Introduction to Graph Databases

Talk given at Data Science Meetup https://www.meetup.com/Practical-Data-Science-Workshops-Bangalore/events/237396439/

Transcript

1. Introduction
   To
   Graph Databases
   Harjindersingh Mistry,
   RedHat
   

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2. Agenda
   ● What is a Graph Database ?
   ● How is it different from RDBMS ?
   ● Applications of Graph Database
   ● Popular Graph Database Systems
   ● Graph Database in AWS
   ● Demo: Graph based Recommendation
   

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3. What is a Graph Database ?
   ● A graph is an ordered pair G = ( V, E ) where
   ○ V = set of vertices or nodes and
   ○ E = set of edges, which are 2-element
   subsets of V
   ● A graph database is the system that helps us
   store and retrieve our data in graph structure
   format.
   ● It is optimized for the dataset that exhibits
   variety of relationships and these
   relationships are important for the workload.
   

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4. How is it different from RDBMS ?
   ● RDBMS also stores entities in relational tables and relationship between
   two entities. But relationships are not stored explicitly !
   ● So, costly JOIN operation is required for collecting related items !
   Customer Table
   ID Name ...
   Product Table
   ID Name ...
   Sales Table
   Cust-ID Product-ID ...
   

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5. How is it different from RDBMS ?
   ● On the other hand, graph database stores the relationships explicitly
   ● The traversal from one data element to another element is very easy and
   lightweight !
   ● No JOIN is required.
   

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6. Applications of Graph Database
   ● Google Knowledge Graph
   ○ Resultant ‘knowledge’ of processing big data
   ○ It enhances the search experience of a user
   ● Social Networks like FaceBook, Twitter,
   LinkedIn
   ○ FaceBook uses Apache Giraph
   ○ Twitter has written its own FlockDB
   ○ LinkedIn has its own Graph Database
   

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7. Applications of Graph Database
   ● Protein Matching
   ○ Useful in pharmaceutical industry for drug discovery
   ○ Structure of a protein determines its behavior
   ○ Graph structure queries are useful in protein comparison
   ● Analysis of Call Data Records ( CDR )
   ○ CDR data mainly tells who called whom
   ○ It is a single huge graph
   ○ It can be used to narrow down crime suspects
   

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8. Popular Graph Database Systems
   Titan DB Neo4J Spark GraphX
   Apache Giraph
   

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9. Graph Database in AWS
   Titan
   Gremlin Server
   DynamoDB
   Storage Backend
   

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10. Demo
    

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11. Thanks!
    hmistry@redhat.com
    

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12. References
    ● Graph
    ○ https://en.wikipedia.org/wiki/Graph_theory#Graph
    ● Graph Database
    ○ https://en.wikipedia.org/wiki/Graph_database
    ● Graph Based Recommendation
    ○ https://developer.ibm.com/dwblog/2017/recommendation-engine-customer-insight-grap
    h-database/
    ● Google Knowledge Graph:
    ○ https://googleblog.blogspot.in/2012/05/introducing-knowledge-graph-things-not.html
    ○ https://en.wikipedia.org/wiki/Knowledge_Graph
    

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13. References
    ● LinkedIn Knowledge Graph
    ○ https://engineering.linkedin.com/blog/2016/10/building-the-linkedin-knowledge-graph
    ● Analyze CDR data to narrow down list of suspects
    ○ https://linkurio.us/how-to-use-phone-calls-and-network-analysis-to-identify-criminals/
    

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14. Images Courtesy
    ● FaceBook, Twitter, LinkedIn icons
    ○ https://goo.gl/images/xgm18q
    ● Protein
    ○ http://www.rcsb.org/pdb/images/5UGX_bio_r_500.jpg?bioNum=1
    ● Analyze CDR data to narrow down on suspects
    ○ https://goo.gl/images/6IbYVF
    ● Titan DB logo
    ○ https://goo.gl/images/0SJIPs
    ● Neo4J logo
    ○ https://goo.gl/images/dbi6IV
    

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15. Images Courtesy
    ● Apache Giraph logo
    ○ https://goo.gl/images/Vf0yLc
    ● Spark GraphX logo
    ○ http://spark.apache.org/images/spark-logo-trademark.png
    ● Amazon DynamoDB logo
    ○ https://goo.gl/images/N4SYlY
    ● Google Knowledge Graph
    ○ https://goo.gl/images/cMIQ2s
    ● Titan Data Layout
    ○ http://s3.thinkaurelius.com/docs/titan/1.0.0/images/titanstoragelayout.png
    

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