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.
Introduction to Hadoop and Hadoop component rebeccatho
This document provides an introduction to Apache Hadoop, which is an open-source software framework for distributed storage and processing of large datasets. It discusses Hadoop's main components of MapReduce and HDFS. MapReduce is a programming model for processing large datasets in a distributed manner, while HDFS provides distributed, fault-tolerant storage. Hadoop runs on commodity computer clusters and can scale to thousands of nodes.
This document provides an overview of YARN (Yet Another Resource Negotiator), the resource management system for Hadoop. It describes the key components of YARN including the Resource Manager, Node Manager, and Application Master. The Resource Manager tracks cluster resources and schedules applications, while Node Managers monitor nodes and containers. Application Masters communicate with the Resource Manager to manage applications. YARN allows Hadoop to run multiple applications like Spark and HBase, improves on MapReduce scheduling, and transforms Hadoop into a distributed operating system for big data processing.
This document provides an overview of big data and Hadoop. It discusses why Hadoop is useful for extremely large datasets that are difficult to manage in relational databases. It then summarizes what Hadoop is, including its core components like HDFS, MapReduce, HBase, Pig, Hive, Chukwa, and ZooKeeper. The document also outlines Hadoop's design principles and provides examples of how some of its components like MapReduce and Hive work.
A MapReduce job usually splits the input data-set into independent chunks which are processed by the map tasks in a completely parallel manner. The framework sorts the outputs of the maps, which are then input to the reduce tasks. Typically both the input and the output of the job are stored in a file-system.
Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of commodity hardware. It was created to support applications handling large datasets operating on many servers. Key Hadoop technologies include MapReduce for distributed computing, and HDFS for distributed file storage inspired by Google File System. Other related Apache projects extend Hadoop capabilities, like Pig for data flows, Hive for data warehousing, and HBase for NoSQL-like big data. Hadoop provides an effective solution for companies dealing with petabytes of data through distributed and parallel processing.
This presentation discusses the following topics:
Hadoop Distributed File System (HDFS)
How does HDFS work?
HDFS Architecture
Features of HDFS
Benefits of using HDFS
Examples: Target Marketing
HDFS data replication
The document discusses Hadoop, an open-source software framework that allows distributed processing of large datasets across clusters of computers. It describes Hadoop as having two main components - the Hadoop Distributed File System (HDFS) which stores data across infrastructure, and MapReduce which processes the data in a parallel, distributed manner. HDFS provides redundancy, scalability, and fault tolerance. Together these components provide a solution for businesses to efficiently analyze the large, unstructured "Big Data" they collect.
The document provides an overview of the Hadoop Distributed File System (HDFS). It describes HDFS design goals of handling hardware failures, large data sets, and streaming data access. It explains key HDFS concepts like blocks, replication, rack awareness, the read/write process, and the roles of the NameNode and DataNodes. It also covers topics like permissions, safe mode, quotas, and commands for interacting with HDFS.
Introduction to Hadoop and Hadoop component rebeccatho
This document provides an introduction to Apache Hadoop, which is an open-source software framework for distributed storage and processing of large datasets. It discusses Hadoop's main components of MapReduce and HDFS. MapReduce is a programming model for processing large datasets in a distributed manner, while HDFS provides distributed, fault-tolerant storage. Hadoop runs on commodity computer clusters and can scale to thousands of nodes.
This document provides an overview of YARN (Yet Another Resource Negotiator), the resource management system for Hadoop. It describes the key components of YARN including the Resource Manager, Node Manager, and Application Master. The Resource Manager tracks cluster resources and schedules applications, while Node Managers monitor nodes and containers. Application Masters communicate with the Resource Manager to manage applications. YARN allows Hadoop to run multiple applications like Spark and HBase, improves on MapReduce scheduling, and transforms Hadoop into a distributed operating system for big data processing.
This document provides an overview of big data and Hadoop. It discusses why Hadoop is useful for extremely large datasets that are difficult to manage in relational databases. It then summarizes what Hadoop is, including its core components like HDFS, MapReduce, HBase, Pig, Hive, Chukwa, and ZooKeeper. The document also outlines Hadoop's design principles and provides examples of how some of its components like MapReduce and Hive work.
A MapReduce job usually splits the input data-set into independent chunks which are processed by the map tasks in a completely parallel manner. The framework sorts the outputs of the maps, which are then input to the reduce tasks. Typically both the input and the output of the job are stored in a file-system.
Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of commodity hardware. It was created to support applications handling large datasets operating on many servers. Key Hadoop technologies include MapReduce for distributed computing, and HDFS for distributed file storage inspired by Google File System. Other related Apache projects extend Hadoop capabilities, like Pig for data flows, Hive for data warehousing, and HBase for NoSQL-like big data. Hadoop provides an effective solution for companies dealing with petabytes of data through distributed and parallel processing.
