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.
Developing Applications with Hadoop 2.0 and YARN by Abhijit Lele Hakka Labs
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.
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.
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.
Developing Applications with Hadoop 2.0 and YARN by Abhijit Lele Hakka Labs
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.
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.
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.
At the StampedeCon 2015 Big Data Conference: YARN enables Hadoop to move beyond just pure batch processing. With that multiple workloads and tenants now must be able to share a single infrastructure for data processing. Features of the Capacity Scheduler enable resource sharing among multiple tenants in a fair manner with elastic queues to maximize utilization. This talk will focus on the features of the Capacity Scheduler that enable Multi-Tenancy and how resource sharing can be rebalanced using features like Preemption.
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 new YARN framework promises to make Hadoop a general-purpose platform for Big Data and enterprise data hub applications. In this talk, you'll learn about writing and taking advantage of applications built on YARN.
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
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.
Arun C Murthy, Founder and Architect at Hortonworks Inc., talks about the upcoming Next Generation Apache Hadoop MapReduce framework at the Hadoop Summit, 2011.
At the StampedeCon 2015 Big Data Conference: YARN enables Hadoop to move beyond just pure batch processing. With that multiple workloads and tenants now must be able to share a single infrastructure for data processing. Features of the Capacity Scheduler enable resource sharing among multiple tenants in a fair manner with elastic queues to maximize utilization. This talk will focus on the features of the Capacity Scheduler that enable Multi-Tenancy and how resource sharing can be rebalanced using features like Preemption.
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 new YARN framework promises to make Hadoop a general-purpose platform for Big Data and enterprise data hub applications. In this talk, you'll learn about writing and taking advantage of applications built on YARN.
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
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.
Arun C Murthy, Founder and Architect at Hortonworks Inc., talks about the upcoming Next Generation Apache Hadoop MapReduce framework at the Hadoop Summit, 2011.
ApacheCon North America 2014 - Apache Hadoop YARN: The Next-generation Distri...Zhijie Shen
For diverse organizations, Apache Hadoop has become the de-facto place where data & computational resources are shared. This broad usage has stretched its design beyond its intended target. To address this, Apache Hadoop community has come up with next generation of Hadoop’s compute platform: YARN.
YARN in a nutshell is the distributed Operating System of the big-data world. In this talk, we will introduce YARN, covering how the new architecture decouples programming model from resource management, scheduling functions, platform’s fault tolerance & high availability, tools for application tracing & analyses. We will then discuss the exciting ecosystem of Apache Software Foundation projects forming around YARN. We will conclude with a coverage on the applications & services being built around YARN platform which lets user chose the programming models choice, all on the same data.
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 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
Virtualizing Latency Sensitive Workloads and vFabric GemFireCarter Shanklin
This presentation was made by Emad Benjamin of VMware Technical Marketing. Normally I wouldn't upload someone else's preso but I really insisted this get posted and he asked me to help him out.
This deck covers tips and best practices for virtualizing latency sensitive apps on vSphere in general, and takes a deep dive into virtualizing vFabric GemFire, which is a high-performance distributed and memory-optimized key/value store.
Best practices include how to configure the virtual machines and how to tune them appropriately to the hardware the application runs on.
Hadoop World 2011: Next Generation Apache Hadoop MapReduce - Mohadev Konar, H...Cloudera, Inc.
The Apache Hadoop MapReduce framework has hit a scalability limit around 4,000 machines. In this session, we will be presenting the architecture and design of the next generation of MapReduce and will delve into the details of the architecture that makes it much easier to innovate. The architecture will have built in HA, security and multi-tenancy to support many users on the larger clusters. It will also increase innovation, agility and hardware utilization. We will also be presenting large scale and small scale comparisons on some benchmarks with MRV1.
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
Enterprise data centers have to support a diverse of set of workloads: cloud native, big data, high performance computing, and legacy applications. While cloud native applications are ideal to run in Docker clusters, bare metal and virtualization infrastructures must still be supported in the data center. The result is a proliferation of clusters and technologies running in individual silos, resulting in high management costs and low utilization. This talk describes the challenges and experiences in implementing a shared cluster infrastructure based on Kubernetes to support big data, high performance computing, and VM-based workloads. The talk will show the deployment and scaling of a high performance computing workload manager, Spark, and OpenStack, and how the VM and Docker management can be integrated together.
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.
