Update assingment_4.md

assignments/assingment_4.md

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+
 # Assignment 4
 
+Hey there! Welcome to Knowledge Lens Intern Training Program.
 
+This Assignment will serve as a quick refresher on the usage of NoSQL and Time-series databases.
+There are three tasks in this assignment, on completion of which you'll learn:
+*  How to interact with Mongo DB
+*  Using Pandas Dataframe and generating your own excel reports
+*  Leveraging Kairos Time-series database for data ingestion and querying the same
+*  Publishing and Consuming messages via MQTT protocol
+*  Caching mechanism using Redis DB 
 
-## Task 1: Working with Mongo - Advanced
+Happy Coding! :tada:
 
+## :pushpin: Task 1: Working with Mongo - Advanced
 
-## Areas covered:
+
+### :golf: Areas covered:
 - Working with NoSQL
 - Working with Pandas
 
-## Description:
-You are given with a semester deatils in a JSON format, write FAST APIs for the below  : 
+### :books: Description:
+You are given with semester details in a JSON format, write FAST APIs for the below  : 
 
 1. To accept the semester details JSON and insert it in a collection
 2. To get sum and average of all marks filerter by any of "student_id", "batch_id", "semster_id", "subject_id"
@@ -80,32 +92,35 @@ Sample Document:
 }
 ```
 
-Note: Perform all filter operations on Mongo itself
+Note: Perform all filter operations on Mongo.
+Bonus Points: Use Mongo Aggregate framework,
 
-### Tools to use: 
+### :wrench: Tools to use: 
 1. Pycharm / VSCode
 2. Robo3T / Studio3T / MongoDB Compass
 3. PyMongo
 
 
-### Reference:
-https://www.mongodb.com/docs/manual/tutorial/query-documents/
-https://www.mongodb.com/docs/manual/reference/operator/aggregation-pipeline/
-https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_excel.html
-https://fastapi.tiangolo.com/advanced/custom-response/#fileresponse
-https://pymongo.readthedocs.io/en/stable/
+### :mag: References:
+* [Querying Documents on Mongo](https://www.mongodb.com/docs/manual/tutorial/query-documents/)
+* [Quick Summary on Mongo Aggregation Stages](https://www.mongodb.com/docs/manual/reference/operator/aggregation-pipeline/)
+* [Generating Excel Sheets from a Pandas Dataframe](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_excel.html)
+* [How to return files on FastAPI response](https://fastapi.tiangolo.com/advanced/custom-response/#fileresponse)
+* [PyMongo Official Documentation](https://pymongo.readthedocs.io/en/stable/)
+
 
 
-________________________________________________________
-## Task 2: Working with Timeseries
+_________________________________
 
+## :pushpin: Task 2: Working with Time-series
 
-## Areas covered:
+
+### :golf: Areas covered:
 - Timeseries Operation
 - Working with Timeseries
 - Working with Pandas
 
-## Description:
+### :books: Description:
 You are given with a dataset of temperature in the form of a CSV file. The end goal of the project is to create an API interface that will provide the following: 
 
 1. Get daily, weekly and monthly aggregate (min, max, and average) of the data filter by "good" data points and generate report in Excel format.
@@ -120,26 +135,40 @@ Sample Document:
 |2022-04-03 00:02:00.000 | 111.32| good |
 |2022-04-03 00:03:00.000 | 114.98| bad  |
 
-### Tools to use: 
+### :wrench: Tools to use: 
 1. Pycharm / VSCode
 2. Pandas 
-3. Kairosdb
+3. Kairos
+
+### :mag: References:
+* [How to query Kairos DB using Metrics](https://kairosdb.github.io/docs/restapi/QueryMetrics.html)
+
 
-### Reference:
-https://kairosdb.github.io/docs/restapi/QueryMetrics.html
-https://pypi.org/project/kairosdb-python/
+------------------------------------------------------
 
-________________________________________________________
-## Task 3: Working with MQTT
+## :pushpin: Task 3: Working with MQTT & REDIS
+
+### :golf: Areas covered:
+- MQTT Protocol
+- Caching using Redis DB
+
+### :books: Description
+For the sample given in Task 2: 
+
+1. Push a message to the MQTT for every 5th successful record with "good" data quality.
+2. Message should contain stats (sum, average,  timestamp - latest message) of all above 5 good records.
+3. Store each aggregation to a separate redis database.
+4. Develop an API to fetch the above saved data from redis DB.
+
+
+### :wrench: Tools to use: 
+1. Pycharm / VSCode
+2. MQTT - (PIP package: `paho-mqtt`)
+3. REDIS - (PIP package: `redis`)
 
+### :mag: References:
+* [Using MQTT in Python](https://www.emqx.com/en/blog/how-to-use-mqtt-in-python)
+* [Connection to Redis in Python](https://docs.redis.com/latest/rs/references/client_references/client_python/)
 
-## Areas covered:
-- MQTT operation
 
-## Description:
-For the above (task 2) given sample 
 
-1. Push a message to the MQTT for every 5th successful record with "good" 
-2. Message contains stats (sum, average,  timestamp - latest message) of all above 5 good records
-3. store each aggregation to a separate redis database
-4. develop an API to fetch the above saved data from redis