際際滷shows by User: JayushLuniya / http://www.slideshare.net/images/logo.gif 際際滷shows by User: JayushLuniya / Wed, 11 Dec 2019 21:35:32 GMT 際際滷Share feed for 際際滷shows by User: JayushLuniya Future of Apache Ambari /slideshow/future-of-apache-ambari-204628726/204628726 futureofapacheambari-final-170616035029-191211213532
Apache Ambari is an extensible framework that simplifies provisioning, managing and monitoring Hadoop clusters. Apache Ambari was built on a standardized stack-based operations model. Stacks wrap services of all shapes and sizes with a consistent definition and lifecycle-control layer; thereby providing a consistent approach for managing and monitoring the services. This also provided a natural extension point for operators and the community to bring in their own add-on services and plug-in the new services into the stack. However, one of the fundamental limitations of the current Apache Ambari architecture has been that there is a strong one-on-one coupling between entities. For instance, a cluster is tied to a single stack and a Hadoop operator can only deploy services defined in that stack, a cluster can have only a single instance of a service and a host can have only a single instance of a component. Taking into consideration various use case scenarios that cannot be enabled due to these limitations there is a growing need to revamp the Ambari architecture. In this talk, we propose a revamped Apache Ambari architecture that will open up the floodgates for a wide range of scenarios that wouldnt have been possible thus far. We will focus the discussion on a new mpack-based operations model that will replace the stack-based operations model. A management package is a self-contained deployment artifact that includes all the details for deploying, managing and upgrading a set of services bundled in the package. A third-party provider can also build their own management package containing their custom services. This eliminates the need to plug-in their services into a stack and also can define their own upgrade story for these custom services. A Hadoop operator will be able to deploy a Hadoop cluster with a mix of services across multiple packages instead of being limited to a single stack. For example, it would be possible to deploy a cluster with HDFS from HDP and NIFI from HDF. Further, we will also discuss about the architectural changes needed to enable a multi instance architecture in future Ambari releases to support deploying multiple instances of a service in a cluster, deploying multiple instances of a component on a host as well as future proofing the Ambari architecture to leverage some of the advancements happening in the Hadoop community like YARN services (YARN-4692). We will wrap up the conversation with a brief overview of other improvements planned for future releases of Ambari.]]>
Apache Ambari is an extensible framework that simplifies provisioning, managing and monitoring Hadoop clusters. Apache Ambari was built on a standardized stack-based operations model. Stacks wrap services of all shapes and sizes with a consistent definition and lifecycle-control layer; thereby providing a consistent approach for managing and monitoring the services. This also provided a natural extension point for operators and the community to bring in their own add-on services and plug-in the new services into the stack. However, one of the fundamental limitations of the current Apache Ambari architecture has been that there is a strong one-on-one coupling between entities. For instance, a cluster is tied to a single stack and a Hadoop operator can only deploy services defined in that stack, a cluster can have only a single instance of a service and a host can have only a single instance of a component. Taking into consideration various use case scenarios that cannot be enabled due to these limitations there is a growing need to revamp the Ambari architecture. In this talk, we propose a revamped Apache Ambari architecture that will open up the floodgates for a wide range of scenarios that wouldnt have been possible thus far. We will focus the discussion on a new mpack-based operations model that will replace the stack-based operations model. A management package is a self-contained deployment artifact that includes all the details for deploying, managing and upgrading a set of services bundled in the package. A third-party provider can also build their own management package containing their custom services. This eliminates the need to plug-in their services into a stack and also can define their own upgrade story for these custom services. A Hadoop operator will be able to deploy a Hadoop cluster with a mix of services across multiple packages instead of being limited to a single stack. For example, it would be possible to deploy a cluster with HDFS from HDP and NIFI from HDF. Further, we will also discuss about the architectural changes needed to enable a multi instance architecture in future Ambari releases to support deploying multiple instances of a service in a cluster, deploying multiple instances of a component on a host as well as future proofing the Ambari architecture to leverage some of the advancements happening in the Hadoop community like YARN services (YARN-4692). We will wrap up the conversation with a brief overview of other improvements planned for future releases of Ambari.]]