Cypher Language Course and Graph Databases in Neo4j/ONgDB

Training process

Training needs analysis

If you have specific requirements regarding the training programme, we will carry out a training needs analysis for you. This will guide us on which aspects of the programme should receive greater emphasis, so that the training programme meets your specific needs.

What will you gain?

Graph data modeling - You will learn how to map real-world relationships into nodes, edges, and properties, so you can design a practical Neo4j or ONgDB data model for real analytical use cases.

Writing CYPHER queries - You will practice CYPHER from the ground up, including data creation, filtering, sorting, and traversals, so you can build clear queries for everyday graph database work.

Navigating graph structures - You will learn how to move through a graph efficiently and find links, paths, and patterns, making it easier to analyze relationships that are hard to capture in relational databases.

Choosing the right algorithm - You will understand when to use BFS, DFS, Dijkstra, PageRank, and related methods, so you can match the right algorithm to routing, centrality, connectivity, or recommendation tasks.

Using APOC and GDSL - You will work with APOC and GDSL libraries to run built-in procedures and graph algorithms, and read the results correctly without having to implement every method from scratch.

Interpreting analytical results - You will learn how to interpret centrality, similarity, and community detection outputs, so you can turn graph algorithm metrics into clear conclusions for business or data analysis.

Application integration - You will see how to connect a graph database with Java and JavaScript code, so you can execute queries from an application and use graph data in working software solutions.

Real-world graph use cases - You will review examples from social networks, transport, and recommendation systems, helping you recognize when a graph database is a better fit than other data models.

Training programme

1. Introduction to graph databases using Neo4j/ONgDB as an example

the importance of graph databases in data analysis,
platform architecture,
discussion of Neo4j/ONgDB as popular graph database solutions,
installation and configuration of Neo4j/ONgDB.

2. Fundamentals of the CYPHER query language

CYPHER query structure,
creating nodes and relationships in a graph database,
simple CYPHER queries for retrieving data,
selection and filtering,
data import,
traversing the graph.

3. Graph theory

basic concepts and terminology of graphs,
types of graphs: directed, undirected, weighted, labeled,
application of graphs in various fields.

4. Basic algorithms of graph data analysis – theoretical aspects

graphs: BFS (Breadth-First Search) and DFS (Depth-First Search),
shortest path: Dijkstra and Bellman-Ford,
minimum spanning tree: Kruskal and Prim,
depth-first search (DFS) and strongly connected components,
PageRank algorithm,
other graph algorithms depending on the needs and interests of the participants.

5. Fundamentals of the CYPHER query language – theory and practice in Neo4j/ONgDB

advanced CYPHER queries,
filters, conditions, sorting and limitations in CYPHER queries,
creating complex CYPHER queries, such as searching for the shortest path.

6. Practical application of graph algorithms available in the APOC and GDSL libraries and interpretation of the resulting information

use of the APOC and GDSL libraries in Neo4j/ONgDB,
practical application of graph algorithms available in the libraries,
interpretation of resulting information from graph algorithms.

7. Practical use of output information from graph algorithms in data analysis processes

data modeling in Neo4j/ONgDB,
use of output information from graph algorithms in data analysis.

8. Advanced graph analysis techniques

pattern search algorithms in graphs (e.g. subgraph pattern),
determining centrality in graphs (e.g. node centrality, betweenness centrality),
community detection algorithms in social networks,
the use of graph algorithms for recommendations.

9. Graph similarity analysis

measuring similarity between graphs,
comparing graph structures and identifying similar patterns,
application of graph similarity analysis in data analysis.

10. Examples of practical applications of graph databases

analysis of social networks and relationships between users,
modeling roads and routes in transport systems,
analysis of dependencies and connections in recommendation systems.

11. Introduction to the GraphQL query language in the context of graph databases

comparison of CYPHER and GraphQL,
creating GraphQL queries to retrieve data from a graph database.

12. Case study: Data analysis in a specific domain using Neo4j/ONgDB

13. Integration with programming languages

integration with a project in Java and JavaScript,
working with the database from the programming language level.

What are the prerequisites for participating in the training?

Database basics - You should understand records, relationships, tables, or entities and be able to read simple database queries, so moving into the graph model will be much easier for you.

Analytical thinking - You should be comfortable analyzing relationships between objects and understanding data structures, because the course covers graph modeling and interpreting algorithm outputs.

Basic coding literacy - You should be able to read simple code or scripts and follow command syntax, as part of the training includes CYPHER queries and integration with Java or JavaScript.

Computer environment skills - You should be comfortable using your operating system, installing tools, and launching applications, because the training includes setup and hands-on work with Neo4j or ONgDB.