Training Information

Duration: 8 hours

Skill Level: Intermediate

Prerequisite Courses: Graphs & Graph Databases, Basic Cypher Querying, Data Management with Cypher

Course Description: This course introduces you to the common administration tasks for managing Neo4j Enterprise Edition, either in a development environment or in a production environment. First, you learn about what Neo4j is. Next you learn about the various architectures where Neo4j is used and you learn about the versions and editions of Neo4j where you install Neo4j Enterprise Edition.

Then you learn the basics of managing a Neo4j instance and how to perform both offline and online backups as well as using the cypher-shell utility for accessing the database from a terminal. You will also learn how to manage plugins to be used by the Neo4j instance and how to load a database using the import tool.

In this course, you will learn by performing 12 hands-on exercises using a Docker Neo4j instance.

Course Modules:

• 1. Introduction to Neo4j
• 2. Overview of Neo4j Administration
• 3. Managing a Neo4j Instance
• 4. Using cypher-shell to Manage Databases
• 5. Copying Databases
• 6. Changing the Database Location
• 7. Checking Database Consistency
• 8. Scripting to Manage Databases
• 9. Configuring Plugins
• 10. Changing the HTTP Port
• 11. Importing Data

At the completion of this course, you should be able to:

• ● Determine which deployment option to use
• ● Start a Neo4j instance
• ● Stop the Neo4j instance
• ● Set the password for the neo4j user
• ● Copy a Neo4j database
• ● Modify the location for a Neo4j database
• ● Check the consistency of a Neo4j database
• ● Create scripts for modifying a Neo4j database
• ● Manage plugins for a Neo4j database
• ● Configure the HTTP port used by the Neo4j instance/li>
• ● Perform an online backup of a Neo4j database
• ● Create a database with the import tool

Duration: 4 hours

Skill Level: Intermediate

Prerequisite Courses: Graphs & Graph Databases

Course Description:This training introduces the Neo4j GraphQL Library and covers how to build Node.js GraphQL APIs backed by Neo4j. The course focuses on the backend aspects of building a GraphQL API and does not cover concepts such as integrating GraphQL into a front-end application. Specific topics covered include:

Course Modules:

• ● What is GraphQL and how to query GraphQL APIs using GraphQL Playground
• ● How to create GraphQL type definitions using the GraphQL Schema Definition Language (SDL)
• ● How to use the Neo4j GraphQL Library to create Node.js GraphQL APIs
• ● How to protect GraphQL APIs, making use of authentication and authorization patterns
• ● How to add custom logic to the GraphQL API using Cypher and custom resolvers
• ● Making use of Cypher procedures like APOC and the Graph Data Science library in your GraphQL API

Duration: 3.5 hours

Skill Level: Beginner/intermediate

Prerequisites: Working knowledge of JavaScript, NPM and the Node.js ecosystem. We also assume that you have at least a basic knowledge of Neo4j.

Course Description: In this course, we walk through the steps to integrate Neo4j into an existing Node.js project. We will follow the steps to create an instance of the Neo4j JavaScript Driver, learn how to use sessions and transactions, and use that knowledge to replace hardcoded API responses with data from a Neo4j Sandbox instance.

Duration: 8 hours

Skill Level: Advanced

Prerequisite Courses: Graphs & Graph Databases, Basic Cypher Querying, Intermediate Cypher Querying, Data Management with Cypher, Graph Data Modeling

Course Description: This course dives deep into the inner workings of Cypher, in order to discuss strategies for optimizing query performance. This includes an exploration of Cypher query planning, and how the physical data structures affect the cost of information access.

This course makes reference to, but does NOT deeply explore, the effect of data modeling choices on query cost. That discussion is covered in the courses Graph Data Modeling and Implementing Graph Models. Instead, this course focuses on how to get best query results from an invariant model.

Note that this course is twice as long as most, at 8 hours. It is usually delivered in two parts for easier consumption.

Course Modules:

• 1. Preparing for Query Tuning
• 2. How queries work in Neo4j
• 3. Controlling Row Cardinality
• 4. Neo4j Behind the Scenes
• 5. Optimizing Property Access
• 6. Node Degree Shortcuts
• 7. Monitoring Running Queries

At the completion of this course, you should be able to:

• ● Define the terms row and DB hit in the context of Cypher querying
• ● Use EXPLAIN and PROFILE to identify weaknesses in a query plan
• ● Use Cypher tools to minimize the number of rows processed in a query
• ● Use best practices for minimizing property access
• ● Use monitoring tools to identify the underlying causes of a long-running query

Duration: 4 hours

Skill Level: Beginner

Prerequisite Course: Graphs & Graph Databases

Course Description: This course explores the philosophy and process behind creating graph models. It examines how the data structures and uses of a graph inform modeling decisions, and how these differ from the usual circumstances and decisions involved in designing a relational model.