> Wed, 11 Dec 2019 21:35:32 GMT /slideshow/future-of-apache-ambari-204628726/204628726 JayushLuniya@slideshare.net(JayushLuniya) Future of Apache Ambari JayushLuniya Apache Ambari is an extensible framework that simplifies provisioning, managing and monitoring Hadoop clusters. Apache Ambari was built on a standardized stack-based operations model. Stacks wrap services of all shapes and sizes with a consistent definition and lifecycle-control layer; thereby providing a consistent approach for managing and monitoring the services. This also provided a natural extension point for operators and the community to bring in their own add-on services and plug-in the new services into the stack. However, one of the fundamental limitations of the current Apache Ambari architecture has been that there is a strong one-on-one coupling between entities. For instance, a cluster is tied to a single stack and a Hadoop operator can only deploy services defined in that stack, a cluster can have only a single instance of a service and a host can have only a single instance of a component. Taking into consideration various use case scenarios that cannot be enabled due to these limitations there is a growing need to revamp the Ambari architecture. In this talk, we propose a revamped Apache Ambari architecture that will open up the floodgates for a wide range of scenarios that wouldnt have been possible thus far. We will focus the discussion on a new mpack-based operations model that will replace the stack-based operations model. A management package is a self-contained deployment artifact that includes all the details for deploying, managing and upgrading a set of services bundled in the package. A third-party provider can also build their own management package containing their custom services. This eliminates the need to plug-in their services into a stack and also can define their own upgrade story for these custom services. A Hadoop operator will be able to deploy a Hadoop cluster with a mix of services across multiple packages instead of being limited to a single stack. For example, it would be possible to deploy a cluster with HDFS from HDP and NIFI from HDF. Further, we will also discuss about the architectural changes needed to enable a multi instance architecture in future Ambari releases to support deploying multiple instances of a service in a cluster, deploying multiple instances of a component on a host as well as future proofing the Ambari architecture to leverage some of the advancements happening in the Hadoop community like YARN services (YARN-4692). We will wrap up the conversation with a brief overview of other improvements planned for future releases of Ambari. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/futureofapacheambari-final-170616035029-191211213532-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Apache Ambari is an extensible framework that simplifies provisioning, managing and monitoring Hadoop clusters. Apache Ambari was built on a standardized stack-based operations model. Stacks wrap services of all shapes and sizes with a consistent definition and lifecycle-control layer; thereby providing a consistent approach for managing and monitoring the services. This also provided a natural extension point for operators and the community to bring in their own add-on services and plug-in the new services into the stack. However, one of the fundamental limitations of the current Apache Ambari architecture has been that there is a strong one-on-one coupling between entities. For instance, a cluster is tied to a single stack and a Hadoop operator can only deploy services defined in that stack, a cluster can have only a single instance of a service and a host can have only a single instance of a component. Taking into consideration various use case scenarios that cannot be enabled due to these limitations there is a growing need to revamp the Ambari architecture. In this talk, we propose a revamped Apache Ambari architecture that will open up the floodgates for a wide range of scenarios that wouldnt have been possible thus far. We will focus the discussion on a new mpack-based operations model that will replace the stack-based operations model. A management package is a self-contained deployment artifact that includes all the details for deploying, managing and upgrading a set of services bundled in the package. A third-party provider can also build their own management package containing their custom services. This eliminates the need to plug-in their services into a stack and also can define their own upgrade story for these custom services. A Hadoop operator will be able to deploy a Hadoop cluster with a mix of services across multiple packages instead of being limited to a single stack. For example, it would be possible to deploy a cluster with HDFS from HDP and NIFI from HDF. Further, we will also discuss about the architectural changes needed to enable a multi instance architecture in future Ambari releases to support deploying multiple instances of a service in a cluster, deploying multiple instances of a component on a host as well as future proofing the Ambari architecture to leverage some of the advancements happening in the Hadoop community like YARN services (YARN-4692). We will wrap up the conversation with a brief overview of other improvements planned for future releases of Ambari.