This presentation discusses the following topics:
Hadoop Distributed File System (HDFS)
How does HDFS work?
HDFS Architecture
Features of HDFS
Benefits of using HDFS
Examples: Target Marketing
HDFS data replication
The document discusses Hadoop, an open-source software framework that allows distributed processing of large datasets across clusters of computers. It describes Hadoop as having two main components - the Hadoop Distributed File System (HDFS) which stores data across infrastructure, and MapReduce which processes the data in a parallel, distributed manner. HDFS provides redundancy, scalability, and fault tolerance. Together these components provide a solution for businesses to efficiently analyze the large, unstructured "Big Data" they collect.
The document provides an overview of the Hadoop Distributed File System (HDFS). It describes HDFS design goals of handling hardware failures, large data sets, and streaming data access. It explains key HDFS concepts like blocks, replication, rack awareness, the read/write process, and the roles of the NameNode and DataNodes. It also covers topics like permissions, safe mode, quotas, and commands for interacting with HDFS.
This presentation discusses the follow topics
What is Hadoop?
Need for Hadoop
History of Hadoop
Hadoop Overview
Advantages and Disadvantages of Hadoop
Hadoop Distributed File System
Comparing: RDBMS vs. Hadoop
Advantages and Disadvantages of HDFS
Hadoop frameworks
Modules of Hadoop frameworks
Features of 'Hadoop‘
Hadoop Analytics Tools
The document outlines the process for developing a MapReduce application including:
1) Writing map and reduce functions with unit tests, then a driver program to run on test data.
2) Running the program on a cluster with the full dataset and fixing issues.
3) Tuning the program for performance after it is working correctly.
The document provides an overview of Hadoop and its ecosystem. It discusses the history and architecture of Hadoop, describing how it uses distributed storage and processing to handle large datasets across clusters of commodity hardware. The key components of Hadoop include HDFS for storage, MapReduce for processing, and an ecosystem of related projects like Hive, HBase, Pig and Zookeeper that provide additional functions. Advantages are its ability to handle unlimited data storage and high speed processing, while disadvantages include lower speeds for small datasets and limitations on data storage size.
Hive is a data warehouse infrastructure tool that allows users to query and analyze large datasets stored in Hadoop. It uses a SQL-like language called HiveQL to process structured data stored in HDFS. Hive stores metadata about the schema in a database and processes data into HDFS. It provides a familiar interface for querying large datasets using SQL-like queries and scales easily to large datasets.
Infrastructure as a Service ( IaaS) is one of the three fundamental services in cloud computing. IaaS provides access to basic computing resources such as hardware- processor, storage , network cards and more
This document discusses web mining and outlines its goals, types, and techniques. Web mining involves examining data from the world wide web and includes web content mining, web structure mining, and web usage mining. Content mining analyzes web page contents, structure mining analyzes hyperlink structures, and usage mining analyzes web server logs and user browsing patterns. Common techniques discussed include page ranking algorithms, focused crawlers, usage pattern discovery, and preprocessing of web server logs.
Hadoop MapReduce is an open source framework for distributed processing of large datasets across clusters of computers. It allows parallel processing of large datasets by dividing the work across nodes. The framework handles scheduling, fault tolerance, and distribution of work. MapReduce consists of two main phases - the map phase where the data is processed key-value pairs and the reduce phase where the outputs of the map phase are aggregated together. It provides an easy programming model for developers to write distributed applications for large scale processing of structured and unstructured data.
The document provides an introduction to NoSQL and HBase. It discusses what NoSQL is, the different types of NoSQL databases, and compares NoSQL to SQL databases. It then focuses on HBase, describing its architecture and components like HMaster, regionservers, Zookeeper. It explains how HBase stores and retrieves data, the write process involving memstores and compaction. It also covers HBase shell commands for creating, inserting, querying and deleting data.
Hadoop DFS consists of HDFS for storage and MapReduce for processing. HDFS provides massive storage, fault tolerance through data replication, and high throughput access to data. It uses a master-slave architecture with a NameNode managing the file system namespace and DataNodes storing file data blocks. The NameNode ensures data reliability through policies that replicate blocks across racks and nodes. HDFS provides scalability, flexibility and low-cost storage of large datasets.
The document discusses various scheduling techniques in cloud computing. It begins with an introduction to scheduling and its importance in cloud computing. It then covers traditional scheduling approaches like FCFS, priority queue, and shortest job first. The document also presents job scheduling frameworks, dynamic and fault-tolerant scheduling, deadline-constrained scheduling, and inter-cloud meta-scheduling. It concludes with the benefits of effective scheduling in improving service quality and resource utilization in cloud environments.
