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Free Download Mastering Databases with Python SQL, NoSQL, Vector DBs
Published 8/2026
Created by Ayoub Allali
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: Arabic | Duration: 44 Lectures ( 8h 52m ) | Size: 6.7 GB
Mastering Databases with Python: SQL, NoSQL, Vector DBs
What you'll learn
⚡ Design and query relational databases using core SQL concepts including DDL, DML, DCL, TCL, joins, grouping, and sorting.
⚡ Build applications with NoSQL databases, including MongoDB (document), Redis (key-value), Neo4j (graph), and Cassandra (column-family).
⚡ Connect and perform CRUD operations across SQL and NoSQL systems natively using Python drivers and frameworks.
⚡ Store and query vector embeddings using FAISS and ChromaDB to build Retrieval-Augmented Generation (RAG) AI applications in Python.
Requirements
❗ Basic knowledge of Python programming
Description
Master modern data storage architectures and learn how to manage, query, and integrate databases using Python. This course provides a complete end-to-end journey from foundational relational models to cutting-edge AI vector search systems.
Whether you are a developer, data scientist, or AI engineer, understanding how to select, structure, and query the right database is crucial for building scalable, high-performance applications.
What You Will Learn
✨Relational Databases & SQL: Core principles of relational modeling, execution of complex DDL, DML, DCL, and TCL queries, grouping, sorting, and join operations.
✨NoSQL Paradigm: Key concepts, CAP Theorem, and practical implementation across document stores (MongoDB), key-value databases (Redis), and graph structures (Neo4j).
✨Python Database Frameworks: Native Python drivers and ORMs for seamlessly connecting Python applications to MySQL, MongoDB, Firebase, Redis, Apache Cassandra, and Neo4j.
✨Vector Databases for AI: Foundations of embeddings, vectoring frameworks, FAISS distance metrics, and ChromaDB integration for AI applications.
✨Retrieval-Augmented Generation (RAG): Hands-on implementation of RAG pipelines using modern AI architecture and NVIDIA Nemotron.
✨Specialized Data Systems: Industrial-grade time-series and IoT data handling with Apache IoTDB.
Every section combines practical concepts with real-world Python implementations. By the end of this course, you will have the skills needed to design, query, and integrate diverse database systems into modern software and AI workflows.
Who this course is for
⭐ Aspiring Database Administrators (DBAs), Data Engineers, and Database Managers.
⭐ Data Scientists and AI Engineers seeking to build Retrieval-Augmented Generation (RAG) pipelines using Vector DBs like FAISS and ChromaDB.
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