shoppingcart_demo | Demo environment for a Shopping Cart data processing

by jros2300 Python Version: Current License: No License

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kandi X-RAY | shoppingcart_demo Summary

shoppingcart_demo is a Python library. shoppingcart_demo has no bugs, it has no vulnerabilities and it has low support. However shoppingcart_demo build file is not available. You can download it from GitHub.

This repository contains an example of a Big Data pipeline to process a stream of event from a Shopping Cart. With the scripts and templates contained in this repository you can create an AWS environment to generate and process this stream of events. If you run these scripts and templates, you're going to create resources in your AWS account, and that's going to have a cost. Please review the content before you use it.

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shoppingcart_demo has a low active ecosystem.

It has 0 star(s) with 0 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

shoppingcart_demo has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of shoppingcart_demo is current.

Quality

shoppingcart_demo has no bugs reported.

Security

shoppingcart_demo has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

shoppingcart_demo does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

shoppingcart_demo releases are not available. You will need to build from source code and install.

shoppingcart_demo has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions are available. Examples and code snippets are not available.

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shoppingcart_demo Key Features

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shoppingcart_demo Examples and Code Snippets

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Vulnerabilities

No vulnerabilities reported

Install shoppingcart_demo

To create all the resources you should follow these steps:. python createResources.py -b <bucket_name>.
Create a S3 bucket to store the events and all the processed files
Use the script createResources.py to generate the Kinesis Firehose stream and the required IAM role for it.
Create all the other resources using the CloudFormation template ShoppingCart.cform. This stack creates:
VPC to store all the other resources, with the subnets and security groups
Redshit cluster where you can find the final table with the carts information
DataPipeline to process the events with Pig and copy the data to Redshift. This pipeline runs every hour.
DataPipeline to process the events with Pig and train the Machine Learning Model. You can run this pipeline manually.
EC2 Instance generating random events every five minutes

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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