The document discusses different models for distributed systems including physical, architectural and fundamental models. It describes the physical model which captures the hardware composition and different generations of distributed systems. The architectural model specifies the components and relationships in a system. Key architectural elements discussed include communicating entities like processes and objects, communication paradigms like remote invocation and indirect communication, roles and responsibilities of entities, and their physical placement. Common architectures like client-server, layered and tiered are also summarized.
The document provides an overview of MapReduce, including:
1) MapReduce is a programming model and implementation that allows for large-scale data processing across clusters of computers. It handles parallelization, distribution, and reliability.
2) The programming model involves mapping input data to intermediate key-value pairs and then reducing by key to output results.
3) Example uses of MapReduce include word counting and distributed searching of text.
Replication in computing involves sharing information so as to ensure consistency between redundant resources, such as software or hardware components, to improve reliability, fault-tolerance, or accessibility.
As part of the recent release of Hadoop 2 by the Apache Software Foundation, YARN and MapReduce 2 deliver significant upgrades to scheduling, resource management, and execution in Hadoop.
At their core, YARN and MapReduce 2’s improvements separate cluster resource management capabilities from MapReduce-specific logic. YARN enables Hadoop to share resources dynamically between multiple parallel processing frameworks such as Cloudera Impala, allows more sensible and finer-grained resource configuration for better cluster utilization, and scales Hadoop to accommodate more and larger jobs.
Designed by Sanjay Ghemawat , Howard Gobioff and Shun-Tak Leung of Google in 2002-03.
Provides fault tolerance, serving large number of clients with high aggregate performance.
The field of Google is beyond the searching.
Google store the data in more than 15 thousands commodity hardware.
Handles the exceptions of Google and other Google specific challenges in their distributed file system.
HDFS is a Java-based file system that provides scalable and reliable data storage, and it was designed to span large clusters of commodity servers. HDFS has demonstrated production scalability of up to 200 PB of storage and a single cluster of 4500 servers, supporting close to a billion files and blocks.
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.
This presentation discusses the follow topics
What is Hadoop?
Need for Hadoop
History of Hadoop
Hadoop Overview
Advantages and Disadvantages of Hadoop
Hadoop Distributed File System
Comparing: RDBMS vs. Hadoop
Advantages and Disadvantages of HDFS
Hadoop frameworks
Modules of Hadoop frameworks
Features of 'Hadoop‘
Hadoop Analytics Tools
The document outlines the process for developing a MapReduce application including:
1) Writing map and reduce functions with unit tests, then a driver program to run on test data.
2) Running the program on a cluster with the full dataset and fixing issues.
3) Tuning the program for performance after it is working correctly.
The document provides an overview of Hadoop and its ecosystem. It discusses the history and architecture of Hadoop, describing how it uses distributed storage and processing to handle large datasets across clusters of commodity hardware. The key components of Hadoop include HDFS for storage, MapReduce for processing, and an ecosystem of related projects like Hive, HBase, Pig and Zookeeper that provide additional functions. Advantages are its ability to handle unlimited data storage and high speed processing, while disadvantages include lower speeds for small datasets and limitations on data storage size.
Hive is a data warehouse infrastructure tool that allows users to query and analyze large datasets stored in Hadoop. It uses a SQL-like language called HiveQL to process structured data stored in HDFS. Hive stores metadata about the schema in a database and processes data into HDFS. It provides a familiar interface for querying large datasets using SQL-like queries and scales easily to large datasets.
Infrastructure as a Service ( IaaS) is one of the three fundamental services in cloud computing. IaaS provides access to basic computing resources such as hardware- processor, storage , network cards and more
This document discusses web mining and outlines its goals, types, and techniques. Web mining involves examining data from the world wide web and includes web content mining, web structure mining, and web usage mining. Content mining analyzes web page contents, structure mining analyzes hyperlink structures, and usage mining analyzes web server logs and user browsing patterns. Common techniques discussed include page ranking algorithms, focused crawlers, usage pattern discovery, and preprocessing of web server logs.
Hadoop MapReduce is an open source framework for distributed processing of large datasets across clusters of computers. It allows parallel processing of large datasets by dividing the work across nodes. The framework handles scheduling, fault tolerance, and distribution of work. MapReduce consists of two main phases - the map phase where the data is processed key-value pairs and the reduce phase where the outputs of the map phase are aggregated together. It provides an easy programming model for developers to write distributed applications for large scale processing of structured and unstructured data.
The document provides an introduction to NoSQL and HBase. It discusses what NoSQL is, the different types of NoSQL databases, and compares NoSQL to SQL databases. It then focuses on HBase, describing its architecture and components like HMaster, regionservers, Zookeeper. It explains how HBase stores and retrieves data, the write process involving memstores and compaction. It also covers HBase shell commands for creating, inserting, querying and deleting data.
