Lyftrondata
  • Introduction
    • About Lyftrondata
    • Lyftrondata Feature
    • Lyftrondata System Architecture
      • Lyftrondata Integration Framework
      • Lyftrondata Connector Framework
    • Core Concepts
      • Data Pipelines
      • Vision and Goals
      • Sources and Destinations
        • Types of Sources
        • Types of Destination
    • Free Trial
    • Lyftrondata Apps
      • Data Loader
        • Full Load
        • Incremental Load
      • Data Mirror
        • Prerequisite
        • Integration
      • Data Vault
      • ELT
      • ETL
      • Data Analytics
    • Faq
  • Lyftrondata Connectors
    • Source
      • 📶Sales Analytics
      • 👨‍💻Technology Analytics
      • 💸Finance Analytics
      • 📊Business Analytics
      • 🤝Marketing Analytics
      • 🇸🇴Commerce Analytics
      • ☁️Weather Analytics
      • 🔃Supply Chain Analytics
      • ⏳Human Resources Analytics
    • Destinations
  • Managing Lyftrondata
    • Lyftrondata Installation
      • Requirements
      • On AWS Deployment
      • On AWS Deployment Using AMI
      • On Azure Deployment
      • On DigitalOcean Deployment
      • Deployment Info
    • Configure Lyftrondata
      • AWS S3/IAM User
      • Wasabi
      • Settings and Security
  • Developer Guides
    • Understand Lyftrondata
      • Lyftrondata Architecture
      • Libraries and Dependencies Used in Our Application
      • Services used by Lyftrondata
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Lyftrondata Architecture

This page define the Lyftrondata Architecture flow.

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Last updated 1 year ago

Component
Description

Lyftrondata Fabric (Application Server)

Lyftrondata Application server where the user manage the pipeline and configuration.

DB Server (Project Metadata DB)

Store the actual data related to application.

DB Server (Admin Metadata DB)

Store the metadata of the application.

Lyftrondata Fabric Highly Scalable (Job Server Pods)

Job server pods (multiple warehouses) where lyftrondata application deploy and run the jobs for the pipelines.

Warehouse Logs Server (S3/Wasabi/Blob/Google Storage)

A centralized system for managing and storing warehouse logs, integrated with cloud storage solutions (S3, Wasabi, Blob, Google Storage) for scalable and cost-effective storage.

DB Server (Warehouse Metadata DB)

Store and manage the data related to the jobs runs on multiple warehouses.

Notification Services (Email/Slack)

Facilitates sharing of job-related information through email and Slack notifications.

Info: For lyftrondata installation on the environment follow the

document
Lyftrondata Architecture Flow