Note that the exercises in this course do not use the Neo4j platform. Instead, we use the Arrows whiteboarding app to work entirely on conceptual modeling problems. The tools and strategies for implementing a model in Neo4j are covered in the class Implementing Graph Models.

Course Modules:

• 1. Introduction to Graph Data Modeling
• 2. Designing the Initial Graph Data Model
• 3. Graph Data Modeling Core Principles
• 4. Common Graph Structures
• 5. Refactoring and Evolving a Model

At the completion of this course, you should be able to:

• ● Describe the seven steps involved in the initial graph modeling process
• ● Define the terms intermediate node and linked list
• ● Design a graph model from scratch using Neo4j best practices

Duration: 4 hours

Skill Level: Beginner

Course Description: This course is intended to set the stage for future technical study of Neo4j. It begins by examining the context in which Graph Databases exist, including an overview of the historical evolution of database technologies and comparative analysis of Graph Databases vs. the ubiquitous Relational Database.

In addition, this course begins exploring the Neo4j platform by introducing the data structures, query language, and basic system architecture.

Graphs & Graph Databases is a prerequisite for every other Neo4j University course.

Course Modules:

• 1. Persistence Strategies
• 2. Relational vs Index-Free Adjacency
• 3. Introduction to Property Graphs
• 4. Introduction to Cypher
• 5. The Neo4j Graph Platform

At the completion of this course, you should be able to:

• ● Identify the relative strengths and weaknesses of different database types
• ● Define the term Index-Free Adjacency, and explain how it enables fast traversal of long paths
• ● Define the terms node, relationship, label, and property
• ● Define and distinguish the components of the Neo4j Platform
• ● Create and populate a graph using Neo4j Desktop and Neo4j Browser

Duration: 4 hours

Skill Level: Beginner

Prerequisite Courses: You'll need a laptop with a web browser and a Google Cloud Platform (GCP) account. If you don't already have a GCP account, we'll cover how to sign up for one in the lab. No prior knowledge of Neo4j, GCP or Vertex AI is required.

Abstract: Neo4j is the leading graph database vendor. We’ve worked closely with GCP Engineering for years. Our products, AuraDB and AuraDS are offered as managed services on GCP. Neo4j Enterprise Edition, which includes Graph Database, Graph Data Science and Bloom is offered in the Google Cloud Marketplace. In this hands on lab, you’ll get to learn about Neo4j and GCP Vertex AI. The lab is intended for data scientists and data engineers. We’ll walk through deploying Neo4j and Vertex AI on GCP in your own GCP account. Then we’ll get hands on with a real world dataset, building a machine learning pipeline that takes advantage of features generated using Neo4j Graph Data Science to improve prediction in GCP Vertex AI. You’ll come out of this lab with enough knowledge to apply graph feature engineering to your own datasets.

We’re going to analyze the quarterly filings of asset managers with $100m+ assets under management (AUM). These are regulatory filing made to the Securities and Exchange Commission’s (SEC) EDGAR system. We’re going to show how to load that data from a GCP Cloud Storage bucket into Neo4j. We’ll then explore the relationships of different asset managers and their holdings using the Neo4j Browser and Neo4j’s Cypher query language. Finally, we’ll use Neo4j Graph Data Science to create a graph embedding from our data, export that out, and run supervised learning algorithms in GCP Vertex AI. We’ll try to predict what holdings asset managers will maintain or enlarge in the next quarter. If you’re in the capital markets space, we think you’ll be interested in potential applications of this approach to creating new features for algorithmic trading, understanding tail risk, securities master data management and so on. If you’re not in the capital markets space, this session will still be useful to learn about building machine learning pipelines with Neo4j and GCP Vertex AI.

Skill Level: Beginner

Prerequisites: None - But you are most likely a practitioner that works with data

Course Description: This course is intended to demystify Graph Data Science by covering the fundamentals of graph theory and graph algorithms through easy to follow examples and exercises.

Course Modules:

• 1. Graphs in theory vs in practice
• 2. Graph algorithms
• 3. Unsupervised vs supervised learning
• 4. From data to wisdom
• 5. The Neo4j Graph Data Science Platform

At the completion of this course, you should be able to:

• ● Identify when graph data science can help
• ● Have knowledge about how to get started with Neo4j Graph Data Science platform
• ● Un-supervised learning with graph algorithms