing data, our approach explains how to calculate the total cost of ownership (TCO), develop a deeper understanding of compute and storage resources, and run the big data operations with its own P&L, full transparency in costs, and with metering and billing provisions. While our approach is generic, we will illustrate the methodology with three primary deployments in the Apache Hadoop ecosystem, namely MapReduce and HDFS, HBase, and Storm due to the significance of capital investments with increasing scale in data nodes, region servers, and supervisor nodes respectively.
As we discuss our approach, we will share insights gathered from the exercise conducted on one of the largest data infrastructures in the world. We will illustrate how to organize cluster resources, compile data required and typical sources, develop TCO models tailored for individual situations, derive unit costs of usage, measure resources consumed, optimize for higher utilization and ROI, and benchmark the cost.
BIGDATA- Survey on Scheduling Methods in Hadoop MapReduceMahantesh Angadi
The document summarizes a technical seminar presentation on scheduling methods in the Hadoop MapReduce framework. The presentation covers the motivation for Hadoop and MapReduce, provides an introduction to big data and Hadoop, and describes HDFS and the MapReduce programming model. It then discusses challenges in MapReduce scheduling and surveys the literature on existing scheduling methods. The presentation surveys five papers on proposed MapReduce scheduling methods, summarizing the key points of each. It concludes that improving data locality can enhance performance and that future work could consider scheduling algorithms for heterogeneous clusters.
This presentation will give you Information about :
1.Configuring HDFS
2.Interacting With HDFS
3.HDFS Permissions and Security
4.Additional HDFS Tasks
HDFS Overview and Architecture
5.HDFS Installation
6.Hadoop File System Shell
7.File System Java API
Building enterprise advance analytics platformHaoran Du
Raymond Fu gave a presentation on building an enterprise analytics platform at the SoCal Data Science Conference. He has over 16 years of experience in big data, business intelligence, and enterprise architecture. He discussed how big data disrupts traditional architecture and requires new skills. Advanced analytics involves creating predictive models through machine learning to enable strategic and operational decisions. An enterprise analytics strategy involves data management, modernizing data platforms, and operationalizing advanced analytics models. Fu outlined the key capabilities needed for data management, analytics creation, and analytics operationalization. He provided examples of reference architectures and services that can be used to build an enterprise analytics platform.
This document provides materials and tips for preparing for a job interview at Trace-3, including:
1. Sample answers to common interview questions like discussing weaknesses, knowledge of the company, reasons for wanting to work there, how the applicant can contribute, and asking their own questions.
2. Suggestions for researching the company beforehand using their website, LinkedIn, and press releases.
3. Additional interview preparation resources like types of interviews, thank you letters, and common interview questions for different roles.
4. Other general tips on practicing, preparing questions for the employer, and researching job titles.
Driving Retail Success with Machine Data IntelligenceSumo Logic
Gain a competitive edge this holiday season by harnessing the power of machine data. Watch the on-demand webinar to learn how the out-of-the-box integration between Sumo Logic and Akamai allows organizations to:
• Gain a competitive edge by identifying purchasing trends in real-time
• Improve service by correlating Akamai data sets for reduced errors and downtime
• Strengthen security posture through compliance and web application firewall (WAF) monitoring
• Elastically scale to meet unforeseen or projected spikes in business
• Streamline order management, store performance and loss prevention
See the integration in action.
The document outlines eight disciplines of enterprise modernization: total service orientation, innate entrepreneurship, business ecology, on demand enterprise architecture, centers of excellence, continuous improvement, sustainability, and tenacious leadership. It discusses each discipline in 1-2 paragraphs and provides examples. The overall document promotes an approach to continuously improving and innovating the enterprise in order to survive in the new economy.
This document provides an overview and agenda for Week 6 of the DSE 400 course. It outlines discussions to be held on social media platforms, recommended learning materials to review like videos and articles on Hadoop and Hive, and hands-on activities like installing Hadoop and querying datasets. The assignment requires students to perform queries on a NYSE stocks dataset using Hive or R, and submit the queries and results as a PDF. Mentoring and help resources are also listed.
