GraphDB_AWS_Neptune_Neo4J

We live in a connected world! There are no isolated pieces of information, but rich, connected domains all around us.

Welcome to the Real world of System-Of-Engagements

CONNECTED DATA

Many of the most important questions we want to ask of our data today from our Data Platform (System-of-engagement) require us to understand how things are connected – in what ways, and with what strength, weight or quality. Such as:

• Which applications and services in my network will be affected if a particular network element—a router or switch, for example—fails?
  • Do we have redundancy throughout the network for our most important customers?
• What's the quickest route between two stations on the underground?
• Which friends and colleagues do we have in common?
• What do you recommend this customer should buy, view, or listen to next?
• Which products, services and subscriptions does a user have permission to access and modify?
• What's the cheapest or fastest means of delivering this parcel from A to B?
• Which parties are likely working together to defraud their bank or insurer?
• Which institutions are most at risk of poisoning the financial markets?

This needs a Data Platform which can manage and make sense of - large volumes of variably-structured, - densely-connected data. - what depends on what; how things flow; analysing the impact of events on deep dependency chains; identifying and assessing risk; (All connected data problems) 😃 .

These are the kinds of problems for which GrphDB like Amazon Neptune is ideally suited.

P.S.: I am still a big proponent of POLYGLOT DB/ FEDERATED DATA LAKE.

Welcome to world of GraphDB!!

• Graph databases natively embraces relationships.
• While other databases compute relationships at query time through expensive JOIN operations, a Graph database stores connections alongside the data in the model.
• The Property Graph Model: A Node-Edges-Properties model to organize Domain Data and their inherent relationships. A quick glimpse of Property Graph Model-

Relational Model	Graph Model
Please click on the above image to expand	Please click on the above image to expand

• Independent of the total size of your dataset, graph databases excel at managing highly-connected data and complex queries.

• Graph DB is used for very many types of Use Cases (not just for Social Graph or Recommendation engine), such as

  • MASTER DATA MANAGEMENT
  • GRAPH BASED SEARCH
  • REAL TIME RECOMMENDATIONS
  • SOCIAL NETWORK
  • FRAUD DETECTION
  • Google Maps and Routes APIs
  • NETWORK & IT-OPERATIONS
  • IDENTITY & ACCESS MANAGEMENT
  • System-Of-Engagements

• Graph DB Offers over other RDMBS as well as NOSQL DBs

  • Intuitiveness
  • Speed
  • Agility graphs are the most natural way to model data in the real world.

Please relish Video to understand the aforementioned high level concepts in a better way.

The Quick rundown of this repo

Please note:* 

 	- This page serve just as the *Index page*.
	- Please click on hyperlinks of the respective items to delve deep into it.
	- Please keep checking your **compass (this index page)** for to seamlessly steer your way to the next mile of this wonderful journey.

• Tranformation from RDBMS Model Schema to Graph Data Model
• AWS Neptune vis-a-vis Neo4j
• Polymorphysm in AWS Neptune
• Deep dive into AWS Neptune Property Graph DB
  • AWS Neptune Cluster Setup
  • AWS Neptune Property Graph DataModel UNDERSTANDING
  • AWS Neptune Property Graph DataModel CREATE & QUERY 1 Using Gremlin Notebook
  • AWS Neptune Property Graph DataModel CREATE & QUERY 2 Using Gremlin Notebook
  • CREATE, VISUALIZE & PERDICTIVE QUERY play on SOCIAL NETWORK PG Datamodel Using Gremlin Notebook
  • Bulk Loading Data from S3 into AWS Neptune using BULK LOAD API
  • Production garde Data pipeline with Neptune as Data Sink
  • Visualize data in Amazon Neptune using VIS.js
  • Writing to Amazon Neptune from Amazon Kinesis Data Streams

AWS Neptune Property GraphDB Codesets

Following folders carry Jupyter notebook (interactive python notebook/ .ipynb) || Python Codeset as well as pseudocode || refernce blogs et al.

• Social-Network-Recommendations

• Visualize data in Amazon Neptune using VIS.js

Contact info

Feel free to contact me to discuss any issues, questions, or comments.

My contact info can be found on my GitHub page.

License

I, The DeepHiveMind, am providing code and resources in this repository to you under custom Copyright & license (Copyright 2019 DeepHiveMind & Creative Commons Legal Code CC0 1.0 Universal). Please Refer to the [Copyright 2019 DeepHiveMind License] for further details as to this. Thanks!