Why Graph Databases Are Not Hype, but a Working Tool
Imagine you need to build a recommendation system that takes into account not only "user bought product" but also "user's friend bought a similar product," "the product belongs to a category the user likes," "the user read articles on the topic." In a relational database, such a query turns into a dozen JOINs and runs slowly. In a graph database, it's a traversal of a few edges that executes in milliseconds. That's why graph databases (Graph DB) and Knowledge Graphs have become the standard in tasks where connections matter more than the entities themselves.
Neo4j is the most popular graph DBMS. It stores data as nodes and relationships, and uses the declarative language Cypher for queries. For example, to find a user's friends of friends, in Cypher you just write MATCH (u:User {name: 'Alice'})-[:FRIEND*2]->(fof) RETURN fof. In SQL, this would require a recursive CTE and would be less intuitive.
The "Graph Databases" course on asibiont.com is a deep dive into designing graph schemas, writing efficient Cypher queries, implementing graph algorithms (PageRank, community detection, shortest path), and integrating graphs with AI/ML pipelines. If you want not just to know the theory but to be able to build recommendation systems and Knowledge Graphs, this course is for you.
What You Will Learn in the Course
The program is built around practical skills that you can immediately apply at work.
1. Designing Graph Schemas
You will understand when a graph model outperforms a relational one, how to avoid typical mistakes (e.g., "supernodes" with millions of connections), and how to model data for specific scenarios: social networks, product catalogs, ontologies.
2. Cypher Query Language
You will master the syntax from simple MATCH and WHERE to complex variable-length patterns and subqueries. You will learn to optimize queries using PROFILE and indexes.
3. Graph Algorithms
You will explore how PageRank (node importance scoring), Louvain (community detection), and Dijkstra (shortest path) work. You will see how to apply them via the Neo4j Graph Data Science library.
4. Building Recommendation Systems
You will implement recommendations based on collaborative filtering and graph embeddings in practice. You will learn how to combine graph features with ML models.
5. Integration with AI/ML
You will learn to export graph embeddings (e.g., via Node2Vec) and use them in Python pipelines. You will understand how a Knowledge Graph improves the quality of LLM responses through structured connections.
How Learning Works on asibiont.com
The asibiont.com platform uses AI generation of personalized lessons. This is not just "text from a textbook": the neural network analyzes your starting level, goals, and pace, then builds an individual trajectory. If you are already familiar with SQL but have never written in Cypher, the system will offer more complex design tasks. If you are a beginner, you will start with the basics of graphs and gradually reach algorithms.
Learning is entirely text-based. No videos — only structured lessons, code examples, and practical assignments. Access to materials is open 24/7: you can learn at a convenient time, return to complex topics, and review what you've covered.
Why is AI learning effective?
- The neural network adapts the program to your level: no need to rewatch hours of video if you already know the topic.
- Complex concepts are explained in simple language: for example, the difference between MATCH and OPTIONAL MATCH will be shown with concrete examples.
- You get instant feedback on queries: AI checks Cypher syntax and suggests how to improve the solution.
- Practical assignments are generated for your goals: if you want to build recommendations for e-commerce, you'll get a corresponding dataset and scenario.
Who Will Benefit from the Course
The course is designed for developers, analysts, and data engineers who want to expand their toolkit. It will be especially useful for:
- Backend developers who encounter tasks involving relationship search, social graphs, or recommendations.
- Data Scientists who want to add graph features to ML models and work with Knowledge Graphs.
- Analysts who need to quickly answer questions like "which products are most often bought together with this one?"
- Technical leaders choosing a stack for a new project: you will understand when Neo4j is justified and when it's better to stay with a relational DB.
Practical Example: Recommendations in an Online Store
Suppose you have purchase data. In a relational schema, the tables are users, orders, products, order_items. To recommend a product, you need to find users who bought the same products as the target user and select products they bought that the target user hasn't yet. In SQL, this is three JOINs and a grouping. In Neo4j, the query looks like this:
MATCH (u:User {id: 123})-[:BOUGHT]->(p:Product)<-[:BOUGHT]-(other:User)-[:BOUGHT]->(rec:Product)
WHERE NOT (u)-[:BOUGHT]->(rec)
RETURN rec.name, COUNT(*) AS freq
ORDER BY freq DESC LIMIT 10;
Such a query executes quickly even on millions of nodes if indexes are properly configured. In the course, you will learn to write similar queries and optimize them.
Sources and Further Reading
- Official Neo4j documentation: neo4j.com/docs
- Cypher language specification: opencypher.org
- Neo4j Graph Data Science library: neo4j.com/docs/graph-data-science
- Article "The PageRank Citation Ranking: Bringing Order to the Web" (Page, Brin, 1998) — a classic work on the PageRank algorithm.
Start Learning Today
Graph databases are not only trendy but also practical. Skills in Neo4j and Cypher are increasingly found in job postings for backend developers and data engineers. If you want to master an in-demand tool and learn to build recommendation systems and Knowledge Graphs, the "Graph Databases" course on asibiont.com is an excellent start. AI personalization will make learning comfortable, and practical assignments will help reinforce the theory.
Go to the course page Graph Databases and start learning right now!
Comments