Future of Apache Ambari from Jayush Luniya
]]> 308 1 https://cdn.slidesharecdn.com/ss_thumbnails/futureofapacheambari-final-170616035029-191211213532-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Manage Add-on Services in Apache Ambari /slideshow/manage-addon-services-in-apache-ambari/80153838 ambaridataworkssummitsydney2017-170925225847
Cutting-edge Hadoop clusters are bound to need custom (add-on) services that are not available in the Hadoop distribution of their choice. Agility is crucial for companies to integrate any service into existing large-scale Hadoop clusters with ease. Apache Ambari manages the Hadoop cluster and solves this problem by extending the stack with add-on services, which can be a new Apache project, different Hadoop file system, or internal tool. This talk covers how to create a service definition in Ambari to manage lifecycle commands and configs, plus advanced topics like packaging, installing from multiple repositories, recommending and validating configs using Service Advisor, running custom commands, defining dependencies on configs and other services, and more. We will also cover how to create custom metrics and dashboards using Ambari Metric System and Grafana, generating alerts, and enabling security by authenticating with Kerberos. Further, we will discuss the future of service definitions and how Ambari 3.0 will support custom services through Management Packs to enable Hadoop vendors to release software faster.]]>
Cutting-edge Hadoop clusters are bound to need custom (add-on) services that are not available in the Hadoop distribution of their choice. Agility is crucial for companies to integrate any service into existing large-scale Hadoop clusters with ease. Apache Ambari manages the Hadoop cluster and solves this problem by extending the stack with add-on services, which can be a new Apache project, different Hadoop file system, or internal tool. This talk covers how to create a service definition in Ambari to manage lifecycle commands and configs, plus advanced topics like packaging, installing from multiple repositories, recommending and validating configs using Service Advisor, running custom commands, defining dependencies on configs and other services, and more. We will also cover how to create custom metrics and dashboards using Ambari Metric System and Grafana, generating alerts, and enabling security by authenticating with Kerberos. Further, we will discuss the future of service definitions and how Ambari 3.0 will support custom services through Management Packs to enable Hadoop vendors to release software faster.]]> Mon, 25 Sep 2017 22:58:47 GMT /slideshow/manage-addon-services-in-apache-ambari/80153838 JayushLuniya@slideshare.net(JayushLuniya) Manage Add-on Services in Apache Ambari JayushLuniya Cutting-edge Hadoop clusters are bound to need custom (add-on) services that are not available in the Hadoop distribution of their choice. Agility is crucial for companies to integrate any service into existing large-scale Hadoop clusters with ease. Apache Ambari manages the Hadoop cluster and solves this problem by extending the stack with add-on services, which can be a new Apache project, different Hadoop file system, or internal tool. This talk covers how to create a service definition in Ambari to manage lifecycle commands and configs, plus advanced topics like packaging, installing from multiple repositories, recommending and validating configs using Service Advisor, running custom commands, defining dependencies on configs and other services, and more. We will also cover how to create custom metrics and dashboards using Ambari Metric System and Grafana, generating alerts, and enabling security by authenticating with Kerberos. Further, we will discuss the future of service definitions and how Ambari 3.0 will support custom services through Management Packs to enable Hadoop vendors to release software faster. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ambaridataworkssummitsydney2017-170925225847-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Cutting-edge Hadoop clusters are bound to need custom (add-on) services that are not available in the Hadoop distribution of their choice. Agility is crucial for companies to integrate any service into existing large-scale Hadoop clusters with ease. Apache Ambari manages the Hadoop cluster and solves this problem by extending the stack with add-on services, which can be a new Apache project, different Hadoop file system, or internal tool. This talk covers how to create a service definition in Ambari to manage lifecycle commands and configs, plus advanced topics like packaging, installing from multiple repositories, recommending and validating configs using Service Advisor, running custom commands, defining dependencies on configs and other services, and more. We will also cover how to create custom metrics and dashboards using Ambari Metric System and Grafana, generating alerts, and enabling security by authenticating with Kerberos. Further, we will discuss the future of service definitions and how Ambari 3.0 will support custom services through Management Packs to enable Hadoop vendors to release software faster.
