Apache Ambari - What&#x27;s New in 1.5.0

Apache Ambari - What's New in 2.1

Deploying, configuring, and managing large Apache Hadoop and HBase clusters can be quite complex. Once you have your clusters, keeping them up and running and making sure that the SLAs are met presents even more challenges and headaches to Hadoop operators. To make matters worse, managing upgrades can be a nightmare. Hadoop users are presented with their own fair share of difficulties such as slow running jobs and not knowing why they are slow. For third-party software vendors interested in incorporating Hadoop management and monitoring capabilities, there does not seem to be an obvious, easy solution. Apache Ambari is aimed at making lives of Hadoop operators, users, and integrators simpler by providing a management interface to do all of that and more. This session presents usages of Ambari`s Web UI for Hadoop operators (deploying, managing, and monitoring) as well as Hadoop users (job analytics). The talk will also touch upon Ambari`s REST API and how it is used in the real world. The session concludes by revealing the future roadmap of Ambari including queue management, upgrade, disaster recovery, high availability, and more.

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Managing Enterprise Hadoop Clusters with Apache Ambari

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Apache Ambari - What's New in 2.4

Apache Ambari: Simplified Hadoop Cluster Operation & Troubleshooting

zData BI & Advanced Analytics Platform + 8 Week Pilot Programs

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Apache Ambari Stack Extensibility

Ambari talk at Hadoop Summit, Tokyo 2016 Abstract Apache Ambari has become an indispensable tool for operating Hadoop clusters from as small as 10s of nodes to 1000s of nodes. Ambari’s deep knowledge of the Hadoop stack allows it to deploy a cluster within minutes and manage the entire lifecycle: scaling, security, upgrades, and more. This talk will cover the central features important to cluster operators and the latest innovations from the community. We will discuss automatically deploying clusters with Blueprints, adding custom services, scaling the number of hosts as the data needs grow, adding High Availability for critical services, securing with MIT kerberos, and upgrading the Hadoop stack with features like Rolling & Express Upgrade. More advanced users will also be interested in using Ambari’s powerful REST API to automate workflows. For users and data scientists, Ambari provides LDAP sync, Role-Based Access Control to handle user permissions, and a framework to host Ambari Views such as as the newly added Views for Hive, Oozie, Capacity Scheduler, Tez, Storm, and Zeppelin. Lastly, we will cover how to monitor the health of the cluster via Alerts and troubleshoot problems by using new features like LogSearch and Ambari Metrics Systems integrated with Grafana UI.

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Managing your Hadoop Clusters with Apache Ambari

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En vedette

Utrecht NL-HUG/Data Science-NL - Agile Data Slides

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As the Enterprise’s big data program matures and Apache Hadoop becomes more deeply embedded in critical operations, the ability to support and operate it efficiently and reliably becomes increasingly important. To aid enterprise in operating modern data architecture at scale, Red Hat and Hortonworks have collaborated to integrate Hortonworks Data Platform with Red Hat’s proven platform technologies. Join us in this interactive series, as we’ll demonstrate how Red Hat JBoss Data Virtualization can integrate with Hadoop through Hive and provide users easy access to data.

Red Hat - Presentation at Hortonworks Booth - Strata 2014

The Hadoop community announced Hadoop 3.0 GA in December, 2017 and 3.1 around April, 2018 loaded with a lot of features and improvements. One of the biggest challenges for any new major release of a software platform is its compatibility. Apache Hadoop community has focused on ensuring wire and binary compatibility for Hadoop 2 clients and workloads. There are many challenges to be addressed by admins while upgrading to a major release of Hadoop. Users running workloads on Hadoop 2 should be able to seamlessly run or migrate their workloads onto Hadoop 3. This session will be deep diving into upgrade aspects in detail and provide a detailed preview of migration strategies with information on what works and what might not work. This talk would focus on the motivation for upgrading to Hadoop 3 and provide a cluster upgrade guide for admins and workload migration guide for users of Hadoop. Speaker Suma Shivaprasad, Hortonworks, Staff Engineer Rohith Sharma, Hortonworks, Senior Software Engineer

En vedette (6)