Similar to Writing Yarn Applications Hadoop Summit 2012 (20)
Hortonworks DataFlow (HDF) 3.3 - Taking Stream Processing to the Next LevelHortonworks
The HDF 3.3 release delivers several exciting enhancements and new features. But, the most noteworthy of them is the addition of support for Kafka 2.0 and Kafka Streams.
https://hortonworks.com/webinar/hortonworks-dataflow-hdf-3-3-taking-stream-processing-next-level/
IoT Predictions for 2019 and Beyond: Data at the Heart of Your IoT StrategyHortonworks
Forrester forecasts* that direct spending on the Internet of Things (IoT) will exceed $400 Billion by 2023. From manufacturing and utilities, to oil & gas and transportation, IoT improves visibility, reduces downtime, and creates opportunities for entirely new business models.
But successful IoT implementations require far more than simply connecting sensors to a network. The data generated by these devices must be collected, aggregated, cleaned, processed, interpreted, understood, and used. Data-driven decisions and actions must be taken, without which an IoT implementation is bound to fail.
https://hortonworks.com/webinar/iot-predictions-2019-beyond-data-heart-iot-strategy/
Getting the Most Out of Your Data in the Cloud with CloudbreakHortonworks
Cloudbreak, a part of Hortonworks Data Platform (HDP), simplifies the provisioning and cluster management within any cloud environment to help your business toward its path to a hybrid cloud architecture.
https://hortonworks.com/webinar/getting-data-cloud-cloudbreak-live-demo/
Johns Hopkins - Using Hadoop to Secure Access Log EventsHortonworks
In this webinar, we talk with experts from Johns Hopkins as they share techniques and lessons learned in real-world Apache Hadoop implementation.
https://hortonworks.com/webinar/johns-hopkins-using-hadoop-securely-access-log-events/
Catch a Hacker in Real-Time: Live Visuals of Bots and Bad GuysHortonworks
Cybersecurity today is a big data problem. There’s a ton of data landing on you faster than you can load, let alone search it. In order to make sense of it, we need to act on data-in-motion, use both machine learning, and the most advanced pattern recognition system on the planet: your SOC analysts. Advanced visualization makes your analysts more efficient, helps them find the hidden gems, or bombs in masses of logs and packets.
https://hortonworks.com/webinar/catch-hacker-real-time-live-visuals-bots-bad-guys/
We have introduced several new features as well as delivered some significant updates to keep the platform tightly integrated and compatible with HDP 3.0.
https://hortonworks.com/webinar/hortonworks-dataflow-hdf-3-2-release-raises-bar-operational-efficiency/
Curing Kafka Blindness with Hortonworks Streams Messaging ManagerHortonworks
With the growth of Apache Kafka adoption in all major streaming initiatives across large organizations, the operational and visibility challenges associated with Kafka are on the rise as well. Kafka users want better visibility in understanding what is going on in the clusters as well as within the stream flows across producers, topics, brokers, and consumers.
With no tools in the market that readily address the challenges of the Kafka Ops teams, the development teams, and the security/governance teams, Hortonworks Streams Messaging Manager is a game-changer.
https://hortonworks.com/webinar/curing-kafka-blindness-hortonworks-streams-messaging-manager/
Interpretation Tool for Genomic Sequencing Data in Clinical EnvironmentsHortonworks
The healthcare industry—with its huge volumes of big data—is ripe for the application of analytics and machine learning. In this webinar, Hortonworks and Quanam present a tool that uses machine learning and natural language processing in the clinical classification of genomic variants to help identify mutations and determine clinical significance.
Watch the webinar: https://hortonworks.com/webinar/interpretation-tool-genomic-sequencing-data-clinical-environments/
IBM+Hortonworks = Transformation of the Big Data LandscapeHortonworks
Last year IBM and Hortonworks jointly announced a strategic and deep partnership. Join us as we take a close look at the partnership accomplishments and the conjoined road ahead with industry-leading analytics offers.
View the webinar here: https://hortonworks.com/webinar/ibmhortonworks-transformation-big-data-landscape/
In this exclusive Premier Inside Out, you will hear from Druid committer Slim Bouguerra, Staff Software Engineer and Product Manager Will Xu. These Hortonworkers will explain the vision of these components, review new features, share some best practices and answer your questions.