Manage Add-on Services in Apache Ambari from Jayush Luniya
]]> 506 4 https://cdn.slidesharecdn.com/ss_thumbnails/ambaridataworkssummitsydney2017-170925225847-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Mpack Based Operations Model /slideshow/mpack-based-operations-model/76889552 mpackbasedoperationsmodel-170613054322
The Apache Ambari 3.x architecture will be based on a new mpack-based operations model designed from the ground up and will replace the current stack-based operations model. Management package will be a self-contained distribution artifact that includes all the details for deploying, managing and upgrading a verified combination of services. A cluster operator will be able to deploy a cluster with a mix of services across multiple management packs instead of being limited to deploying services from a single stack.]]>
The Apache Ambari 3.x architecture will be based on a new mpack-based operations model designed from the ground up and will replace the current stack-based operations model. Management package will be a self-contained distribution artifact that includes all the details for deploying, managing and upgrading a verified combination of services. A cluster operator will be able to deploy a cluster with a mix of services across multiple management packs instead of being limited to deploying services from a single stack.]]> Tue, 13 Jun 2017 05:43:22 GMT /slideshow/mpack-based-operations-model/76889552 JayushLuniya@slideshare.net(JayushLuniya) Mpack Based Operations Model JayushLuniya The Apache Ambari 3.x architecture will be based on a new mpack-based operations model designed from the ground up and will replace the current stack-based operations model. Management package will be a self-contained distribution artifact that includes all the details for deploying, managing and upgrading a verified combination of services. A cluster operator will be able to deploy a cluster with a mix of services across multiple management packs instead of being limited to deploying services from a single stack. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/mpackbasedoperationsmodel-170613054322-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> The Apache Ambari 3.x architecture will be based on a new mpack-based operations model designed from the ground up and will replace the current stack-based operations model. Management package will be a self-contained distribution artifact that includes all the details for deploying, managing and upgrading a verified combination of services. A cluster operator will be able to deploy a cluster with a mix of services across multiple management packs instead of being limited to deploying services from a single stack.
Mpack Based Operations Model from Jayush Luniya
]]> 509 2 https://cdn.slidesharecdn.com/ss_thumbnails/mpackbasedoperationsmodel-170613054322-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Streamline Hadoop DevOps with Apache Ambari /slideshow/streamline-hadoop-devops-with-apache-ambari-67928971/67928971 apacheambarihadoopsummittokyo2016v2-161031161715
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. Ambaris 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 Ambaris 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.]]>
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. Ambaris 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 Ambaris 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.]]> Mon, 31 Oct 2016 16:17:15 GMT /slideshow/streamline-hadoop-devops-with-apache-ambari-67928971/67928971 JayushLuniya@slideshare.net(JayushLuniya) Streamline Hadoop DevOps with Apache Ambari JayushLuniya 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. Ambaris 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 Ambaris 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. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/apacheambarihadoopsummittokyo2016v2-161031161715-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> 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. Ambaris 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 &amp; Express Upgrade. More advanced users will also be interested in using Ambaris 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.
