README.md

Big-Data-ETL

To perform the ETL process completely in the cloud and upload a DataFrame to an RDS instance.

Instructions

1. To execute the project follow the below commands for the files "part_one_reviews_us_Software" and "part_one_reviews_us_Grocery": git clone https://github.com/BharatGuturi/Big-Data-ETL.git

2. Create an AWS account.

3. Create RDS and S3 in AWS.

4. Connect google colab with the ipynb files.

5. S3 bucket setting: { "Version": "2012-10-17",
   "Statement": [
   {
   "Sid": "getobject",
   "Effect": "Allow",
   "Principal": "",
   "Action": "s3:GetObject",
   "Resource": "/"
   } ] }

6. Install the required libraries using the following commands: pip install pypandoc pip install pyspark

7. In the following command in the 'Load' section, use the endpoint created in AWS in and database name in <database_name>. In the config command, use the username and password used to create the database

   jdbc_url="jdbc:postgresql://:5432/<database_name>"

   config = {"user":"", "password": "", "driver":"org.postgresql.Driver"}

8. Run the files: "part_one_reviews_us_Software" and "part_one_reviews_us_Grocery"