Social Media Data Processing & Analytics Project - IT Assignment Help

Assignment Task

Overview:
The users of a social network like Facebook mainly have following two workflows where, a community user can
do various types of posts (including text, images, videos) in community that will show up in feed of other users, interact with the posts of other users (like, comment, share etc.).
User can do conversation with other users via Direct messaging or there can be various user discussion groups within that community where multiple users can do conversations.
A scalable solution has to be designed using the Big Data Technologies which can address the above mentioned workflows in an efficient way. Additionally, the solution should be able to provide useful insights/analytics.
Appropriate assumptions can be made for the events data, the analysis that needs to be done on it. Make sure all those details are also made available while submitting the solution.
Submission Requirements:
Theoretical Questions (To be submitted with elaborated diagrams & details in a document)
Prepare a suitable architecture of the system with high level details of the components involved in building the complete system.
Detailed document on the structure of data to be stored, from data flow tier to master storage to delivery tier and how the interaction between different subcomponents of the system work.
I imagined the below architecture and now implementation to be done based on this based on the questions mentioned below (3,4,5) in implementation section

Implementation Questions (To be submitted in form of Python codes and stepwise results)
Out of the two main workflows defined above
Social Media Posts complete workflow
Messenger/Conversations between users/user groups
Choose any one workflow and implement it using open source Technologies such as Kafka and Spark using Python.
Create a streaming analytics pipeline and a dashboard that shows Realtime insights of the application using any open source visualization tool.
Based on the decided workflow gather valuable data points and generate insights out of it.
Submit the python codes and stepwise results of above mentioned (3) and (4).
Note:
Make necessary assumptions regarding the users creating different data points like posts, messages, likes, comments, shares etc.
Make necessary assumptions/generate fake data, regarding the users creating different data points like posts, messages, likes, comments, shares etc.

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