Ambari vs Kubernetes

Overview

Kubernetes

Stacks61.2K

Followers52.8K

Votes685

Ambari

Stacks45

Followers74

Votes2

Ambari vs Kubernetes: What are the differences?

Introduction

Ambari and Kubernetes are both popular technologies used for managing and orchestrating containerized applications. While they have some similarities, there are key differences that set them apart.

Deployment and Scaling: One major difference between Ambari and Kubernetes is their approach to deployment and scaling. Ambari is primarily designed for managing and provisioning Hadoop clusters, making it suitable for big data applications. It provides tools and interfaces for installing, configuring, and scaling Hadoop clusters. On the other hand, Kubernetes is a general-purpose container orchestration platform that can be used for deploying and scaling any containerized application, not limited to big data.
Container Management: Another significant difference is the level of control and management over containers. Ambari focuses more on the management of the entire Hadoop ecosystem, including the various services and components within it. It provides a high-level view of the cluster and simplifies the management of Hadoop services. In contrast, Kubernetes is more granular in its container management approach. It provides fine-grained control over individual containers, allowing for precise resource allocation, scheduling, and networking.
Service Discovery and Load Balancing: Ambari provides built-in service discovery and load balancing capabilities for Hadoop clusters. It can automatically discover and register new services, and distribute client requests to the appropriate instances. Kubernetes also offers service discovery and load balancing, but it goes a step further with its advanced networking features. It provides a flexible and robust networking model, allowing for complex network configurations and traffic routing.
Fault Tolerance and High Availability: Ambari focuses on fault tolerance and high availability within the Hadoop ecosystem. It provides monitoring, alerting, and automatic recovery mechanisms for Hadoop services. Kubernetes, on the other hand, provides fault tolerance and high availability at the container level. It can automatically restart failed containers, reschedule pods to healthy nodes, and replicate containers for increased availability.
Resource Management: Ambari offers resource management for Hadoop clusters through its integration with YARN (Yet Another Resource Negotiator). It provides tools for monitoring and managing cluster resources such as CPU, memory, and disk. Kubernetes also provides resource management capabilities, but it is more generic and agnostic to the underlying resource type. It can manage resources for any containerized application, not limited to Hadoop.
Community and Ecosystem: Ambari has a strong community and ecosystem focused on Hadoop and big data technologies. It is tightly integrated with the Apache Hadoop stack and provides support for a wide range of Hadoop components. Kubernetes, on the other hand, has a larger and more diverse community. It is not limited to a specific technology stack and is widely adopted for a broad range of containerized applications.

In summary, Ambari is specialized for managing and provisioning Hadoop clusters, while Kubernetes is a general-purpose container orchestration platform that can be used for any containerized application. Ambari focuses on managing the Hadoop ecosystem, while Kubernetes provides more granular control over containers. Both offer service discovery and load balancing capabilities, but Kubernetes has more advanced networking features. Ambari focuses on fault tolerance and high availability within the Hadoop ecosystem, while Kubernetes provides these features at the container level. Ambari integrates tightly with the Apache Hadoop stack, while Kubernetes has a larger and more diverse community.

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Advice on Kubernetes, Ambari

Simon

Senior Fullstack Developer at QUANTUSflow Software GmbH

Apr 27, 2020

Decidedon

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Markdown

Our whole DevOps stack consists of the following tools:

@{GitHub}|tool:27| (incl. @{GitHub Pages}|tool:683|/@{Markdown}|tool:1147| for Documentation, GettingStarted and HowTo's) for collaborative review and code management tool
Respectively @{Git}|tool:1046| as revision control system
@{SourceTree}|tool:1599| as @{Git}|tool:1046| GUI
@{Visual Studio Code}|tool:4202| as IDE
@{CircleCI}|tool:190| for continuous integration (automatize development process)
@{Prettier}|tool:7035| / @{TSLint}|tool:5561| / @{ESLint}|tool:3337| as code linter
@{SonarQube}|tool:2638| as quality gate
@{Docker}|tool:586| as container management (incl. @{Docker Compose}|tool:3136| for multi-container application management)
@{VirtualBox}|tool:774| for operating system simulation tests
@{Kubernetes}|tool:1885| as cluster management for docker containers
@{Heroku}|tool:133| for deploying in test environments
@{nginx}|tool:1052| as web server (preferably used as facade server in production environment)
@{SSLMate}|tool:2752| (using @{OpenSSL}|tool:3091|) for certificate management
@{Amazon EC2}|tool:18| (incl. @{Amazon S3}|tool:25|) for deploying in stage (production-like) and production environments
@{PostgreSQL}|tool:1028| as preferred database system
@{Redis}|tool:1031| as preferred in-memory database/store (great for caching)

The main reason we have chosen Kubernetes over Docker Swarm is related to the following artifacts:

Key features: Easy and flexible installation, Clear dashboard, Great scaling operations, Monitoring is an integral part, Great load balancing concepts, Monitors the condition and ensures compensation in the event of failure.
Applications: An application can be deployed using a combination of pods, deployments, and services (or micro-services).
Functionality: Kubernetes as a complex installation and setup process, but it not as limited as Docker Swarm.
Monitoring: It supports multiple versions of logging and monitoring when the services are deployed within the cluster (Elasticsearch/Kibana (ELK), Heapster/Grafana, Sysdig cloud integration).
Scalability: All-in-one framework for distributed systems.
Other Benefits: Kubernetes is backed by the Cloud Native Computing Foundation (CNCF), huge community among container orchestration tools, it is an open source and modular tool that works with any OS.