View the webinar here: https://hortonworks.com/webinar/hortonworks-premier-apache-druid/
Accelerating Data Science and Real Time Analytics at ScaleHortonworks
Gaining business advantages from big data is moving beyond just the efficient storage and deep analytics on diverse data sources to using AI methods and analytics on streaming data to catch insights and take action at the edge of the network.
https://hortonworks.com/webinar/accelerating-data-science-real-time-analytics-scale/
TIME SERIES: APPLYING ADVANCED ANALYTICS TO INDUSTRIAL PROCESS DATAHortonworks
Thanks to sensors and the Internet of Things, industrial processes now generate a sea of data. But are you plumbing its depths to find the insight it contains, or are you just drowning in it? Now, Hortonworks and Seeq team to bring advanced analytics and machine learning to time-series data from manufacturing and industrial processes.
Blockchain with Machine Learning Powered by Big Data: Trimble Transportation ...Hortonworks
Trimble Transportation Enterprise is a leading provider of enterprise software to over 2,000 transportation and logistics companies. They have designed an architecture that leverages Hortonworks Big Data solutions and Machine Learning models to power up multiple Blockchains, which improves operational efficiency, cuts down costs and enables building strategic partnerships.
https://hortonworks.com/webinar/blockchain-with-machine-learning-powered-by-big-data-trimble-transportation-enterprise/
Delivering Real-Time Streaming Data for Healthcare Customers: ClearsenseHortonworks
For years, the healthcare industry has had problems of data scarcity and latency. Clearsense solved the problem by building an open-source Hortonworks Data Platform (HDP) solution while providing decades worth of clinical expertise. Clearsense is delivering smart, real-time streaming data, to its healthcare customers enabling mission-critical data to feed clinical decisions.
https://hortonworks.com/webinar/delivering-smart-real-time-streaming-data-healthcare-customers-clearsense/
Making Enterprise Big Data Small with EaseHortonworks
Every division in an organization builds its own database to keep track of its business. When the organization becomes big, those individual databases grow as well. The data from each database may become silo-ed and have no idea about the data in the other database.
https://hortonworks.com/webinar/making-enterprise-big-data-small-ease/
Driving Digital Transformation Through Global Data ManagementHortonworks
Using your data smarter and faster than your peers could be the difference between dominating your market and merely surviving. Organizations are investing in IoT, big data, and data science to drive better customer experience and create new products, yet these projects often stall in ideation phase to a lack of global data management processes and technologies. Your new data architecture may be taking shape around you, but your goal of globally managing, governing, and securing your data across a hybrid, multi-cloud landscape can remain elusive. Learn how industry leaders are developing their global data management strategy to drive innovation and ROI.
Presented at Gartner Data and Analytics Summit
Speaker:
Dinesh Chandrasekhar
Director of Product Marketing, Hortonworks
HDF 3.1 pt. 2: A Technical Deep-Dive on New Streaming FeaturesHortonworks
Hortonworks DataFlow (HDF) is the complete solution that addresses the most complex streaming architectures of today’s enterprises. More than 20 billion IoT devices are active on the planet today and thousands of use cases across IIOT, Healthcare and Manufacturing warrant capturing data-in-motion and delivering actionable intelligence right NOW. “Data decay” happens in a matter of seconds in today’s digital enterprises.
To meet all the needs of such fast-moving businesses, we have made significant enhancements and new streaming features in HDF 3.1.
https://hortonworks.com/webinar/series-hdf-3-1-technical-deep-dive-new-streaming-features/
Hortonworks DataFlow (HDF) 3.1 - Redefining Data-In-Motion with Modern Data A...Hortonworks
Join the Hortonworks product team as they introduce HDF 3.1 and the core components for a modern data architecture to support stream processing and analytics.
You will learn about the three main themes that HDF addresses:
Developer productivity
Operational efficiency
Platform interoperability
https://hortonworks.com/webinar/series-hdf-3-1-redefining-data-motion-modern-data-architectures/
Unlock Value from Big Data with Apache NiFi and Streaming CDCHortonworks
Apache NiFi is an easy to use, powerful, and reliable system to process and distribute data. It provides an end-to-end platform that can collect, curate, analyze, and act on data in real-time, on-premises, or in the cloud with a drag-and-drop visual interface. It’s being used across industries on large amounts of data that had stored in isolation which made collaboration and analysis difficult.
Join industry experts from Hortonworks and Attunity as they explain how Apache NiFi and streaming CDC technology provides a distributed, resilient platform for unlocking the value of data in new ways.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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.
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.
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.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
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
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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
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath