Data Ops

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Builds Workflow and Automate Job Execution for Data Processing Operations

Data Ops is an Apache Airflow-based workflow orchestration service that creates data processing workflow and automates job scheduling. It can be used independently in a Kubernetes Engine cluster environment of Samsung Cloud Platform or along with other application SW.

Overview

  • Easy Installation

    A web-based console allows for easy installation of Data Ops in a standard Kubernetes cluster environment and Apache Airflow and management modules are installed automatically. In addition, the Data Ops dashboard provides comprehensive monitoring of a web server and a scheduler.

  • Configuration of Dynamic Pipeline

    A pipeline is configured for data processing using Python code. Since tasks are created dynamically by working with a date-time format for scheduling data operations, users can configure the desired workflow type and scheduling.

  • Easy Workflow Management

    DAG (Direct Acyclic Graph) configuration is visualized and managed using a web-based UI, making it easier to understand parallel processes of data flow. Managing timeout of an individual task, number of retries, and priorities also become easy.

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Service Architecture

  • User → Request/Distribute Products → Data Ops Image & Chart Repository
    • Data Ops Image & Chart Repository
      Manager/ Manager Client
      Airflow/ Web Server , Scheduler, Executor
    • Data Ops Image & Chart Repository → >Kubernetes Engine
      Kubernetes Engine
      Container/ Container/ Container
  • Data Engineer → Process Data → Data Ops Image & Chart Repository

Key Features

  • Easy installation

    - Install open source Airflow in a container environment

  • GUI-based easy management

    - Easily manage Airflow settings in a container environment
    - Distribute Airflow plug-in
    - Status monitoring of Airflow services

  • Write/Schedule workflow

    - Easy scalability thanks to python-based workflow
    - Task performed automatically by a scheduler
    - Manage resource by DAG task
    - Reprocess data processing error/failure

  • Airflow components

    - Web server : Support visualization of DAG components and status, manage Airflow configurations
    - Scheduler : Orchestrate various DAG and tasks, support DAG reservation/execution
    - Executor : Provide KubernetesExecutor, a Kubernetes-based dynamic executor
    - Metadata DB : Store metadata on DAG, execution and user, role and connection, and other Airflow components

Pricing

    • Billing
    • The amount of CPU time used by the Kubernetes Engine pod of Data Ops
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