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Detailed Comparison

Kubernetes	Ambari
Kubernetes is an open source orchestration system for Docker containers. It handles scheduling onto nodes in a compute cluster and actively manages workloads to ensure that their state matches the users declared intentions.	This project is aimed at making Hadoop management simpler by developing software for provisioning, managing, and monitoring Apache Hadoop clusters. It provides an intuitive, easy-to-use Hadoop management web UI backed by its RESTful APIs.
Lightweight, simple and accessible;Built for a multi-cloud world, public, private or hybrid;Highly modular, designed so that all of its components are easily swappable	Alerts; Ambari Python Libraries; Automated Kerberizaton; Blueprints; Configurations; Service Dashboards; Metrics
Statistics
Stacks 61.2K	Stacks 45
Followers 52.8K	Followers 74
Votes 685	Votes 2
Pros & Cons
Pros 166 Leading docker container management solution 130 Simple and powerful 108 Open source 76 Backed by google 58 The right abstractions Cons 16 Steep learning curve 15 Poor workflow for development 8 Orchestrates only infrastructure 4 High resource requirements for on-prem clusters 2 Too heavy for simple systems	Pros 2 Ease of use
Integrations
Vagrant Docker Rackspace Cloud Servers Microsoft Azure Google Compute Engine Ansible Google Kubernetes Engine	Hadoop Ubuntu Debian

What are some alternatives to Kubernetes, Ambari?

Rancher

Rancher is an open source container management platform that includes full distributions of Kubernetes, Apache Mesos and Docker Swarm, and makes it simple to operate container clusters on any cloud or infrastructure platform.

Docker Compose

With Compose, you define a multi-container application in a single file, then spin your application up in a single command which does everything that needs to be done to get it running.

Grafana

Grafana is a general purpose dashboard and graph composer. It's focused on providing rich ways to visualize time series metrics, mainly though graphs but supports other ways to visualize data through a pluggable panel architecture. It currently has rich support for for Graphite, InfluxDB and OpenTSDB. But supports other data sources via plugins.

Docker Swarm

Swarm serves the standard Docker API, so any tool which already communicates with a Docker daemon can use Swarm to transparently scale to multiple hosts: Dokku, Compose, Krane, Deis, DockerUI, Shipyard, Drone, Jenkins... and, of course, the Docker client itself.

Kibana

Kibana is an open source (Apache Licensed), browser based analytics and search dashboard for Elasticsearch. Kibana is a snap to setup and start using. Kibana strives to be easy to get started with, while also being flexible and powerful, just like Elasticsearch.

Prometheus

Prometheus is a systems and service monitoring system. It collects metrics from configured targets at given intervals, evaluates rule expressions, displays the results, and can trigger alerts if some condition is observed to be true.

Tutum

Tutum lets developers easily manage and run lightweight, portable, self-sufficient containers from any application. AWS-like control, Heroku-like ease. The same container that a developer builds and tests on a laptop can run at scale in Tutum.

Portainer

It is a universal container management tool. It works with Kubernetes, Docker, Docker Swarm and Azure ACI. It allows you to manage containers without needing to know platform-specific code.

Nagios

Nagios is a host/service/network monitoring program written in C and released under the GNU General Public License.

Netdata

Netdata collects metrics per second & presents them in low-latency dashboards. It's designed to run on all of your physical & virtual servers, cloud deployments, Kubernetes clusters & edge/IoT devices, to monitor systems, containers & apps

Related Comparisons

Ambari vs Kubernetes: What are the differences?

Introduction

Ambari and Kubernetes are both popular technologies used for managing and orchestrating containerized applications. While they have some similarities, there are key differences that set them apart.

Deployment and Scaling: One major difference between Ambari and Kubernetes is their approach to deployment and scaling. Ambari is primarily designed for managing and provisioning Hadoop clusters, making it suitable for big data applications. It provides tools and interfaces for installing, configuring, and scaling Hadoop clusters. On the other hand, Kubernetes is a general-purpose container orchestration platform that can be used for deploying and scaling any containerized application, not limited to big data.
Container Management: Another significant difference is the level of control and management over containers. Ambari focuses more on the management of the entire Hadoop ecosystem, including the various services and components within it. It provides a high-level view of the cluster and simplifies the management of Hadoop services. In contrast, Kubernetes is more granular in its container management approach. It provides fine-grained control over individual containers, allowing for precise resource allocation, scheduling, and networking.
Service Discovery and Load Balancing: Ambari provides built-in service discovery and load balancing capabilities for Hadoop clusters. It can automatically discover and register new services, and distribute client requests to the appropriate instances. Kubernetes also offers service discovery and load balancing, but it goes a step further with its advanced networking features. It provides a flexible and robust networking model, allowing for complex network configurations and traffic routing.
Fault Tolerance and High Availability: Ambari focuses on fault tolerance and high availability within the Hadoop ecosystem. It provides monitoring, alerting, and automatic recovery mechanisms for Hadoop services. Kubernetes, on the other hand, provides fault tolerance and high availability at the container level. It can automatically restart failed containers, reschedule pods to healthy nodes, and replicate containers for increased availability.
Resource Management: Ambari offers resource management for Hadoop clusters through its integration with YARN (Yet Another Resource Negotiator). It provides tools for monitoring and managing cluster resources such as CPU, memory, and disk. Kubernetes also provides resource management capabilities, but it is more generic and agnostic to the underlying resource type. It can manage resources for any containerized application, not limited to Hadoop.
Community and Ecosystem: Ambari has a strong community and ecosystem focused on Hadoop and big data technologies. It is tightly integrated with the Apache Hadoop stack and provides support for a wide range of Hadoop components. Kubernetes, on the other hand, has a larger and more diverse community. It is not limited to a specific technology stack and is widely adopted for a broad range of containerized applications.

Ambari vs Kubernetes

Overview

Ambari vs Kubernetes: What are the differences?