Hadoop 2.0 is approaching. A defining characteristic of Hadoop 2.0 is its next generation resource management framework called YARN. YARN enables Hadoop to grow beyond its MapReduce origins to embrace multiple workloads spanning interactive queries, batch processing, streaming & more.
Hadoop YARN is the next generation computing platform in Apache Hadoop with support for programming paradigms besides MapReduce. In the world of Big Data, one cannot solve all the problems wholly using the Map Reduce programming model. Typical installations run separate programming models like MR, MPI, graph-processing frameworks on individual clusters. Running fewer larger clusters is cheaper than running more small clusters. Therefore,_leveraging YARN to allow both MR and non-MR applications to run on top of a common cluster becomes more important from an economical and operational point of view. This talk will cover the different APIs and RPC protocols that are available for developers to implement new application frameworks on top of YARN. We will also go through a simple application which demonstrates how one can implement their own Application Master, schedule requests to the YARN resource-manager and then subsequently use the allocated resources to run user code on the NodeManagers.
Hortonworks Get Started Building YARN Applications Dec. 2013. We cover YARN basics, benefits, getting started and roadmap. Actian shares their experience and recommendations on building their real-world YARN application.
Running Non-MapReduce Big Data Applications on Apache Hadoophitesh1892
Apache Hadoop has become popular from its specialization in the execution of MapReduce programs. However, it has been hard to leverage existing Hadoop infrastructure for various other processing paradigms such as real-time streaming, graph processing and message-passing. That was true until the introduction of Apache Hadoop YARN in Apache Hadoop 2.0. YARN supports running arbitrary processing paradigms on the same Hadoop cluster. This allows for development of newer frameworks as well as more efficient implementations of existing frameworks that can all run on and share the resources of a single multi-tenant YARN cluster. This talk gives a brief introduction to YARN. We will illustrate how to create applications and how to best make use of YARN. We will show examples of different applications such as Apache Tez and Apache Samza that can leverage YARN and present best practices/guidelines on building applications on top of Apache Hadoop YARN.
Hadoop YARN is the next generation computing platform in Apache Hadoop with support for programming paradigms besides MapReduce. In the world of Big Data, one cannot solve all the problems wholly using the Map Reduce programming model. Typical installations run separate programming models like MR, MPI, graph-processing frameworks on individual clusters. Running fewer larger clusters is cheaper than running more small clusters. Therefore,_leveraging YARN to allow both MR and non-MR applications to run on top of a common cluster becomes more important from an economical and operational point of view. This talk will cover the different APIs and RPC protocols that are available for developers to implement new application frameworks on top of YARN. We will also go through a simple application which demonstrates how one can implement their own Application Master, schedule requests to the YARN resource-manager and then subsequently use the allocated resources to run user code on the NodeManagers.
Hortonworks Get Started Building YARN Applications Dec. 2013. We cover YARN basics, benefits, getting started and roadmap. Actian shares their experience and recommendations on building their real-world YARN application.
Running Non-MapReduce Big Data Applications on Apache Hadoophitesh1892
Apache Hadoop has become popular from its specialization in the execution of MapReduce programs. However, it has been hard to leverage existing Hadoop infrastructure for various other processing paradigms such as real-time streaming, graph processing and message-passing. That was true until the introduction of Apache Hadoop YARN in Apache Hadoop 2.0. YARN supports running arbitrary processing paradigms on the same Hadoop cluster. This allows for development of newer frameworks as well as more efficient implementations of existing frameworks that can all run on and share the resources of a single multi-tenant YARN cluster. This talk gives a brief introduction to YARN. We will illustrate how to create applications and how to best make use of YARN. We will show examples of different applications such as Apache Tez and Apache Samza that can leverage YARN and present best practices/guidelines on building applications on top of Apache Hadoop YARN.
