---
title: "Inside Qwasar: Engineering Lab Project - Building a Vector Database from Scratch"
description: Learn how Qwasar learners build vector databases from scratch using distributed systems, AI tools, and modern backend technologies in hands-on labs.
image: https://blog.qwasar.io/hubfs/Inside%20Qwasar%20Engineering%20Lab%20Project%20-%20Building%20a%20Vector%20Database%20from%20Scratch.png
---

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# [Inside Qwasar: Engineering Lab Project - Building a Vector Database from Scratch](https://blog.qwasar.io/blog/inside-qwasar-engineering-lab-project-building-a-vector-database-from-scratch)

 Aug 19, 2025 9:00:00 AM / by [Caitlin Carlton](https://blog.qwasar.io/blog/author/caitlin-carlton)

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At Qwasar, we don't just teach software engineering: we train future engineers to solve problems that matter in the real world. In an effort to bring the real world into our Master’s curriculum, we created “Engineering Labs” - a virtual software engineering lab, similar to an architect’s studio or a hardware engineer’s on-site lab.

As with a traditional on-site lab, students are given a project that requires hands-on work to build a finished product. Labs are also an opportunity to work with tools used on the job by engineers and developers that may not be covered in the curriculum.

One standout example? Our **Vector Database Engineering Lab**, a Level 10 project that blends machine learning, distributed systems, and modern backend engineering into one powerful, practical challenge.

The task: design and implement a **scalable, fault-tolerant vector database** capable of performing fast similarity searches in high-dimensional space

### **Why Vector Databases Matter**

With the explosion of AI tools, recommendation systems, and unstructured data (like images, audio, and user behavior), traditional databases are hitting their limits. Searching for similar content—think “find me an image like this one” or “suggest similar songs”, requires a different kind of data storage and retrieval approach.

Enter the **vector database**: a type of system optimized for storing and querying data points represented as vectors, often derived from machine learning models. These systems power core features at companies like Spotify, Google, OpenAI, and Pinterest,  and our learners are building them from scratch.

### **What Learners Build**

This lab is designed to stretch learners technically and conceptually. Over the course of the project, students must:

- Implement **KD-Trees** and **Locality Sensitive Hashing (LSH)** to bucket and index vectors for similarity search.
- Build out a **distributed architecture** using **data sharding** and **replication** to ensure scalability and fault tolerance.
- Ensure sub-second **query speeds** for nearest-neighbor lookups.
- Deploy with tools like **Docker** and **Kubernetes**, and work with languages like **Python, Go, Rust, and C++**.
- Integrate systems using technologies like **FoundationDB**, **SciPy**, and **PyTorch**.

Students are expected to draw on knowledge of **SQL and NoSQL databases**, **machine learning basics**, **LLM tooling**, and **data structures and algorithms**, including graphs, depth-first search (DFS), and breadth-first search (BFS).

### **More Than Code, It’s Engineering**

This Engineering Lab is a great example of how hands-on learning outperforms traditional exams and lectures. It's not about memorizing definitions or passing tests; it's about **designing systems** that could power real applications. Whether the end use is **anomaly detection**, **AI-driven recommendations**, or **multimedia search**, this project builds the mindset of a systems-level engineer, provides a potential portfolio piece for the resume, and ensures learners retain the knowledge and skills covered in this project.

At a difficulty level of 10, this lab is reserved for advanced learners in Season 3 of our software engineering, AI/ML, and backend development tracks. It’s where theory meets the grind of architecture, implementation, debugging, and iteration.

### **Why We Do Engineering Labs At Qwasar**

Labs like this are exactly why Qwasar learners are landing roles at companies like Tesla, LinkedIn, Microsoft, Zoom, and Capgemini. Our grads don’t just “know” concepts, they’ve applied them in full-stack builds, peer-reviewed systems, and high-performance codebases. They’ve faced ambiguity, asked tough technical questions, and emerged with more than just a final project, they’ve gained confidence, competence, and the ability to engineer at scale.

At Qwasar, we train resourceful engineers, not just developers. We focus on the skills that don’t go out of date: problem-solving, collaboration, architecture design, and technical grit.

Recruiters want candidates who have demonstrated and proven experience of actually building software - not of passing exams and memorization. They also don’t want to see the same projects over and over again. Our Labs provide an opportunity for learners to distinguish themselves amongst other candidates.

If you're wondering what kind of challenges we tackle at Qwasar, this is it. If you're ready to go beyond bootcamps and theory, and start working on the kinds of problems real engineers face at top-tier companies, then you're in the right place.

**The future of tech is being built with vectors and our learners are already building it.**

 Tags: [Hands-On Learning](https://blog.qwasar.io/blog/topic/hands-on-learning), [vector database](https://blog.qwasar.io/blog/topic/vector-database), [engineering lab project](https://blog.qwasar.io/blog/topic/engineering-lab-project), [hands-on software engineering](https://blog.qwasar.io/blog/topic/hands-on-software-engineering), [real-world coding challenges](https://blog.qwasar.io/blog/topic/real-world-coding-challenges)

![Caitlin Carlton](https://blog.qwasar.io/hs-fs/hubfs/caitlin_image.jpeg?width=100&height=100&name=caitlin_image.jpeg)

#### Written by [Caitlin Carlton](https://blog.qwasar.io/blog/author/caitlin-carlton)

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