DSE 400 is a free online course that provides an introduction to data science over 8 weeks. Week 1 focuses on getting started with R and RStudio, reading introductory materials, and importing and displaying the Housing dataset from UCI. Participants are asked to engage in online discussions, work on collaborative presentations, and complete assignments like importing the Housing dataset into R. Upcoming weeks will cover topics like statistics, machine learning, Hadoop, visualizations and building data products.
This document provides an overview and roadmap for the DSE 400 - Fast Track to Data Science course. The week 1 agenda includes introductions, reading assignments on data science topics, installing R and RStudio, practicing with math and machine learning datasets, and an assignment to import and display the Housing dataset from UCI Machine Learning Repository in R. The course aims to provide an introduction to data science, analytics, and visualization over 8 weeks covering topics like statistics, machine learning, Hadoop, ethics, and building data products.
A document outlines plans to build a Center of Excellence for Big Data Analytics. It will provide expertise, manage governance practices, and support analytics projects. The COE will maximize quality and efficiency of analytics across business lines. It will focus on business strategy alignment, best practices, advice, community services, communication, technical architecture, support, education, and governance alignment. Keys to success include having a clear strategy, demonstrating value, engaging people, establishing processes, and selecting the right technologies.
This document provides an overview and agenda for Week 8 of the Data Scientist Enablement (DSE) 400 program. It outlines the week's discussions on ethics in big data, recommended learning materials, activities including exploring datasets and starting a blog, and an assignment to cleanse and visualize a sensor dataset or complete an alternative task. The timeline for the full DSE program and options for adaptive learning and proficiency certification are also summarized.
Dr Mohan K Bavirisetty - 8 Disciplines of Enterprise Modernization - FinalDr. Mohan K. Bavirisetty
The document summarizes the 8 disciplines of enterprise modernization according to Dr. Mohan K. Bavirisetty: 1) total service orientation, 2) innate entrepreneurship, 3) business ecology, 4) continuous improvement, 5) enterprise architecture on demand, 6) thought leadership through centers of excellence, 7) sustainability, and 8) tenacious leadership. The document provides details on each discipline and examples of organizations that have successfully implemented aspects of enterprise modernization.
This document provides an overview of advanced analytics frameworks, platforms, and methodologies. It begins with introducing advanced analytics and defining it. It then discusses various frameworks, platforms from companies like IBM, AeroSpike, and BlueMix. It also covers methodologies for analytics like CRISP-DM, SEMMA, and SMAM. The document references several Gartner reports and ends with taking questions.
Week 7 of the Data Scientist Enablement program focuses on advanced topics including MapReduce, Lambda Architecture, and Google BigQuery. Participants are instructed to continue tutorials on Hortonworks and explore Google public datasets. The assignment involves performing queries on a baseball statistics dataset using Hadoop, R, or BigQuery to analyze maximum and average runs by year and identify top players. Participants can earn a proficiency certificate based on their overall performance and mastery of concepts across the four module program.
Dr Mohan K Bavirisetty - 8 Disciplines of Enterprise Modernization - FinalDr. Mohan K. Bavirisetty
The document summarizes the 8 disciplines of enterprise modernization according to Dr. Mohan K. Bavirisetty: 1) total service orientation, 2) innate entrepreneurship, 3) business ecology, 4) continuous improvement, 5) enterprise architecture on demand, 6) thought leadership through centers of excellence, 7) sustainability, and 8) tenacious leadership. The document provides details on each discipline and examples of organizations that have successfully implemented aspects of enterprise modernization.
This document outlines a data science enablement roadmap created by the Advanced Center of Excellence at Modern Renaissance Corporation. The roadmap consists of 1 introductory course and 3 advanced courses that can earn a student a master's level certificate in data science. The introductory course provides a broad overview of topics like algorithms, statistics, machine learning, and big data platforms. The advanced courses focus on specific skills like machine learning with R, modern data platforms using Hadoop, and advanced big data analytics techniques. The goal is to give students a versatile, practical skill set for a career in data science or big data engineering.
This document provides an introduction to polyglot processing using various big data frameworks. It discusses the lambda and kappa architectures for handling batch, micro-batch, and streaming workloads. The document then demonstrates Apache Spark, Storm, Kafka and Redis for stream processing and compares these tools to Flink. It concludes that polyglot processing allows for any data type or workload to be handled and that frameworks like Spark, Storm and Flink each have strengths for distributed, real-time computation.