Hadoop Summit Europe Talk 2014: Apache Hadoop YARN: Present and FutureVinod Kumar Vavilapalli
Title: Apache Hadoop YARN: Present and Future
Abstract: Apache Hadoop YARN evolves the Hadoop compute platform from being centered only around MapReduce to being a generic data processing platform that can take advantage of a multitude of programming paradigms all on the same data. In this talk, we'll talk about the journey of YARN from a concept to being the cornerstone of Hadoop 2 GA releases. We'll cover the current status of YARN, how it is faring today and how it stands apart from the monochromatic world that is Hadoop 1.0. We`ll then move on to the exciting future of YARN - features that are making YARN a first class resource-management platform for enterprise Hadoop, rolling upgrades, high availability, support for long running services alongside applications, fine-grain isolation for multi-tenancy, preemption, application SLAs, application-history to name a few.
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
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.
Apache Ambari: Managing Hadoop and YARNHortonworks
Part of the Hortonworks YARN Ready Webinar Series, this session is about management of Apache Hadoop and YARN using Apache Ambari. This series targets developers and we will feature a demo on Ambari.
Hortonworks technical workshop operations with ambariHortonworks
Ambari continues on its journey of provisioning, monitoring and managing enterprise Hadoop deployments. With 2.0, Apache Ambari brings a host of new capabilities including updated metric collections; Kerberos setup automation and developer views for Big Data developers. In this Hortonworks Technical Workshop session we will provide an in-depth look into Apache Ambari 2.0 and showcase security setup automation using Ambari 2.0. View the recording at https://www.brighttalk.com/webcast/9573/155575. View the github demo work at https://github.com/abajwa-hw/ambari-workshops/blob/master/blueprints-demo-security.md. Recorded May 28, 2015.
Discover HDP2.1: Apache Storm for Stream Data Processing in HadoopHortonworks
For the first time, Hortonworks Data Platform ships with Apache Storm for processing stream data in Hadoop.
In this presentation, Himanshu Bari, Hortonworks senior product manager, and Taylor Goetz, Hortonworks engineer and committer to Apache Storm, cover Storm and stream processing in HDP 2.1:
+ Key requirements of a streaming solution and common use cases
+ An overview of Apache Storm
+ Q & A
Hortonworks tech workshop in-memory processing with sparkHortonworks
Apache Spark offers unique in-memory capabilities and is well suited to a wide variety of data processing workloads including machine learning and micro-batch processing. With HDP 2.2, Apache Spark is a fully supported component of the Hortonworks Data Platform. In this session we will cover the key fundamentals of Apache Spark and operational best practices for executing Spark jobs along with the rest of Big Data workloads. We will also provide a working example to showcase micro-batch and machine learning processing using Apache Spark.
Hortonworks Technical Workshop: What's New in HDP 2.3Hortonworks
The recently launched HDP 2.3 is a major advancement of Open Enterprise Hadoop. It represents the best of community led development with innovations spanning Apache Hadoop, Apache Ambari, Ranger, HBase, Spark and Storm. In this session we will provide an in-depth overview of new functionality and discuss it's impact on new and ongoing big data initiatives.
Pig has added some exciting new features in 0.10, including a boolean type, UDFs in JRuby, load and store functions for JSON, bloom filters, and performance improvements. Join Alan Gates, Hortonworks co-founder and long-time contributor to the Apache Pig and HCatalog projects, to discuss these new features, as well as talk about work the project is planning to do in the near future. In particular, we will cover how Pig can take advantage of changes in Hadoop 0.23.
This presentation about Hadoop YARN will help you understand the Hadoop 1.0 and Hadoop 2.0, limitations of Hadoop 1.0, need for YARN, what is YARN, workloads running on YARN, YARN components, YARN architecture and you will also go through a demo on YARN. YARN is the cluster resource management layer of the Apache Hadoop Ecosystem, which schedules jobs and assigns resources. Hadoop 1.0 is designed to run MapReduce jobs only and had issues in scalability, resource utilization, etc. whereas YARN solved those issues and users could work on multiple processing models. Now let us get started and learn YARN in detail.