Hadoop DFS consists of HDFS for storage and MapReduce for processing. HDFS provides massive storage, fault tolerance through data replication, and high throughput access to data. It uses a master-slave architecture with a NameNode managing the file system namespace and DataNodes storing file data blocks. The NameNode ensures data reliability through policies that replicate blocks across racks and nodes. HDFS provides scalability, flexibility and low-cost storage of large datasets.
The document discusses various scheduling techniques in cloud computing. It begins with an introduction to scheduling and its importance in cloud computing. It then covers traditional scheduling approaches like FCFS, priority queue, and shortest job first. The document also presents job scheduling frameworks, dynamic and fault-tolerant scheduling, deadline-constrained scheduling, and inter-cloud meta-scheduling. It concludes with the benefits of effective scheduling in improving service quality and resource utilization in cloud environments.
The document discusses different models for distributed systems including physical, architectural and fundamental models. It describes the physical model which captures the hardware composition and different generations of distributed systems. The architectural model specifies the components and relationships in a system. Key architectural elements discussed include communicating entities like processes and objects, communication paradigms like remote invocation and indirect communication, roles and responsibilities of entities, and their physical placement. Common architectures like client-server, layered and tiered are also summarized.
The document provides an overview of MapReduce, including:
1) MapReduce is a programming model and implementation that allows for large-scale data processing across clusters of computers. It handles parallelization, distribution, and reliability.
2) The programming model involves mapping input data to intermediate key-value pairs and then reducing by key to output results.
3) Example uses of MapReduce include word counting and distributed searching of text.
Replication in computing involves sharing information so as to ensure consistency between redundant resources, such as software or hardware components, to improve reliability, fault-tolerance, or accessibility.
As part of the recent release of Hadoop 2 by the Apache Software Foundation, YARN and MapReduce 2 deliver significant upgrades to scheduling, resource management, and execution in Hadoop.
At their core, YARN and MapReduce 2’s improvements separate cluster resource management capabilities from MapReduce-specific logic. YARN enables Hadoop to share resources dynamically between multiple parallel processing frameworks such as Cloudera Impala, allows more sensible and finer-grained resource configuration for better cluster utilization, and scales Hadoop to accommodate more and larger jobs.
Designed by Sanjay Ghemawat , Howard Gobioff and Shun-Tak Leung of Google in 2002-03.
Provides fault tolerance, serving large number of clients with high aggregate performance.
The field of Google is beyond the searching.
Google store the data in more than 15 thousands commodity hardware.
Handles the exceptions of Google and other Google specific challenges in their distributed file system.
HDFS is a Java-based file system that provides scalable and reliable data storage, and it was designed to span large clusters of commodity servers. HDFS has demonstrated production scalability of up to 200 PB of storage and a single cluster of 4500 servers, supporting close to a billion files and blocks.
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.
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
This document provides best practices for YARN administrators and application developers. For administrators, it discusses YARN configuration, enabling ResourceManager high availability, configuring schedulers like Capacity Scheduler and Fair Scheduler, sizing containers, configuring NodeManagers, log aggregation, and metrics. For application developers, it discusses whether to use an existing framework or develop a native application, understanding YARN components, writing the client, and writing the ApplicationMaster.
Taming YARN @ Hadoop conference Japan 2014Tsuyoshi OZAWA
The document discusses Resource Manager high availability in YARN. It describes how the active and standby Resource Managers store state information in ZooKeeper, and how the standby automatically fails over to become active if it detects a failure of the active. Key configurations include enabling HA, specifying the ZooKeeper addresses, and setting timeouts.
Taming YARN @ Hadoop Conference Japan 2014Tsuyoshi OZAWA
The document discusses YARN (Yet Another Resource Negotiator), a resource management framework for Hadoop. It describes YARN components like the ResourceManager, NodeManager, and ApplicationMaster. It covers YARN configuration, capacity planning, health checks, thread tuning, and enabling high availability of the ResourceManager through ZooKeeper.
YARN - way to share cluster BEYOND HADOOPOmkar Joshi
Describing YARN's architecture, Resource localization model, security and future work (like rm restart, RM -HA), contibuting to open source and hadoop.
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.
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.
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.
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
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.
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.
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.
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.
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.
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.
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Infrastructure Challenges in Scaling RAG with Custom AI modelsZilliz
Building Retrieval-Augmented Generation (RAG) systems with open-source and custom AI models is a complex task. This talk explores the challenges in productionizing RAG systems, including retrieval performance, response synthesis, and evaluation. We’ll discuss how to leverage open-source models like text embeddings, language models, and custom fine-tuned models to enhance RAG performance. Additionally, we’ll cover how BentoML can help orchestrate and scale these AI components efficiently, ensuring seamless deployment and management of RAG systems in the cloud.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Full-RAG: A modern architecture for hyper-personalizationZilliz
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!SOFTTECHHUB
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
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For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!