Hadoop vs Kubernetes

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Hadoop vs Kubernetes: What are the differences?

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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 · 8.9M 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.
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Pros of Hadoop
Pros of Kubernetes
  • 39
    Great ecosystem
  • 11
    One stack to rule them all
  • 4
    Great load balancer
  • 1
    Amazon aws
  • 1
    Java syntax
  • 164
    Leading docker container management solution
  • 128
    Simple and powerful
  • 106
    Open source
  • 76
    Backed by google
  • 58
    The right abstractions
  • 25
    Scale services
  • 20
    Replication controller
  • 11
    Permission managment
  • 9
    Supports autoscaling
  • 8
    Cheap
  • 8
    Simple
  • 6
    Self-healing
  • 5
    No cloud platform lock-in
  • 5
    Promotes modern/good infrascture practice
  • 5
    Open, powerful, stable
  • 5
    Reliable
  • 4
    Scalable
  • 4
    Quick cloud setup
  • 3
    Cloud Agnostic
  • 3
    Captain of Container Ship
  • 3
    A self healing environment with rich metadata
  • 3
    Runs on azure
  • 3
    Backed by Red Hat
  • 3
    Custom and extensibility
  • 2
    Sfg
  • 2
    Gke
  • 2
    Everything of CaaS
  • 2
    Golang
  • 2
    Easy setup
  • 2
    Expandable

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Cons of Hadoop
Cons of Kubernetes
    Be the first to leave a con
    • 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
    • 1
      Additional vendor lock-in (Docker)
    • 1
      More moving parts to secure
    • 1
      Additional Technology Overhead

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    - 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.

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    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.
    See all alternatives