Business Analytics Competency centre: A strategic Differentiator BSGAfrica
The document discusses establishing a business analytics competency center (BACC) to help organizations better utilize analytics. It notes that effective analytics requires more than just technology and emphasizes the importance of aligning business and IT perspectives. A BACC can serve as a central hub to develop analytics infrastructure, promote collaboration, and ensure analytics efforts are in line with business priorities. The goal of a BACC is to facilitate a strategic, enterprise-wide approach to analytics through joint ownership between business and IT.
The document discusses a Business Intelligence Competence Center (BICC) and its role in business analytics strategies. It notes that few organizations currently have a BICC. A BICC is a cross-functional team that supports and promotes effective BI use across an organization. It outlines the typical tasks of a BICC, including developing a corporate BI strategy and data management strategy, as well as providing education and support. The document also discusses BICC organization, skills, risks, and the impacts of big data and mobile BI. It provides an example of how USG People implements a BICC within its extended organization.
Institute H: The Road to Becoming a Center of Excellence
Thursday, October 8, 9:00 am - 12:00 p.m., Executive C D
Lisa D'Adamo-Weinstein, Director, Academic Support
Northeast Center of SUNY Empire State College
Elaine Richardson, Retired Director, Academic Success Center
Clemson University
Laura Sanders, Assistant Dean, Student Success, College of Engineering
Valparaiso University
The purpose of the Centers of Excellence Designation Program is to:
promote professional standards of excellence for learning centers;
encourage centers to develop, maintain and assess quality programs and services to enhance student learning;
honor the history of established and unique learning centers; and
celebrate the outstanding achievements of centers that meet and exceed these standards.
This post-conference institute will walk participants through the rationale for the creation of the designation program;
review the criteria for evaluation and discuss the steps for completing an application. We will also share insights
gathered during the first two rounds of applications reviews to assist participants in developing a clear plan for how
they can best put together their own application
This document provides an overview of Hadoop versions 1.x and 2.x. Hadoop 1.x included HDFS for storage and MapReduce for processing. It had limitations around scalability, availability, and resources. Hadoop 2.x introduced YARN to replace MapReduce and address its limitations. YARN provides a framework for multiple data processing models and improved cluster utilization. It allows multiple applications like streaming, interactive query, and graph processing to run on the same Hadoop cluster.
2013 Nov 20 Toronto Hadoop User Group (THUG) - Hadoop 2.2.0Adam Muise
The document discusses Hadoop 2.2.0 and new features in YARN and MapReduce. Key points include: YARN introduces a new application framework and resource management system that replaces the jobtracker, allowing multiple data processing engines besides MapReduce; MapReduce is now a library that runs on YARN; Tez is introduced as a new data processing framework to improve performance beyond MapReduce.
Bikas saha:the next generation of hadoop– hadoop 2 and yarnhdhappy001
The document discusses Apache YARN, which is the next-generation resource management platform for Apache Hadoop. YARN was designed to address limitations of the original Hadoop 1 architecture by supporting multiple data processing models (e.g. batch, interactive, streaming) and improving cluster utilization. YARN achieves this by separating resource management from application execution, allowing various data processing engines like MapReduce, HBase and Storm to run natively on Hadoop frames. This provides a flexible, efficient and shared platform for distributed applications.
Apache Hadoop YARN: Understanding the Data Operating System of HadoopHortonworks
This deck covers concepts and motivations behind Apache Hadoop YARN, the key technology in Hadoop 2 to deliver a Data Operating System for the enterprise.
Combine SAS High-Performance Capabilities with Hadoop YARNHortonworks
The document discusses combining SAS capabilities with Hadoop YARN. It provides an introduction to YARN and how it allows SAS workloads to run on Hadoop clusters alongside other workloads. The document also discusses resource settings for SAS workloads on YARN and upcoming features for YARN like delegated containers and Kubernetes integration.
YARN (Yet Another Resource Negotiator) improves on MapReduce by separating cluster resource management from job scheduling and tracking. It introduces the ResourceManager for global resource management and per-application ApplicationMasters to manage individual applications. This provides improved scalability, availability, and allows various data processing frameworks beyond MapReduce to operate on shared Hadoop clusters. Key components of YARN include the ResourceManager, NodeManagers, ApplicationMasters and Containers as the basic unit of resource allocation. MRv2 uses a generalized architecture and APIs to provide benefits like rolling upgrades, multi-tenant clusters, and higher resource utilization.
