Building Dashboards with Neo4j Aura
Build and share interactive dashboards in Neo4j Aura. Visualize graph data with AI-generated and Cypher-powered cards, charts, filters, and maps.
Start your Neo4j learning path with the fundamentals: graph database concepts, Cypher queries, data modeling, importing data, and graph analytics.
Every Neo4j skill rests on the same base: knowing what a graph database is, querying it with Cypher, designing a model that matches the questions you want to ask, and loading your data in. Take the core sequence in order — Neo4j Fundamentals, Cypher Fundamentals, Graph Data Modeling Fundamentals, and Importing Data Fundamentals — each one short, hands-on, and assuming nothing beyond the course before it. Then branch by interest into Aura, graph analytics, or generative AI.
Build and share interactive dashboards in Neo4j Aura. Visualize graph data with AI-generated and Cypher-powered cards, charts, filters, and maps.
Discover how graph databases work and get hands-on with Neo4j: learn graph theory, nodes, relationships, and write your first Cypher queries.
Learn Cypher, the query language for Neo4j. Read and write graph data, match patterns of nodes and relationships, and build queries in under an hour.
Design graph data models for Neo4j using proven best practices. Turn application questions into nodes, relationships, and properties that scale.
Learn Aura Graph Analytics: on-demand, session-based graph compute. Run graph algorithms from Cypher, Python, or any driver, isolated from your DB.
Import data into Neo4j with confidence. Load CSV files, map relationships, and use Cypher and the Data Importer to build a graph from your own data.
Master Cypher aggregation in Neo4j: count, collect, sum, avg, min, max, and percentile functions, plus lists and pattern comprehension, hands-on.
Learn Neo4j AuraDB, the fully managed graph database service. Create a cloud instance, import data, query with Cypher, and manage backups in Aura.
Learn Neo4j Graph Data Science fundamentals hands-on: project graphs, run and configure graph algorithms, and interpret results on a real movie dataset.