Building a Fully Automated ETL Pipeline with Docker & Neo4j For a GraphRAG Chatbot
In our last video, we laid the groundwork by creating a basic data ingestion pipeline for Neo4j, manually inputting data into the database. While this method was great for learning, it's not ideal for larger, more complex datasets or real-world applications.
In this video, we take it up a notch by developing a fully automated Extraction, Transformation, and Load (ETL) pipeline. We'll harness the power of Docker and Docker Compose to streamline the entire process, ensuring scalability and efficiency in your data workflow.
By the end of this tutorial, you'll have a solid ETL pipeline that simplifies data processing and scales effortlessly with your needs. Whether you're dealing with big data or just looking to optimize your current setup, this video has got you covered. Let’s dive in and start building this essential component of your data infrastructure!
Git Repo:
https://github.com/Princekrampah/neo4...
Medium Articles:
First video's article:
/ building-a-graphrag-from-scratch-neo4j-csv...
Second video's article:
/ building-an-extract-transform-and-load-pip...
Third video's article:
/ learning-the-basics-of-langchain-for-neo4j...
Forth video article:
/ natural-language-to-cypher-with-agentic-ai...
Fifth video's article:
/ agentic-graphrag-with-neo4j-and-langchain
Sixth video's article:
/ graphrag-with-neo4j-building-containerized...
Previous Video:
• From CSV To GraphRAG Systems With Neo4j An...
💡 All Videos in Series:
• GraphRAG Systems
Tags:
#DataIngestion #ETLPipeline #DockerTutorial #Neo4j #DataAutomation #TechTutorial #BigData #DataPipeline #PythonProgramming #BackendDev #DatabaseManagement #ScalableSolutions #SoftwareEngineering #DockerCompose #PythonTips #AdvancedETL #DataEngineering #TechTips #DataScience #Coding #CodeWithPrince
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