Below topics are explained in this Hadoop YARN presentation:
1. Hadoop 1.0 (MapReduce 1)
2. Limitations of Hadoop 1.0 (MapReduce 1)
3. Need for YARN
4. What is YARN
5. Workloads running on YARN
6. YARN components
7. YARN architecture
8. Demo on YARN
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
One of the key differences between Presto and Hive, also a crucial functional requirement Facebook made when launching this new SQL engine project, was to have the opportunity to query different kinds of data sources via a uniform ANSI SQL interface.
Presto, an open source distributed analytical SQL engine, implements this with it’s connector architecture, creating an abstraction layer for anything that can be expressed as in a row-like format, ranging from MySQL tables, HDFS, Amazon S3 to NoSQL stores, Kafka streams and proprietary data sources. Presto connector SPI allows anyone to implement a Presto connector and benefit from the capabilities of the Presto SQL engine, enabling them to join data from various sources within a single SQL query.
Hadoop Summit Europe Talk 2014: Apache Hadoop YARN: Present and FutureVinod Kumar Vavilapalli
Title: Apache Hadoop YARN: Present and Future
Abstract: Apache Hadoop YARN evolves the Hadoop compute platform from being centered only around MapReduce to being a generic data processing platform that can take advantage of a multitude of programming paradigms all on the same data. In this talk, we'll talk about the journey of YARN from a concept to being the cornerstone of Hadoop 2 GA releases. We'll cover the current status of YARN, how it is faring today and how it stands apart from the monochromatic world that is Hadoop 1.0. We`ll then move on to the exciting future of YARN - features that are making YARN a first class resource-management platform for enterprise Hadoop, rolling upgrades, high availability, support for long running services alongside applications, fine-grain isolation for multi-tenancy, preemption, application SLAs, application-history to name a few.
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
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.
Apache Ambari: Managing Hadoop and YARNHortonworks
Part of the Hortonworks YARN Ready Webinar Series, this session is about management of Apache Hadoop and YARN using Apache Ambari. This series targets developers and we will feature a demo on Ambari.
Hortonworks technical workshop operations with ambariHortonworks
Ambari continues on its journey of provisioning, monitoring and managing enterprise Hadoop deployments. With 2.0, Apache Ambari brings a host of new capabilities including updated metric collections; Kerberos setup automation and developer views for Big Data developers. In this Hortonworks Technical Workshop session we will provide an in-depth look into Apache Ambari 2.0 and showcase security setup automation using Ambari 2.0. View the recording at https://www.brighttalk.com/webcast/9573/155575. View the github demo work at https://github.com/abajwa-hw/ambari-workshops/blob/master/blueprints-demo-security.md. Recorded May 28, 2015.
Discover HDP2.1: Apache Storm for Stream Data Processing in HadoopHortonworks
For the first time, Hortonworks Data Platform ships with Apache Storm for processing stream data in Hadoop.
In this presentation, Himanshu Bari, Hortonworks senior product manager, and Taylor Goetz, Hortonworks engineer and committer to Apache Storm, cover Storm and stream processing in HDP 2.1:
+ Key requirements of a streaming solution and common use cases
+ An overview of Apache Storm
+ Q & A
Hortonworks tech workshop in-memory processing with sparkHortonworks
Apache Spark offers unique in-memory capabilities and is well suited to a wide variety of data processing workloads including machine learning and micro-batch processing. With HDP 2.2, Apache Spark is a fully supported component of the Hortonworks Data Platform. In this session we will cover the key fundamentals of Apache Spark and operational best practices for executing Spark jobs along with the rest of Big Data workloads. We will also provide a working example to showcase micro-batch and machine learning processing using Apache Spark.
Hortonworks Technical Workshop: What's New in HDP 2.3Hortonworks
The recently launched HDP 2.3 is a major advancement of Open Enterprise Hadoop. It represents the best of community led development with innovations spanning Apache Hadoop, Apache Ambari, Ranger, HBase, Spark and Storm. In this session we will provide an in-depth overview of new functionality and discuss it's impact on new and ongoing big data initiatives.
