Data Pipeline
  • Data Pipeline
    • About Data Pipeline
    • Design Philosophy
    • Low Code Visual Authoring
    • Real-time and Batch Orchestration
    • Event based Process Orchestration
    • ML and Data Ops
    • Distributed Compute
    • Fault Tolerant and Auto-recovery
    • Extensibility via Custom Scripting
  • Getting Started
    • Homepage
      • List Pipelines
      • Create
        • Creating a New Pipeline
          • Adding Components to Canvas
          • Connecting Components
            • Events [Kafka and Data Sync]
          • Memory and CPU Allocations
        • Creating a New Job
          • Job Editor Page
          • Task Components
            • Readers
              • HDFS Reader
              • MongoDB Reader
              • DB Reader
              • S3 Reader
              • Azure Blob Reader
              • ES Reader
              • Sandbox Reader
            • Writers
              • HDFS Writer
              • Azure Writer
              • DB Writer
              • ES Writer
              • S3 Writer
              • Sandbox Writer
              • Mongodb Writer
              • Kafka Producer
            • Transformations
          • PySpark Job
          • Python Job
      • List Jobs
      • List Components
      • Delete Orphan Pods
      • Scheduler
      • Data Channel
      • Cluster Event
      • Trash
      • Settings
    • Pipeline Workflow Editor
      • Pipeline Toolbar
        • Pipeline Overview
        • Pipeline Testing
        • Search Component in Pipelines
        • Push Pipeline (to VCS/GIT)
        • Pull Pipeline
        • Full Screen
        • Log Panel
        • Event Panel
        • Activate/Deactivate Pipeline
        • Update Pipeline
        • Failure Analysis
        • Pipeline Monitoring
        • Delete Pipeline
        • Pipeline Component Configuration
        • Pipeline Failure Alert History
      • Component Panel
      • Right-side Panel
    • Testing Suite
    • Activating Pipeline
    • Monitoring Pipeline
    • Job Monitoring
  • Components
    • Adding Components to Workflow
    • Component Architecture
    • Component Base Configuration
    • Resource Configuration
    • Intelligent Scaling
    • Connection Validation
    • Readers
      • S3 Reader
      • HDFS Reader
      • DB Reader
      • ES Reader
      • SFTP Stream Reader
      • SFTP Reader
      • Mongo DB Reader
        • MongoDB Reader Lite (PyMongo Reader)
        • MongoDB Reader
      • Azure Blob Reader
      • Azure Metadata Reader
      • ClickHouse Reader (Docker)
      • Sandbox Reader
      • Azure Blob Reader
    • Writers
      • S3 Writer
      • DB Writer
      • HDFS Writer
      • ES Writer
      • Video Writer
      • Azure Writer
      • ClickHouse Writer (Docker)
      • Sandbox Writer
      • MongoDB Writers
        • MongoDB Writer
        • MongoDB Writer Lite (PyMongo Writer)
    • Machine Learning
      • DSLab Runner
      • AutoML Runner
    • Consumers
      • SFTP Monitor
      • MQTT Consumer
      • Video Stream Consumer
      • Eventhub Subscriber
      • Twitter Scrapper
      • Mongo ChangeStream
      • Rabbit MQ Consumer
      • AWS SNS Monitor
      • Kafka Consumer
      • API Ingestion and Webhook Listener
    • Producers
      • WebSocket Producer
      • Eventhub Publisher
      • EventGrid Producer
      • RabbitMQ Producer
      • Kafka Producer
      • Synthetic Data Generator
    • Transformations
      • SQL Component
      • Dateprep Script Runner
      • File Splitter
      • Rule Splitter
      • Stored Producer Runner
      • Flatten JSON
      • Email Component
      • Pandas Query Component
      • Enrichment Component
      • Mongo Aggregation
      • Data Loss Protection
      • Data Preparation (Docker)
      • Rest Api Component
      • Schema Validator
    • Scripting
      • Script Runner
      • Python Script
        • Keeping Different Versions of the Python Script in VCS
    • Scheduler
  • Custom Components
  • Advance Configuration & Monitoring
    • Configuration
      • Default Component Configuration
      • Logger
    • Data Channel
    • Cluster Events
    • System Component Status
  • Version Control
  • Use Cases
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  1. Getting Started
  2. Homepage
  3. Create

Creating a New Pipeline

This section provides the steps involved in creating a new Pipeline flow.

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Check out the below-given illustration on how to create a new pipeline.

  • Navigate to the Create Pipeline or Job interface.

  • Click the Create option provided for Pipeline.

  • The New Pipeline window opens asking for the basic information.

  • Enter a name for the new Pipeline.

  • Describe the Pipeline (Optional).

  • Select a resource allocation option using the radio button- the given choices are:

    • Low

    • Medium

    • High

Please Note: This feature is used to deploy the pipeline with high, medium, or low-end configurations according to the velocity and volume of data that the pipeline must handle. All the components saved in the pipeline are then allocated resources based on the selected Resource Allocation option depending on the component type (Spark and Docker).

  • Click the Save option to create the pipeline. By clicking the Save option, the user gets redirected to the pipeline workflow editor.​

  • A success message appears to confirm the creation of a new pipeline.

  • The Pipeline Editor page opens for the newly created pipeline.

  • Resource allocation can be changed anytime by clicking on top left edit icon near pipeline name.​

Creating a New Pipeline
Creating a new pipeline