Welcome to Under The Hood! This video series allows you to pop open the top of our graph technology for a peek at how it improves the way developers and data scientists manage and make sense of data.
Our technology leaders will cover a variety of topics tailored for those who are new to graph databases, as well as help seasoned users stay current with the latest innovations. Our goal is to help you take advantage of all that graph databases have to offer.
We hope you enjoy the series and encourage you to sign up for new episode alerts.
Graph database technology has transformed businesses worldwide, from fraud detection to supply chain management. At the core of this innovation is the idea that relationships between the data are just as important as the data itself. Get ready to learn about the foundational concepts of graph databases to begin your journey.
What does it mean to use graphs to store data and how is this different from other databases? In this episode, we introduce the concept of a data model, explain its implications, and show an example of how graph databases offer a simple solution to complex problems, like an application-database model mismatch.
Efficient query processing, memory, and CPU management are all essential components of a reliable system. They make Neo4j a reliable and performant graph DBMS, suitable for use as a generic application storage backend or system of record. Learn the components of Neo4j’s secure and high-performance compliant system.
Scalability brings significant challenges to graph databases, whether its data volume or resource utilization. We will explore how to reframe this complexity in a more useful way and look at sources and patterns to show how it manifests in graphs. Learn how Neo4j helps you manage and take advantage of this complexity.
Complexity increases over time and puts more strain on your database. Gain insights from complex datasets by extracting knowledge at the data model level. We will walk through an example of user recommendations to show you how to identify friction points that may appear between the application and the database.
The graph model, unlike a schema-based relational database, represents the new entity as just another path that users can connect to others. An advantage is that the query remains unchanged, and the complexity of managing the data is hidden, leaving you with the power of connected data without complicating user experience.
Graph composition is one of the most advanced concepts for graph databases. We will explore what it is and review how composition feeds into scalability needs and builds the foundation for transactional analytics. As part of this discussion, we will show you how to seamlessly combine multiple databases.
The graph data model presents unique analytics opportunities. Neo4j is built to be the perfect full-spectrum database. It treats every query equally, without assumptions about where on the spectrum of online transaction processing (OLTP) to online analytical processing (OLAP) it may lie. Learn how graph databases excel at both.
Graphs are exciting because they let us look at data, databases, and knowledge in a variety of new ways, which inspires new ideas. Now that you’ve learned about some of the problems the Neo4j graph data platform solves for you and your organization, we'll walk through the future potential of graph databases.