Pig has added some exciting new features in 0.10, including a boolean type, UDFs in JRuby, load and store functions for JSON, bloom filters, and performance improvements. Join Alan Gates, Hortonworks co-founder and long-time contributor to the Apache Pig and HCatalog projects, to discuss these new features, as well as talk about work the project is planning to do in the near future. In particular, we will cover how Pig can take advantage of changes in Hadoop 0.23.
This presentation about Hadoop YARN will help you understand the Hadoop 1.0 and Hadoop 2.0, limitations of Hadoop 1.0, need for YARN, what is YARN, workloads running on YARN, YARN components, YARN architecture and you will also go through a demo on YARN. YARN is the cluster resource management layer of the Apache Hadoop Ecosystem, which schedules jobs and assigns resources. Hadoop 1.0 is designed to run MapReduce jobs only and had issues in scalability, resource utilization, etc. whereas YARN solved those issues and users could work on multiple processing models. Now let us get started and learn YARN in detail.
Below topics are explained in this Hadoop YARN presentation:
1. Hadoop 1.0 (MapReduce 1)
2. Limitations of Hadoop 1.0 (MapReduce 1)
3. Need for YARN
4. What is YARN
5. Workloads running on YARN
6. YARN components
7. YARN architecture
8. Demo on YARN
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
One of the key differences between Presto and Hive, also a crucial functional requirement Facebook made when launching this new SQL engine project, was to have the opportunity to query different kinds of data sources via a uniform ANSI SQL interface.
Presto, an open source distributed analytical SQL engine, implements this with it’s connector architecture, creating an abstraction layer for anything that can be expressed as in a row-like format, ranging from MySQL tables, HDFS, Amazon S3 to NoSQL stores, Kafka streams and proprietary data sources. Presto connector SPI allows anyone to implement a Presto connector and benefit from the capabilities of the Presto SQL engine, enabling them to join data from various sources within a single SQL query.
A Comparative Performance Evaluation of Apache FlinkDongwon Kim
I compare Apache Flink to Apache Spark, Apache Tez, and MapReduce in Apache Hadoop in terms of performance. I run experiments using two benchmarks, Terasort and Hashjoin.
Presto, an open source distributed SQL engine originally built at Facebook, has a rapidly growing community of developers and users. In this talk, speakers from both Facebook and Teradata, will discuss technical details of some of the recent developments such as integration with Hadoop ecosystem (YARN/Slider and Ambari), security features (Kerberos), enabling BI tools via JDBC/ODBC drivers, new connectors (Redis, MongoDB) and storage engines (Raptor) as well as improvements in performance and ANSI SQL coverage. In addition, we will present a few use cases and major new users that leverage interactive SQL capabilities Presto offers. Finally, we will present our roadmap for the next year.
See the video at https://youtu.be/wMy3LXuTb0U
Speaker: Geetha Balasundaram, Developer at ThoughtWorks
From tools and technology to people and requirements, what's different in the data engineering space? App development is traditional now. All enterprises want to become data-guided. Data lake is good start yet the know-hows and do-hows are so many.
Experiences from building a data lake in the retail domain, the talk will be covering.
- What is this vast new space of data engineering,
- Why it is critical to think in terms of data rather than features
- How important it is to understand these technologies and create a data lake that is usable and insightful to business
(BDT303) Running Spark and Presto on the Netflix Big Data PlatformAmazon Web Services
In this session, we discuss how Spark and Presto complement the Netflix big data platform stack that started with Hadoop, and the use cases that Spark and Presto address. Also, we discuss how we run Spark and Presto on top of the Amazon EMR infrastructure; specifically, how we use Amazon S3 as our data warehouse and how we leverage Amazon EMR as a generic framework for data-processing cluster management.
