
Discover how graph databases model real-world relationships with nodes and edges, compare with relational and key-value stores, and learn cipher queries, data import, and building a recommendation app.
Meet the author behind Evergreen Technologies, sharing expertise in computer vision, natural language processing, and Elasticsearch through beginner-friendly courses that build strong fundamentals in image processing and machine learning.
Discover graph database properties: intuitiveness, speed, and agility—through a social network example, real-world use cases with eBay and Walmart, and readable cypher queries.
Install and activate the new 4G software by downloading, entering the license key, and set up; then create databases, add plugins, and import data for graph databases.
Launch a new graph database named cinema in Neo4j, start it, and open the Neo4j browser to inspect an empty schema before creating nodes and edges in a future lecture.
Model a cinema graph with person and movie nodes, define properties like name, year, title, and tagline, and connect them via acted in, directed, and produced relationships with role details.
Insert a sample movie and actors in Neo4j, then add directors and edge relationships like roles and ages, using cypher queries and scripts to visualize the full movie graph.
Learn to read a movie graph in Neo4j and query data with Cypher using match and return to explore movies, actors, and directors.
Learn to update and delete graph data in Neo4j by creating nodes and relationships, setting properties, and removing relationships or nodes with detach delete.
Import csv into a neo4j database by loading employee, people, and company data, creating nodes and relationships, and parsing multi-valued skills per employee.
Explore data aggregation in Neo4j by computing average ages, counts, distinct nodes, and max/min values, using collect, distinct, and standard deviation to analyze a sample graph.
Explore real-time recommendations with advanced cypher queries in a Neo4j cinema graph, using multi-hop traversals and shortest-path reasoning to connect Kevin Bacon, Tom Hanks, and Tom Cruise.
Master the fundamentals of graph databases with Neo4j, including graph modeling, installation, and importing data. Use Cypher to perform create, read, update, and delete operations and build movie recommendation scenarios.
Graph databases are gaining popularity these days because of their ease of use, simplicity, and agility. As compared to relational or document databases, graph databases are great at expressing real-world data models with same set of semantics as a product manager would express. Unlike document databases, it supports transactional operations. Unlike relational databases, it supports fast querying across large databases.
eBay is successfully using Neo4J graph databases for optimizing routes for local delivery. Walmart is using the Neo4J database for real-time local recommendations. Graph databases are also popular for fraud detection use cases.
In this course, you will learn the fundamentals of graph databases. No prior background in a graph database is assumed, which makes it ideal for beginner developers. I'll cover the rationale behind using a graph database.
Content and Overview
This course teaches you how to build a graph database using Neo4J. You will work along with me step by step to build the following answers
Introduction to Graph databases
Introduction to Neo4j
Build real-world recommendations using Neo4j
What am I going to get from this course?
Learn to build a recommendation engine from a professional trainer from your desk.
Over 10 lectures teaching you how to build a recommendation engine using a graph database
Suitable for beginner programmers and ideal for users who learn faster when shown.
Visual training method, offering users increased retention and accelerated learning.
Breaks even the most complex applications down into simplistic steps.
Offers challenges to students to enable the reinforcement of concepts. Also, solutions are described to validate the challenges.
At the end of the course you'll be able to demonstrate your command of graph databases and will be ready to build brand-new applications. A graph database is also useful for solving Graph Neural network challenges.