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
      • πŸ“ΆSales Analytics
      • πŸ‘¨β€πŸ’»Technology Analytics
      • πŸ’ΈFinance Analytics
      • πŸ“ŠBusiness Analytics
      • 🀝Marketing Analytics
      • πŸ‡ΈπŸ‡΄Commerce Analytics
      • ☁️Weather Analytics
      • πŸ”ƒSupply 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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Data Pipelines

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Last updated 10 months ago

Organizations are striving to become data-driven, moving away from intuitive decision-making to fact-based decisions supported by data. However, many enterprises face challenges in implementing this approach because the workforce handling these tasks is often non-technical. Making data accessible for analytics involves complex technical processes. Data pipelines address this issue by simplifying data analysis for business analysts, enabling more efficient decision-making.

Pipelines in Lyftrondata

A data pipeline in Lyftrondata is a no-code data processing framework that loads data from various sources, such as databases, SaaS applications, or files, into a destination database or data warehouse. For instance, you can load data from your Facebook Ads account into a Google BigQuery data warehouse for analysis.

With just a few clicks, you can have analysis-ready data at your fingertips, without any data loss. You can even view samples of incoming data in real-time as it loads from your source into your destination.

Data Pipeline Flow