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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.

Deploying, configuring, and managing large Hadoop and HBase clusters can be quite complex. Just upgrading one Hadoop component on a 2000-node cluster can take a lot of time and expertise, and there have been few tools specialized for Hadoop cluster administrators. AMBARI is an Apache incubator project to deliver Monitoring and Management functionality for Hadoop clusters. This paper presents the AMBARI tools for cluster management, specifically: Cluster pre-configuration and validation; Hadoop software deployment, installation, and smoketest; Hadoop configuration and re-config; and a basic set of management ops including start/stop service, add/remove node, etc. In providing these capabilities, AMBARI seeks to integrate with (rather than replace) existing open-source packaging and deployment technology available in most data centers, such as Puppet and Chef, Yum, Apt, and Zypper.

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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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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.

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Apache Hive is the defacto standard for SQL queries over petabytes of data in Hadoop. It is a comprehensive and compliant engine that offers the broadest range of SQL semantics for Hadoop, providing a powerful set of tools for analysts and developers to access Hadoop data. The session will cover the latest advancements in Hive and provide practical tips for maximizing Hive Performance. Audience: Developers, Architects and System Engineers from the Hortonworks Technology Partner community. Recording: https://hortonworks.webex.com/hortonworks/lsr.php?RCID=7c8f800cbbef256680db14c78b871f97

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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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This presentation will give an overview of two exciting releases for Apache HBase and Phoenix. HBase 2.0 is the next stable major release for Apache HBase scheduled for early 2018. It is the next evolution from the Apache HBase community after 1.0. HBase-2.0 contains a large number of features that are long time in development, some of which include rewritten region assignment , perf improvements (RPC, rewritten write pipeline, etc), async clients and WAL, C++ client, offheaping memstore and other buffers, shading of dependencies, as well as a lot of other fixes and stability improvements. We will go into technical details on some of the most important improvements in the release, as well as what are the implications for the users in terms of API and upgrade paths. Phoenix 5.0 is the next biggest release because of its integration with HBase 2.0 and lot of performance improvements in support of secondary Indexes. It has a lot of cool features such as encoded columns, Kafka, Hive integration, and many other performance improvements. We have presented this at Data work summit 2018 in San Jose.

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