The following are the steps to run the code

1. Install the required packages and create the docker image for the kafka producer --- this will be done within docker container in the kafka-producer folder --- To do this, run the following command: docker build -t kafka-producer . make sure you are in the kafka-producer folder

   --- This will create a docker image with the name kafka-producer

--- We do also have wait-for-it.sh script which will wait for the kafka zookeeper and kafka server to be up and running before running the producer

--- wthin the docker container, we have the following files: --- kafka-producer.py - this is the python script that will produce the messages to the kafka topic also dummy data is generated using faker library also topic declaration is done here --- requirements.txt - this is the list of packages that are required to run the python script --- wait-for-it.sh - this is the script that will wait for the kafka zookeeper and kafka server to be up and running before running the producer

2. Setup for tthe flink processor ---- In this folder, we have the following files and folder: --- wait-for-it.sh - this is the script that will wait for the kafka zookeeper and kafka server to be up and running before running the producer --- pom.xml - this is the maven pom file that will be used to build the flink processor also configuration for flink is done here --- Dockerfile - this is the dockerfile that will be used to build the docker image for the flink processor configure wait-for-it.sh script is also done here also target/flink-processor-1.0-SNAPSHOT.jar is copied to the docker image

   NB: target folder is created after building the flink processor with some java files --- flink-processor-1.0-SNAPSHOT.jar is the jar file that is generated after building the flink processor --- how to get while inside flink-processor folder run the following command: mvn clean install --- this will generate the jar file in the target folder

   ---src/main ---java Here we have the following files: --- Main.java - this is the main class that will be used to run the flink processor ---Weather.java - this is the class that will be used to create the weather object ---WeatherDeserializer.java - this is the class that will be used to deserialize the weather object

    ---resources
    Here we have the following files:
    ---log4j.properties - this is the log4j configuration file responsible for logging the flink processor
   

What to do: --- run the following command to build the docker image for the flink processor: docker build -t flink-processor . make sure you are in the flink-processor folder

3. Setup for the kafka consumer --- Postgress will be our kafka consumer --- In this folder, we have the following files and folder: --- Dockerfile - this is the dockerfile that will be used to build the docker image for the kafka consumer -- It will have the script for creating the database and tables create_table.sql --- create_table.sql - this is the script that will be used to create the database and tables having the following columns: --- id - this is the id of the city --- city - this is the name of the city --- average_temperature - this is the average temperature of the city

   --- Command for buitling the docker image: docker build -t postgres .

4. Finally run the docker compose file to run all the containers --- This will run the kafka producer, flink processor and kafka consumer --- Command for running the docker compose file: docker-compose up -- to run the containers docker-compose down -- to stop the containers

   --- Reaching the postgres container: --- Command for reaching the postgres container: docker exec -it postgres psql -U postgres -d postgres --- you will enter the postgres container and you can run the following command to see the data in the database: /dt --- you will see the created table: --- you can also run the following command to see the data in the database: SELECT * FROM weather;