In this advanced Neo4j tutorial, you will gain a mastery of the Cypher query language. Learn how to ask complex questions with ease as you discover the simplicity of Cypher. The course does have a quick cypher refresher included, but it is recommended that you already have a working knowledge of Neo4j and know the basics of the Cypher Query Language.
Course Outline
This course teaches how to design and implement a graph data model and associated queries. With a mixture of instruction and hands-on practice sessions, you’ll learn how to apply the property graph model to solve common modeling problems. You’ll also learn how to evolve an existing graph in a controlled manner to support new or changed requirements.
Skills taught:
Prerequisites
Course Outline
This course will give you a foundational knowledge of graph databases and use cases. You'll learn all the getting-started basics, including data import and creation, basic modeling, and querying. Learn to use Neo4j's powerful query language, Cypher, and how it can drastically improve your connected data problems.
Best Suited For:
Skills taught:
Prerequisites
You don’t need any previous experience with Neo4j, NOSQL databases or specific development languages
Course Outline
**This course requires some Java experience***
The transition to microservices can be an exciting change of pace for developers. But how can graphs connect domain data together from different microservices? In this session you’ll learn how to use Spring Data Neo4j and Spring Boot to create connected views of data from multiple microservices.
You’ll learn:
It's the evening before GraphConnect NYC which means it's time for our annual GraphConnect Hackathon!
The topic for this year's event is graphs-4-good.
Aside from the great feeling you will get by giving back and hacking for a good cause, you'll have the opportunity to win some amazing prizes and make great friends from all over the world!
Register: https://www.meetup.com/nycneo4j/events/242972036/
We showcase the use of Neo4j as a backend to our AI technology in eBay's virtual shopping assistant: eBay ShopBot. We will discuss how we used Neo4j as a probabilistic graph model to drive conversations. We also touch upon the key learnings for deployment and scalability in Google cloud platform. We will touch upon the following application oriented learnings of using neo4j for a year in production:
1. How we deploy at scale in GCP, how we scaled to 1TB graph data
2. How neo4j can form the backend for a conversational engine like shopbot
3. Discussions and recommendations on the use of procedures against cypher in Neo4j
4. Future plans
The Neo4j openCypher team shows Cypher for Apache Spark, with multiple graphs and composable queries. They also talk about more new features coming in openCypher. (You can get more detail on language developments at Wednesday’s NYC openCypher Meetup [link http://www.opencypher.org/event/2017/10/25/event-oc-meetup/])
Xfinity xFi is Comcast’s personalized Wi-Fi experience that is tailored to cater customers’ individual needs. In this session, Jessica will discuss the innovations she expects to be providing with the Xfinity Profile Graph, and how Neo4j connects and provides context for these features. Jessica will describe how Comcast’s smart home platform will help make customers feel safer and more comfortable, while providing personalized content and services to the entire household.
Scripps Networks Interactive is a leading developer of engaging lifestyle content in the home, food and travel categories for television, the internet and emerging platforms. Their U.S. lifestyle portfolio comprises popular television and internet brands HGTV, DIY Network, Food Network, Cooking Channel, Travel Channel and Great American Country, which collectively engage more than 190 million consumers each month, and HGTV was among the top 10 cable networks in 2016.
Managing the asset and instance-level metadata for Scripps' historical media portfolio is critical to their business. It affects everything from broadcast availability to viewing behavior to the ability to syndicate content to non-linear and international broadcast partners. In this session, the speakers will discuss:
Cypher started in Neo4j. It’s now used by SAP HANA Graph, Redis Graph and Agens Graph over PostgreSQL, among others. The Neo4j Graph Platform will include Cypher for Apache Spark, with Hadoop and other integrations, allowing the data lake to be projected into graphs. And there’s more to come …
How do you read 100,000 documents? The connection between the words we use and things and ideas that they represent can be represented as a structure. Using Neo4j this linguistic and semantic structure is developed to facilitate the large-scale analysis of text for meaning representation and automatic reading at scale. Learn how natural language processing can be implemented within Neo4j at scale to reveal actionable insights. Also, see how these structures are visualized in virtual reality.
Many enterprises have data assets stored in RDBMS and offline documents such as MS Excel. Graph database can create complex data relationships across these heterogeneous data sources for sales reps, clients, vendors, and product domains, etc. Clearly, “integration” among these systems is challenging but critical to the sustainability and success of enterprise analytics. Moreover, the collaborative whiteboard-friendly style of Graph modeling engages business stakeholders better than the arcane nature of RDBMS, etc. And complex, real-world queries are much efficient and easier to state than with traditional databases. Graph technology definitely has appeal to large enterprises’ in orchestrating and evolving data integration solutions to data management challenges.
This session will discuss major aspects of such an endeavor by sharing lessons learned regarding topics such as:
• Understanding the key use cases for Graph in the real-world of large business enterprises (“why”)
• Determining the “Business Value” of an Enterprise Graph Database program (“what”)
• Planning the roadmap on getting started via POC for technical discovery & impact analysis (“how”)