Need advice about which tool to choose?Ask the StackShare community!
Add tool
Hadoop vs Kubernetes: What are the differences?
- Architecture: Hadoop is a distributed storage and processing system mainly used for handling large-scale data analytics tasks, while Kubernetes is a container orchestration platform that automates the deployment, scaling, and management of containerized applications. Hadoop utilizes a master-slave architecture with nodes such as NameNode, DataNode, and YARN ResourceManager, whereas Kubernetes follows a master-worker architecture with components like Master, Nodes, and Pods.
- Purpose: Hadoop is specifically designed for processing and storing big data, providing tools like HDFS for storage and MapReduce for processing, whereas Kubernetes focuses on orchestrating containerized applications in a scalable and efficient manner, allowing seamless deployment and management of containers in a clustered environment.
- Resource Management: In Hadoop, resources are managed at the cluster level through YARN (Yet Another Resource Negotiator), allocating resources based on the requirements of MapReduce and other applications, while Kubernetes manages resources at a finer granularity using its scheduler to assign resources (CPU, memory) to individual containers, ensuring optimal usage and isolation.
- Fault Tolerance: Hadoop inherently provides fault tolerance mechanisms through data replication and resilient processing, tolerating node failures and ensuring data reliability, while Kubernetes offers fault tolerance by restarting failed containers, rescheduling them on healthy nodes, and supporting multi-zone deployments for increased availability.
- Monitoring and Scalability: Hadoop lacks extensive support for monitoring and scaling capabilities out of the box, often necessitating third-party tools for monitoring and scaling tasks, whereas Kubernetes includes built-in monitoring (Prometheus) and scaling features (Horizontal Pod Autoscaler), making it easier to monitor cluster health and scale applications based on demand.
In Summary, Hadoop and Kubernetes differ in architecture, purpose, resource management, fault tolerance, and monitoring, catering to distinct use cases in big data processing and container orchestration.
Decisions about Hadoop and Kubernetes
Simon Reymann
Senior Fullstack Developer at QUANTUSflow Software GmbH · | 30 upvotes · 12M views
Our whole DevOps stack consists of the following tools:
- GitHub (incl. GitHub Pages/Markdown for Documentation, GettingStarted and HowTo's) for collaborative review and code management tool
- Respectively Git as revision control system
- SourceTree as Git GUI
- Visual Studio Code as IDE
- CircleCI for continuous integration (automatize development process)
- Prettier / TSLint / ESLint as code linter
- SonarQube as quality gate
- Docker as container management (incl. Docker Compose for multi-container application management)
- VirtualBox for operating system simulation tests
- Kubernetes as cluster management for docker containers
- Heroku for deploying in test environments
- nginx as web server (preferably used as facade server in production environment)
- SSLMate (using OpenSSL) for certificate management
- Amazon EC2 (incl. Amazon S3) for deploying in stage (production-like) and production environments
- PostgreSQL as preferred database system
- Redis 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.
Manage your open source components, licenses, and vulnerabilities
Learn MorePros of Hadoop
Pros of Kubernetes
Pros of Hadoop
- Great ecosystem39
- One stack to rule them all11
- Great load balancer4
- Amazon aws1
- Java syntax1
Pros of Kubernetes
- Leading docker container management solution166
- Simple and powerful130
- Open source108
- Backed by google76
- The right abstractions58
- Scale services26
- Replication controller20
- Permission managment11
- Supports autoscaling9
- Cheap8
- Simple8
- Self-healing7
- Open, powerful, stable5
- Promotes modern/good infrascture practice5
- Reliable5
- No cloud platform lock-in5
- Scalable4
- Quick cloud setup4
- Cloud Agnostic3
- Custom and extensibility3
- A self healing environment with rich metadata3
- Captain of Container Ship3
- Backed by Red Hat3
- Runs on azure3
- Expandable2
- Sfg2
- Everything of CaaS2
- Gke2
- Golang2
- Easy setup2
Sign up to add or upvote prosMake informed product decisions
Cons of Hadoop
Cons of Kubernetes
Cons of Hadoop
Be the first to leave a con
Cons of Kubernetes
- Steep learning curve16
- Poor workflow for development15
- Orchestrates only infrastructure8
- High resource requirements for on-prem clusters4
- Too heavy for simple systems2
- Additional vendor lock-in (Docker)1
- More moving parts to secure1
- Additional Technology Overhead1
Sign up to add or upvote consMake informed product decisions
55
44.4K
824
3.9K
58.3K
- No public GitHub repository available -
What is Hadoop?
The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.
What is Kubernetes?
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.
Need advice about which tool to choose?Ask the StackShare community!
Jobs that mention Hadoop and Kubernetes as a desired skillset
What companies use Hadoop?
What companies use Kubernetes?
Manage your open source components, licenses, and vulnerabilities
Learn MoreSign up to get full access to all the companiesMake informed product decisions
What tools integrate with Hadoop?
What tools integrate with Kubernetes?
What tools integrate with Hadoop?
What tools integrate with Kubernetes?
Sign up to get full access to all the tool integrationsMake informed product decisions
Blog Posts
+1 2
1605
+4 4
1267
+2 2
1217
+9 3
4752
+11 2
2493
+8 5
2577
+4 1
817
+11 6
7144
What are some alternatives to Hadoop and Kubernetes?
Cassandra
Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.
MongoDB
MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.
Elasticsearch
Elasticsearch is a distributed, RESTful search and analytics engine capable of storing data and searching it in near real time. Elasticsearch, Kibana, Beats and Logstash are the Elastic Stack (sometimes called the ELK Stack).
Splunk
It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.
Snowflake
Snowflake eliminates the administration and management demands of traditional data warehouses and big data platforms. Snowflake is a true data warehouse as a service running on Amazon Web Services (AWS)—no infrastructure to manage and no knobs to turn.