Hortonworks Yarn Code Walk Through January 2014Hortonworks
This slide deck accompanies the Webinar recording YARN Code Walk through on Jan. 22, 2014, on Hortonworks.com/webinars under Past Webinars, or
https://hortonworks.webex.com/hortonworks/lsr.php?AT=pb&SP=EC&rID=129468197&rKey=b645044305775657
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.
Apache Hadoop YARN is the modern distributed operating system for big data applications. It morphed the Hadoop compute layer to be a common resource management platform that can host a wide variety of applications. Many organizations leverage YARN in building their applications on top of Hadoop without themselves repeatedly worrying about resource management, isolation, multi-tenancy issues, etc.
In this talk, we’ll start with the current status of Apache Hadoop YARN—how it is used today in deployments large and small. We'll then move on to the exciting present and future of YARN—features that are further strengthening YARN as the first class resource management platform for data centers running enterprise Hadoop.
We’ll discuss the current status as well as the future promise of features and initiatives like: powerful container placement, global scheduling, support for machine learning and deep learning workloads through GPU and FPGA support, extreme scale with YARN federation, containerized apps on YARN, support for long-running services (alongside applications) natively without any changes, seamless application upgrades, powerful scheduling features like application priorities, intra-queue preemption across applications, and operational enhancements including insights through Timeline Service V2, a new web UI, and better queue management.
Speaker: Sanjay Radia, Chief Architect, Founder, Hortonworks
Apache Hadoop YARN is the modern distributed operating system for big data applications. It morphed the Hadoop compute layer to be a common resource management platform that can host a wide variety of applications. Many organizations leverage YARN in building their applications on top of Hadoop without themselves repeatedly worrying about resource management, isolation, multi-tenancy issues, etc.
In this talk, we’ll start with the current status of Apache Hadoop YARN—how it is used today in deployments large and small. We'll then move on to the exciting present and future of YARN—features that are further strengthening YARN as the first class resource management platform for data centers running enterprise Hadoop.
We’ll discuss the current status as well as the future promise of features and initiatives like: powerful container placement, global scheduling, support for machine learning and deep learning workloads through GPU and FPGA support, extreme scale with YARN federation, containerized apps on YARN, support for long-running services (alongside applications) natively without any changes, seamless application upgrades, powerful scheduling features like application priorities, intra-queue preemption across applications, and operational enhancements including insights through Timeline Service V2, a new web UI, and better queue management.
Apache Hadoop YARN is the latest distributed operating system for HDSF for big data applications and storage. YARN has transformed the Hadoop Compute Layer into a general resource management platform capable of hosting a wide variety of applications.
This lecture begins with the current state how Apache Hadoop YARN is currently used in large scale deployment. The next topic will cover about strengthening YARN 's current and future - like YARN' s excitement - as a top-notch resource management platform for data centers running enterprise Hadoop. Discuss the current state and future of the following functions and initiatives: support of machine learning through strong container placement, global scheduling, GPU and FPGA support and deep learning workload, large scale of YARN federation, on YARN Containerized applications, natural support that does not change to long-running services (along with applications), seamless application upgrades, powerful scheduling functions, operational improvements and better queue management.
The second part of the lecture focuses on the latest enhancement of HDFS. HDFS has several advantages: horizontal scale of IO bandwidth, storage scaled to petabyte storage. In addition, it provides extremely low latency metadata operations and coordinates for over 60,000 concurrent clients. Hadoop 3.0 recently introduced Erasure Coding. One limitation of HDFS is the scaling of multiple files and blocks in the system. I will explain the fundamental change of Hadoop's storage infrastructure using Ozone technology which will be announced soon. This will allow Hadoop to scale billions of files and blocks in the future to a larger number of smaller objects than before.
Apache Hadoop YARN is the modern distributed operating system for big data applications. It morphed the Hadoop compute layer to be a common resource management platform that can host a wide variety of applications. Many organizations leverage YARN in building their applications on top of Hadoop without themselves repeatedly worrying about resource management, isolation, multi-tenancy issues, etc.