Utrecht NL-HUG/Data Science-NL - Agile Data Slides

Apache Ambari - What's New in 1.6.0

Orange County HUG - Agile Data on HDP

LA HUG - Agile Analytics Applications on HDP

MapReduce Container ReUse

Red Hat - Presentation at Hortonworks Booth - Strata 2014

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Apache Ambari is now the preferred way of provisioning, managing and monitoring Hadoop Clusters. Ambari helps users to manage Hadoop clusters simplifying actions such as upgrades, configuration management, service management, etc. From release 2.0, Ambari started supporting automated Rolling Upgrades. This was further enhanced with release 2.2.0.0 to include support for Express Upgrades, which allows users to upgrade large scale clusters faster but requiring cluster downtime. This talk will cover planning and execution of Hadoop cluster upgrades from an operational perspective. The talk will also cover the internals of the upgrade process including the various stages such as pre-upgrade, backup, service checks, configuration upgrades, and finalization. Finally, the talk will cover troubleshooting upgrade failures, monitoring services during upgrades and post upgrade actions. The presentation will conclude with a case study that will cover how the upgrade process works on a large cluster (including aspects such as planning the upgrade, the amount of time required for the various stages, and troubleshooting)

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Enterprise-Grade Rolling Upgrade for a Live Hadoop Cluster

Speakers: Kevin O'Dell, Aleksandr Shulman & Kathleen Ting (Cloudera) From supporting the 0.90.x, 0.92, 0.94, and 0.96 HBase installations on clusters ranging from tens to hundreds of nodes, Cloudera has seen it all. Having automated the upgrade paths from the different Apache releases, we have developed a smooth path that can help the community with upcoming upgrades. In addition to automation best practices, in this talk you'll also learn proactive configuration tweaks and operational best practices to keep your HBase cluster always up and running. We'll also walk through how to contain an application bug let loose in production, to minimize the impact on HBase posed by faulty hardware, and the direct correlation between inefficient schema design and HBase performance.

Tales from the Cloudera Field

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Speaker: Ravi Mutyala When we started using Apache Accumulo on large scale, our key concern was on monitoring the health of the cluster. Accumulo exposes metrics through JMX. Ganglia and Nagios are the de-facto metrics and monitoring tools for hadoop clusters. We identified that integration with ganglia, nagios and Apache Ambari will provide ease of use both for monitoring and managing Accumulo clusters. We started with ganglia and nagios integration which helps reuse all the hadoop monitoring infrastructure for Accumulo. Our next target is Apache Ambari integration for Accumulo. In this talk, we focus on why we need to integrate and how this can be done. We will show a Hands On for ganglia and nagios integration and share the status of ambari integration.

Accumulo Summit 2014: Monitoring Apache Accumulo

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NGINX: Basics and Best Practices EMEA

NGINX, Inc.

NGINX is used by more than 130 million websites as a lightweight way to serve web content. Use it to decrease costs, improve performance and open up bottlenecks in web and application server environments without a major architectural overhaul. In this talk, we'll cover the three most basic use cases of static content delivery, application load balancing, and web proxying with caching; and touch on the NGINX maintained Docker container.

NGINX 101 - now with more Docker

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NGINX 101 - now with more Docker

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Apache Ambari - What's New in 1.2.3

Hadoop is often viewed as needing racks of dedicated boxes -despite the fact that in sheer number terms, the majority of Hadoop clusters ever created have been brought up on public cloud infrastructures -particularly Amazon`s. Yet the rest of datacenter computing is moving towards virtualization -be it in-cloud startups or in-enterprise IT departments. Some organizations are standing up private clouds: a rack or two of servers with an API for VM creation. Hadoop can live there -it just needs to integrate better. At the same time, OpenStack is emerging as the de-facto standard open source cloud platform for private use, and is available publicly from a number of cloud infrastructure service providers. This talk looks at what we`ve done -and are doing- to integrate Hadoop with OpenStack. This is taking it beyond Hadoop`s current support for Amazon`s infrastructure, making a combined Hadoop + OpenStack cluster something to consider in-house -and in-cloud. This work is being done in collaboration with members of the OpenStack community, showing how cloud and big data projects can not only co-exist, we can co-develop our platforms.

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Plus de Hortonworks

Hortonworks DataFlow (HDF) 3.3 - Taking Stream Processing to the Next Level

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/

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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/

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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/

Curing Kafka Blindness with Hortonworks Streams Messaging Manager

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/

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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/

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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/

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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

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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/

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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.

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