MapR is a distribution of Apache Hadoop that includes over a dozen projects like HBase, Hive, Pig, and Spark. It provides capabilities for big data and constantly upgrades projects within 90 days of release. MapR also contributes to open source. Key benefits include high availability without special configurations, superior performance reducing costs, and data protection through snapshots. It also supports real-time applications, security, multi-tenancy, and assistance from MapR data scientists and engineers.
The job throughput and Apache Hadoop cluster utilization benefits of YARN and MapReduce v2 are widely known. Who wouldn’t want job throughput increased by 2x? Most likely you’ve heard (repeatedly) about the key benefits that could be gained from migrating your Hadoop cluster from MapReduce v1 to YARN: namely around improved job throughput and cluster utilization, as well as around permitting different computational frameworks to run on Hadoop. What you probably haven’t heard about are the configuration tweaks needed to ensure your existing MR v1 jobs can run on your YARN cluster as well as YARN specific configuration settings. In this session we’ll start with a list of recommended YARN configurations, and then step through the most common use-cases we’ve seen in the field. Production migrations can quickly go awry without proper guidance. Learn from others’ misconfigurations to get your YARN cluster configured right the first time.
Big Data Everywhere Chicago: Getting Real with the MapR Platform (MapR)BigDataEverywhere
Jim Scott, Director of Enterprise Strategy, MapR; Cofounder, CHUG
In this talk, we will take a look back at the short history of Hadoop, along with the trials and tribulation that have come along with this ground-breaking technology. We will explore the reasons why enterprises need to look deeper into their wants and needs and further into the future to prepare for where they are going.
Talk held at a combined meeting of the Web Performance Karlsruhe (http://www.meetup.com/Karlsruhe-Web-Performance-Group/events/153207062) & Big Data Karlsruhe/Stuttgart (http://www.meetup.com/Big-Data-User-Group-Karlsruhe-Stuttgart/events/162836152) user groups.
Agenda:
- Why Hadoop 2?
- HDFS 2
- YARN
- YARN Apps
- Write your own YARN App
- Tez, Hive & Stinger Initiative
Tez: Accelerating Data Pipelines - fifthelt3rmin4t0r
This document provides an overview of Tez, an Apache project that provides a framework for executing data processing jobs on Hadoop clusters. Tez allows expressing data processing jobs as directed acyclic graphs (DAGs) of tasks and executes these tasks in a optimized manner. It addresses limitations of MapReduce by providing a more flexible execution engine that can optimize performance and resource utilization.
Developing YARN Applications - Integrating natively to YARN July 24 2014Hortonworks
This document provides an overview of developing applications for YARN, the resource management framework in Hadoop 2.0. It describes YARN concepts like containers and the ApplicationMaster, the APIs used to develop YARN applications, and walks through building a simple distributed shell application. It also discusses the Application Timeline Server for application metrics and monitoring.
YARN (Yet Another Resource Negotiator) is a distributed operating system for large scale data processing. It improves on MapReduce by allowing multiple data processing engines and frameworks to share common distributed compute resources and data storage on large Hadoop clusters. YARN introduces a resource management layer separate from job scheduling and processing logic. This allows Hadoop to support diverse workloads including batch processing, interactive queries, real-time streams and more. YARN also enables multi-tenant clusters to share resources among multiple users and applications in a secure manner through queues and containers.
Introduction sur Tez par Olivier RENAULT de HortonWorks Meetup du 25/11/2014Modern Data Stack France
During this presentation, Olivier will introduce Apache Tez. What it does ? Why is it seen by many as the Map Reduce v2. How is it helping Hive / Pig / Cascading and other increase their performance.
Speaker: Olivier Renault is a Principal Solution Engineer at Hortonworks the company behind Hortonworks Data Platform. Olivier is an expert on how to deploy Hadoop at scale in a secure and performant manner.