Streamline Hadoop DevOps with Apache Ambari from Jayush Luniya
]]> 640 4 https://cdn.slidesharecdn.com/ss_thumbnails/apacheambarihadoopsummittokyo2016v2-161031161715-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Managing Enterprise Hadoop Clusters with Apache Ambari /slideshow/managing-enterprise-hadoop-clusters-with-apache-ambari-64421498/64421498 managingenterprisehadoopclusterswithapacheambari-v2-160727051057
際際滷s from ApacheCon]]>
際際滷s from ApacheCon]]> Wed, 27 Jul 2016 05:10:57 GMT /slideshow/managing-enterprise-hadoop-clusters-with-apache-ambari-64421498/64421498 JayushLuniya@slideshare.net(JayushLuniya) Managing Enterprise Hadoop Clusters with Apache Ambari JayushLuniya 際際滷s from ApacheCon <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/managingenterprisehadoopclusterswithapacheambari-v2-160727051057-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> 際際滷s from ApacheCon
Managing Enterprise Hadoop Clusters with Apache Ambari from Jayush Luniya
]]> 368 3 https://cdn.slidesharecdn.com/ss_thumbnails/managingenterprisehadoopclusterswithapacheambari-v2-160727051057-thumbnail.jpg?width=120&height=120&fit=bounds presentation 000000 http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Apache Ambari: Simplified Hadoop Cluster Operation & Troubleshooting /slideshow/apache-ambari-simplified-hadoop-cluster-operation-troubleshooting/63625484 ambarihadoopsummit2016final-160630221627
New enterprise features in Apache Ambari 2.4.0 release for simplified cluster operation and troubleshoorting. Ambari talk at Hadoop Summit 2016.]]>
New enterprise features in Apache Ambari 2.4.0 release for simplified cluster operation and troubleshoorting. Ambari talk at Hadoop Summit 2016.]]> Thu, 30 Jun 2016 22:16:27 GMT /slideshow/apache-ambari-simplified-hadoop-cluster-operation-troubleshooting/63625484 JayushLuniya@slideshare.net(JayushLuniya) Apache Ambari: Simplified Hadoop Cluster Operation & Troubleshooting JayushLuniya New enterprise features in Apache Ambari 2.4.0 release for simplified cluster operation and troubleshoorting. Ambari talk at Hadoop Summit 2016. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ambarihadoopsummit2016final-160630221627-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> New enterprise features in Apache Ambari 2.4.0 release for simplified cluster operation and troubleshoorting. Ambari talk at Hadoop Summit 2016.
Apache Ambari: Simplified Hadoop Cluster Operation & Troubleshooting from Jayush Luniya
]]> 678 5 https://cdn.slidesharecdn.com/ss_thumbnails/ambarihadoopsummit2016final-160630221627-thumbnail.jpg?width=120&height=120&fit=bounds presentation 000000 http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Apache Ambari Stack Extensibility /slideshow/apache-ambari-stack-extensibility/63625300 apacheambaristackextensibility-160630220743
Apache Ambari Meetup - Apache Ambari Stack Extensibility]]>
Apache Ambari Meetup - Apache Ambari Stack Extensibility]]> Thu, 30 Jun 2016 22:07:43 GMT /slideshow/apache-ambari-stack-extensibility/63625300 JayushLuniya@slideshare.net(JayushLuniya) Apache Ambari Stack Extensibility JayushLuniya Apache Ambari Meetup - Apache Ambari Stack Extensibility <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/apacheambaristackextensibility-160630220743-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Apache Ambari Meetup - Apache Ambari Stack Extensibility
Apache Ambari Stack Extensibility from Jayush Luniya
]]> 1489 5 https://cdn.slidesharecdn.com/ss_thumbnails/apacheambaristackextensibility-160630220743-thumbnail.jpg?width=120&height=120&fit=bounds presentation 000000 http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 https://public.slidesharecdn.com/v2/images/profile-picture.png https://cdn.slidesharecdn.com/ss_thumbnails/futureofapacheambari-final-170616035029-191211213532-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/future-of-apache-ambari-204628726/204628726 Future of Apache Ambari https://cdn.slidesharecdn.com/ss_thumbnails/ambaridataworkssummitsydney2017-170925225847-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/manage-addon-services-in-apache-ambari/80153838 Manage Add-on Services... https://cdn.slidesharecdn.com/ss_thumbnails/mpackbasedoperationsmodel-170613054322-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/mpack-based-operations-model/76889552 Mpack Based Operations...