Delegate, Automate, Dominate: Putting Graph Tech to Work for You to Unlock Hidden Insights and Opportunities

Co-presenter: Mark Heckler
Different database technologies optimize for different uses. Graph databases excel in discovering relationships, known or unknown, within vast sets of data and can help unlock value from overlooked or underutilized sources. Join the presenters in this session to discover what consideration make a dataset a candidate for graph storage and analysis. You'll also learn tips and tricks for data ingestion and structuring while gaining insights on how to build APIs that optimize for meaningful analysis of data relationships. Likewise, you'll learn how to delegate tasks to tools, automate essential but non-critical path functions, and dominate your domain with actionable insights that unlock your data's full value.

Transcript

1. Jennifer Reif Email: jennifer.reif@neo4j.com Twitter: @JMHReif LinkedIn: linkedin.com/in/jmhreif Github: github.com/JMHReif Website: jmhreif.com Delegate, Automate, Dominate Putting Graph Tech to Work for You to Unlock Hidden Insights and Opportunities Mark Heckler Email: markheckler@microsoft.com Twitter: @mkheck LinkedIn: linkedin.com/in/markheckler Github: github.com/mkheck Website: thehecklers.com
2. Who Am I? • Developer + Advocate • Continuous learner • Technical content writer • Conference speaker • Other: geek
3. Who Am I? • Author • Architect & Developer • Developer Advocate, Java/JVM Languages • Java Champion, Rockstar • Kotlin Developer Expert • Pilot bit.ly/springbootbook
4. What makes a good graph?
5. Connected data! • Mixed entity types with queries spanning multiple • Analyzing connections between entities • Changing data models and needs • Impacts/Dependencies layers deep
6. Data set?
7. Kaggle Netflix • Relationship context is important • Multiple types of entities connected • Kaggle Net fl ix set + Wikipedia country names
8. Data model
9. Import - tips and tricks
10. Load Productions LOAD CSV WITH HEADERS FROM "https://raw.githubusercontent.com/JMHReif/graph-demo-datasets/main/kaggle-net fl ix/titles.csv" as row CALL apoc.merge.node(["Production",apoc.text.capitalize(toLower(row.type))], {productionId: row.id}, {title: row.title, …}, {}) YIELD node as p WITH row, p CALL { … MERGE (g:Genre {name: apoc.text.capitalize(genre)}) MERGE (p)-[r:CATEGORIZED_BY]->(g) } WITH row, p CALL { … MERGE (c:Country {iso2Code: country}) MERGE (p)-[r2:PRODUCED_IN]->(c) } RETURN count(row); https://github.com/JMHReif/graph-demo-datasets/blob/main/kaggle-net fl ix/load-data.cypher
11. Load Production People :auto LOAD CSV WITH HEADERS FROM "https://raw.githubusercontent.com/JMHReif/graph-demo-datasets/main/kaggle-net fl ix/credits.csv" as row WITH row CALL { WITH row MERGE (p:Person {personId: row.person_id}) SET p.name = row.name WITH row, p CALL apoc.create.addLabels(p,[apoc.text.capitalize(toLower(row.role))]) YIELD node as person RETURN person } IN TRANSACTIONS OF 20000 ROWS RETURN count(row); https://github.com/JMHReif/graph-demo-datasets/blob/main/kaggle-net fl ix/load-data.cypher
12. Top to Bottom • 2 CSV fi les • Add country name with Wikipedia • Use Cypher + APOC magic • Start in small pieces • :auto IN TRANSACTIONS to batch • Multiple statements to conserve memory • Can be scripted • Also can schedule (using APOC)
13. Let’s build an API! Demo time!
14. Automate • Platforming • Con fi guration • Deployment • Idempotent • Monitoring • Management
15. Actionable insights Multiple levels, multiple perspectives • Data • System of systems • Platform
16. Resources • Source code: github.com/HecklerReifCollab/person-service • Data set: github.com/JMHReif/graph-demo-datasets/tree/main/kaggle-net fl ix • Neo4j AuraDB: dev.neo4j.com/aura • Azure Spring Apps: aka.ms/azurespringapps Jennifer Reif Email: jennifer.reif@neo4j.com Twitter: @JMHReif LinkedIn: linkedin.com/in/jmhreif Github: GitHub.com/JMHReif Website: jmhreif.com Mark Heckler Email: markheckler@microsoft.com Twitter: @mkheck LinkedIn: linkedin.com/in/markheckler Github: github.com/mkheck Website: thehecklers.com