YARN is a resource management framework for Hadoop that allows multiple data processing engines such as MapReduce, Spark, and Storm to run on the same cluster. It introduces a global ResourceManager and per-node NodeManagers to allocate and manage resources across applications. YARN supports multi-tenant clusters with queues that provide resource guarantees and isolation between users and workloads. A demo showed preemption and multi-tenant queues handling different workloads hitting the cluster.
This talk takes you on a rollercoaster ride through Hadoop 2 and explains the most significant changes and components.
The talk has been held on the JavaLand conference in Brühl, Germany on 25.03.2014.
Agenda:
- Welcome Office
- YARN Land
- HDFS 2 Land
- YARN App Land
- Enterprise Land
Pivotal: Hadoop for Powerful Processing of Unstructured Data for Valuable Ins...EMC
Pivotal has setup and operationalized 1000 node Hadoop cluster called the Analytics Workbench. It takes special setup and skills to manage such a large deployment. This session shares how we set it up and how you will manage it.
Objective 1: Understand what it takes to operationalize a 1000-nodeHadoop cluster.
After this session you will be able to:
Objective 2: Understand how to set up and manage the day to day challenges of a large Hadoop deployments.
Objective 3: Have a view to the tools that are necessary to solve the challenges of managing the large Hadoop cluster.
YARN - Presented At Dallas Hadoop User GroupRommel Garcia
This document provides an overview of YARN (Yet Another Resource Negotiator) in Hadoop 2.0. It discusses:
1) How YARN improves on Hadoop 1.X by allowing multiple applications to share cluster resources and enabling new types of applications beyond just MapReduce. YARN serves as the cluster resource manager.
2) Key YARN concepts like applications, containers, the resource manager, node manager, and application master. Containers are the basic unit of allocation that replace static map and reduce slots.
3) How MapReduce runs on YARN by using an application master and negotiating containers from the resource manager, rather than being tied to static slots. This improves efficiency.
YARN - Next Generation Compute Platform fo HadoopHortonworks
YARN was developed as part of Hadoop 2.0 to address limitations in the original Hadoop 1.0 architecture. YARN introduces a centralized resource management framework to allow multiple data processing engines like MapReduce, interactive queries, graph processing, and stream processing to efficiently share common Hadoop cluster resources. It also improves cluster utilization, scalability, and supports multiple paradigms beyond just batch processing. Major companies like Yahoo have realized significant performance and resource utilization gains with YARN in production environments.
Similar to Hadoop - Past, Present and Future - v2.0 (20)
The document discusses consistent hashing and how it allows for efficient data distribution and load balancing across nodes in a distributed system. It describes the consistent hashing algorithm, which maps data items to nodes on a ring. When a node is added or removed, only nearby items need to be remapped, allowing other items and nodes to remain undisturbed. The algorithm facilitates smooth handoffs of data items between nodes to maintain balanced storage.
The document provides an overview of IBM's BigInsights product. It discusses how BigInsights can help businesses gain insights from large, complex datasets through features like built-in text analytics, SQL support, spreadsheet-style analysis, and accelerators for domain-specific analytics like social media. The document also summarizes capabilities of BigInsights like Big SQL, Big Sheets, Big R, and its text analytics engine that allow businesses to explore, analyze, and model large datasets.
This document discusses WANdisco's Non-Stop Hadoop solution, which provides continuous availability of Hadoop across local and wide area networks using an active-active replication technique. It addresses key problems with multi-cluster Hadoop deployments like lack of 100% uptime and challenges sharing data globally. The solution utilizes WANdisco's patented distributed coordination engine to achieve consensus across data centers for metadata operations and absolute consistency. Use cases highlighted include eliminating single point of failures, enabling parallel data ingest across locations, optimizing resource utilization through cluster zoning, and achieving near-zero RTO disaster recovery.
The document provides an overview of IBM's BigInsights product. It discusses how BigInsights can help businesses gain insights from large, complex datasets through features like built-in text analytics, SQL support, spreadsheet-style analysis, and accelerators for domain-specific analytics like social media. The document also summarizes capabilities of BigInsights like Big SQL, Big Sheets, Big R, and its embedded text analytics engine.
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
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This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
State of Artificial intelligence Report 2023kuntobimo2016
Artificial intelligence (AI) is a multidisciplinary field of science and engineering whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen in the next 12 months and a 2022 performance review to keep us honest.
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.