home
Big Data Evolution Spark Basics PySpark RDD DataFrames & Catalyst Basic Operations Data Sources & Formats Advanced Transformations Spark Core & Execution Performance Tuning Structured Streaming Delta Lake / Lakehouse Advanced Projects
About Data Modelling Types of Data Modelling Relational System - Modelling Reporting System - Modelling
Case Studies
LinkedIn System Facebook System WhatsApp System Instagram System Amazon Shopping Ride Hailing (Uber/Ola) Garage System Parking System Netflix System
About Data Governance Objectives of Data Governance Implementation Strategy (How)
Case Studies
Governance First Principles Healthcare Governance Financial Governance Retail Loyalty Governance
Introduction Architecture Virtual Warehouses
Introduction Airflow Architecture Operators & Sensors Advanced Concepts Writing First DAG Production Best Practices
Azure Data Engineer
Evolution of Data Engineering Changing Practices Mastering DE with AI Interview Scenarios AI-DLC Methodology
AWS Lambda Mechanics AWS S3 Case Study DynamoDB Mechanics
diamond Go Premium
person_outline
Hello Guest
Sign in to access all content
Information
About Us Contact Us
Settings & Preferences
App Theme
light_mode Light Mode dark_mode Dark Mode
Account
Sign In / Login
schema Data Modelling
search
  • About Data Modelling
  • Types of Data Modelling
  • Relational System Modelling
  • Reporting System Modelling
    • keyboard_arrow_down Case Studies
    Data Engineering Path  ·  Data Modelling
    UBER & RIDE-SHARING CASE STUDY

    Step 3: Relationships Explained (Uber)

    Banner

    1. Geospatial Location Strategy (Production)

    In actual production systems, writing real-time driver coordinates directly to a relational disk database is highly inefficient. Instead:

    • In-Memory Store: Driver lat/long heartbeats are sent every 4 seconds to an in-memory key-value store (like Redis).
    • Spatial Indexing: Redis uses Geohashes (e.g., GEOADD) or spatial libraries like Uber H3 (a hexagonal hierarchical spatial index) to perform rapid circular area lookups (GEORADIUS).

    2. Trip Lifecycle Constraints

    The trips table acts as a finite state machine. A trip transitions strictly through these states: Requested -> Accepted -> Arrived -> In-Progress -> Completed / Cancelled.

    lock

    This content is reserved for Premium Members.

    Upgrade to Premium
    arrow_back Previous Topic Entity Identification Next Topic arrow_forward Uber Data Model

    Entity Details

    Create New Item
    help

    Submit Technical Query

    Have a question or run into an issue? Describe it below, upload an optional screenshot, and our engineering team will answer it!

    image Attach image (optional)

    Submit Feedback

    build Free Developer Utility Free Tool
    gavel

    Privacy & Legal Disclaimer

    1. Client-Side Browser Processing

    All utility tools on DeepEngineerHub (including Image to PDF, Text Formatters, JSON Converters, and Encryptors) execute 100% locally within your client browser using WebAssembly and JavaScript. No uploaded images, text, or documents are transmitted, collected, or stored on remote servers.

    2. Limitation of Liability ("As-Is" Provision)

    Tools and services are provided free of charge for convenience and educational purposes "as-is" without warranties of any kind. DeepEngineerHub shall not be held liable for any data loss, formatting inconsistencies, or indirect damages resulting from tool usage.

    3. Open Source & Third-Party Software

    Certain utilities utilize open-source client libraries (such as jsPDF, Mermaid.js, Pyodide) licensed under MIT, Apache, or BSD open licenses. All intellectual property remains with their respective copyright holders.