Update assignment3.md

assignments/assignment3.md

+
+
 # Assignment 3
 
+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 
+
+Happy Coding! :tada:
 
-## Task 1: Working with Mongo - Advanced
+## :pushpin: Task 1: Working with Mongo - Advanced
 
 
-## Areas covered:
+### :golf: Areas covered:
 - Working with NoSQL
 - Working with Pandas
 
+### :books: Description:
 
-## Description:
 
 You are given with a dataset of a restaurant review in the form of a JSON file. The end goal of the project is to create an API interface that will provide the following: 
 1. Business name with maximum number of highest average review.
@@ -18,75 +31,86 @@ You are given with a dataset of a restaurant review in the form of a JSON file.
 Sample Document:
 ```json
 {
-  "address": {
-    "building": "120",
-    "coord": [
-      -73.9998042,
-      40.7251256
-    ],
-    "street": "Prince Street",
-    "zipcode": "10012"
-  },
-  "borough": "Manhattan",
-  "cuisine": "Bakery",
-  "grades": [
-    {
-      "date": {
-        "$date": "2014-10-17T00:00:00.000Z"
-      },
-      "grade": "A",
-      "score": 11
-    },
-    {
-      "date": {
-        "$date": "2013-09-18T00:00:00.000Z"
-      },
-      "grade": "A",
-      "score": 13
-    },
-    {
-      "date": {
-        "$date": "2013-04-30T00:00:00.000Z"
-      },
-      "grade": "A",
-      "score": 7
-    },
-    {
-      "date": {
-        "$date": "2012-04-20T00:00:00.000Z"
-      },
-      "grade": "A",
-      "score": 7
-    },
-    {
-      "date": {
-        "$date": "2011-12-19T00:00:00.000Z"
-      },
-      "grade": "A",
-      "score": 3
-    }
-  ],
-  "name": "Olive'S",
-  "restaurant_id": "40363151"
+ "address":  {
+ "building":  "120",
+ "coord":  [
+ -73.9998042,
+ 40.7251256
+ ],
+ "street":  "Prince Street",
+ "zipcode":  "10012"
+ },
+ "borough":  "Manhattan",
+ "cuisine":  "Bakery",
+ "grades":  [
+ {
+ "date":  {
+ "$date":  "2014-10-17T00:00:00.000Z"
+ },
+ "grade":  "A",
+ "score":  11
+ },
+ {
+ "date":  {
+ "$date":  "2013-09-18T00:00:00.000Z"
+ },
+ "grade":  "A",
+ "score":  13
+ },
+ {
+ "date":  {
+ "$date":  "2013-04-30T00:00:00.000Z"
+ },
+ "grade":  "A",
+ "score":  7
+ },
+ {
+ "date":  {
+ "$date":  "2012-04-20T00:00:00.000Z"
+ },
+ "grade":  "A",
+ "score":  7
+ },
+ {
+ "date":  {
+ "$date":  "2011-12-19T00:00:00.000Z"
+ },
+ "grade":  "A",
+ "score":  3
+ }
+ ],
+ "name":  "Olive'S",
+ "restaurant_id":  "40363151"
 }
 ```
-
 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
+
+
+### :golf: Areas covered:
+- Timeseries Operation
+- Working with Timeseries
+- Working with Pandas
+
+### :books: Description:
 
 You are given with a dataset of weather in the form of a CSV file. The end goal of the project is to create an API interface that will provide the following:
 
@@ -99,24 +123,32 @@ Sample Document:
 |31/12/2004 01:00|13478 |
 |31/12/2004 02:00|12865 |
 
-### Tools to use: 
+
+### :wrench: Tools to use: 
 1. Pycharm / VSCode
 2. Pandas 
+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/
+------------------------------------------------------
 
+## :pushpin: Task 3: Working with MQTT & REDIS
 
-## Task 3: Working with MQTT & REDIS
+### :golf: Areas covered:
+- MQTT Protocol
+- Caching using Redis DB
+
+### :books: Description
 
 # Description
 Data from different sites will be pushed with frequency of 10 seconds for the parameters PM10,PM2.5,SO2,NO2 via mqtt.
 
 data can be of different quality - Good ( 0 ), Maintainance ( 1 ), Error ( 2 )
 
-Based on the quality of data update to different redis database.
+Based on the quality of data update to different Redis database.
 
 sample data format: 
 ```json
@@ -129,15 +161,16 @@ sample data format:
 	   
 Use Redis for caching/storing information
 
-create consumer's which consumes data from these topics and store to a redis db based on data quality.
+Create consumer's which consumes data from these topics and store to a Redis db based on data quality.
 
-### Tools to use: 
+### :wrench: Tools to use: 
 1. Pycharm / VSCode
-2. MQTT - (paho-mqtt)
-3. REDIS - (redis)
+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/)
 
-### Reference:
-https://www.emqx.com/en/blog/how-to-use-mqtt-in-python
 
-https://docs.redis.com/latest/rs/references/client_references/client_python/