{"id":9785,"date":"2024-04-15T11:13:17","date_gmt":"2024-04-15T19:13:17","guid":{"rendered":"https:\/\/live-cometml.pantheonsite.io\/?p=9785"},"modified":"2025-04-29T12:33:03","modified_gmt":"2025-04-29T12:33:03","slug":"llm-twin-3-change-data-capture","status":"publish","type":"post","link":"https:\/\/www.comet.com\/site\/blog\/llm-twin-3-change-data-capture\/","title":{"rendered":"I Replaced 1000 Lines of Polling Code with 50 Lines of CDC Magic"},"content":{"rendered":"\n

Welcome to Lesson 3 of 12 in our free course series, LLM Twin: Building Your Production-Ready AI Replica. You\u2019ll learn how to use LLMs, vector DVs, and LLMOps best practices to design, train, and deploy a production ready \u201cLLM twin\u201d of yourself. This AI character will write like you, incorporating your style, personality, and voice into an LLM. For a full overview of course objectives and prerequisites, start with Lesson 1. <\/a><\/em><\/p>\n\n\n\n

Lessons<\/strong><\/p>\n\n\n\n

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  1. An End-to-End Framework for Production-Ready LLM Systems by Building Your LLM Twin<\/a><\/li>\n\n\n\n
  2. Your Content is Gold: I Turned 3 Years of Blog Posts into an LLM Training<\/a><\/li>\n\n\n\n
  3. I Replaced 1000 Lines of Polling Code with 50 Lines of CDC Magic<\/a><\/li>\n\n\n\n
  4. SOTA Python Streaming Pipelines for Fine-tuning LLMs and RAG \u2014 in Real-Time!<\/a><\/li>\n\n\n\n
  5. The 4 Advanced RAG Algorithms You Must Know to Implement<\/a><\/li>\n\n\n\n
  6. Turning Raw Data Into Fine-Tuning Datasets<\/a><\/li>\n\n\n\n
  7. 8B Parameters, 1 GPU, No Problems: The Ultimate LLM Fine-tuning Pipeline<\/a><\/li>\n\n\n\n
  8. The Engineer\u2019s Framework for LLM & RAG Evaluation<\/a><\/li>\n\n\n\n
  9. Beyond Proof of Concept: Building RAG Systems That Scale<\/a><\/li>\n\n\n\n
  10. The Ultimate Prompt Monitoring Pipeline<\/a><\/li>\n\n\n\n
  11. [Bonus] Build a scalable RAG ingestion pipeline using 74.3% less code<\/a><\/li>\n\n\n\n
  12. [Bonus] Build Multi-Index Advanced RAG Apps<\/a><\/li>\n<\/ol>\n\n\n\n

    We have changes everywhere. Linkedin, Medium, Github, Substack can be updated everyday.<\/p>\n\n\n\n

    To be able to have or Digital Twin up to date we need synchronized<\/strong> data.<\/p>\n\n\n\n

    What is synchronized data?<\/p>\n\n\n\n

    Synchronized data<\/strong> is data that is consistent and up-to-date across all systems and platforms it resides on or interacts with. It is the result of making sure that any change made in one dataset is immediately reflected in all other datasets that need to share that information.<\/p>\n\n\n\n

    Change Data Capture(CDC)\u2019s primary purpose <\/strong>is to identify and capture changes made to database data, such as insertions, updates, and deletions.<\/p>\n\n\n\n

    It then logs these events and sends them to a message queue, like RabbitMQ. This allows other system parts to react to the data changes in real-time by reading from the queue, ensuring that all application parts are up-to-date.<\/p>\n\n\n\n

    Today, we will learn how to synchronize a data pipeline and a feature pipeline by using CDC pattern.<\/p>\n\n\n\n

    \"\"\/
    Integrating CDC for Enhanced Data Consistency in LLM System Architecture<\/figcaption><\/figure>\n\n\n\n

    Table of Contents<\/h2>\n\n\n\n
      \n
    1. CDC pattern \u2014 Overview<\/a><\/li>\n\n\n\n
    2. CDC pattern \u2014Digital Twin Architecture Use Case<\/a><\/li>\n\n\n\n
    3. CDC with MongoDB<\/a><\/li>\n\n\n\n
    4. RabbitMQ Message Broker<\/a><\/li>\n\n\n\n
    5. Hands-on CDC: Mongo+RabbitMQ<\/a><\/li>\n\n\n\n
    6. Running the CDC microservice<\/a><\/li>\n<\/ol>\n\n\n\n

      1. CDC pattern \u2013 Overview<\/h2>\n\n\n\n

      Change Data Capture<\/strong>, commonly known as CDC, is an efficient way to track changes in a database.<\/p>\n\n\n\n

      The purpose of CDC is to capture insertions, updates, and deletions applied to a database and to make this change data available in a format easily consumable by downstream applications.<\/p>\n\n\n\n

      Why do we need CDC pattern?<\/strong><\/h3>\n\n\n\n