In this talk, we’ll start with the current status of Apache Hadoop YARN—how it is used today in deployments large and small. We'll then move on to the exciting present and future of YARN—features that are further strengthening YARN as the first-class resource management platform for data centers running enterprise Hadoop.
We’ll discuss the current status as well as the future promise of features and initiatives like: powerful container placement, global scheduling, support for machine learning and deep learning workloads through GPU and FPGA support, extreme scale with YARN federation, containerized apps on YARN, support for long running services (alongside applications) natively without any changes, seamless application upgrades, powerful scheduling features like application priorities, intra-queue preemption across applications, and operational enhancements including insights through Timeline Service V2, a new web UI, and better queue management.
Speakers
Wangda Tan, Staff Software Engineer, Hortonworks
Billie Rinaldi, Principal Software Engineer I, Hortonworks
Apache Hadoop YARN is the modern distributed operating system for big data applications. It morphed the Hadoop compute layer to be a common resource management platform that can host a wide variety of applications. Many organizations leverage YARN in building their applications on top of Hadoop without themselves repeatedly worrying about resource management, isolation, multi-tenancy issues, etc.
In this talk, we’ll start with the current status of Apache Hadoop YARN—how it is used today in deployments large and small. We'll then move on to the exciting present and future of YARN—features that are further strengthening YARN as the first class resource management platform for data centers running enterprise Hadoop.
We’ll discuss the current status as well as the future promise of features and initiatives like: powerful container placement, global scheduling, support for machine learning and deep learning workloads through GPU and FPGA support, extreme scale with YARN federation, containerized apps on YARN, support for long running services (alongside applications) natively without any changes, seamless application upgrades, powerful scheduling features like application priorities, intra-queue preemption across applications, and operational enhancements including insights through Timeline Service V2, a new web UI, and better queue management.
Speakers
Vinod Kumar Vavilapalli, Hortonworks, Director of Engineering
Sunil Govindan, Hortonworks, Staff Engineer
Everything you wanted to know about Apache Tez:
-- Distributed execution framework targeted towards data-processing applications.
-- Based on expressing a computation as a dataflow graph.
-- Highly customizable to meet a broad spectrum of use cases.
-- Built on top of YARN – the resource management framework for Hadoop.
-- Open source Apache incubator project and Apache licensed.
Apache Tez - Accelerating Hadoop Data Processinghitesh1892
Apache Tez - A New Chapter in Hadoop Data Processing. Talk at Hadoop Summit, San Jose. 2014 By Bikas Saha and Hitesh Shah.
Apache Tez is a modern data processing engine designed for YARN on Hadoop 2. Tez aims to provide high performance and efficiency out of the box, across the spectrum of low latency queries and heavy-weight batch processing.
Similar to Developing Applications with Hadoop 2.0 and YARN by Abhijit Lele (20)
Bringing Sequential Analysis to A/B Testing with examples from his work at Optimizely.
These slides are from a talk given at the SF Data Engineering meetup. http://www.meetup.com/SF-Data-Engineering/events/231047195/
DataEngConf SF16 - Multi-temporal Data StructuresHakka Labs
A mind-bending way of dealing with time syncing when aggregating data from many disparate sources. Talk by Jasmine Tsai and Alyssa Kwan, Clover Health. To hear about future conferences go to http://dataengconf.com
DataEngConf SF16 - Tales from the other side - What a hiring manager wish you...Hakka Labs
Tips for suceeding in your data science job interview. Talk by Bridge Mellichamp, Stitch Labs. To hear about future conferences go to http://dataengconf.com
DataEngConf SF16 - Methods for Content Relevance at LinkedInHakka Labs
Learn how LinkedIn makes article recommendations for its users. Talk by Ajit Singh, LinkedIn. To hear about future conferences go to http://dataengconf.com
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
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.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
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
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
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.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
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”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
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.
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.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
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.