End to End architecture and Implementation of a Data ingestion Pipeline in AWS Cloud for build Data lake

For Reference on AWS IOT Device side Python SDK, kindly follow the below link and clone from it.

https://github.com/aws/aws-iot-device-sdk-python

Please check the following while using AWS IOT Core Python SDK and Boto3 with Python 3.x or Python 2.x versions in this following project.

On 2021-01-15, deprecation for Python 2.7 was announced and support was dropped on 2021-07-15. To avoid disruption, customers using Boto3 on Python 2.7 may need to upgrade their version of Python or pin the version of Boto3. For more information, see this blog post.https://aws.amazon.com/blogs/developer/announcing-end-of-support-for-python-2-7-in-aws-sdk-for-python-and-aws-cli-v1/

On 2022-05-30, support for Python 3.6 was ended. This follows the Python Software Foundation end of support for the runtime which occurred on 2021-12-23. For more information, see this blog post. https://aws.amazon.com/blogs/developer/python-support-policy-updates-for-aws-sdks-and-tools/

Here is the illustration of the Complete architectural implementation of the AWS Cloud IOT Data lake with IOT telemetry data coming from MQTT Client's running on Cisco IOT gateways.

A) (IR1101 , IR829, IR 1800 series Cisco IOT gateways could be used to run the MQTT Client) or

B) A linux machine or Raspberry PI could be used to run a vanilla MQTT Client (code present in this repository) on it.

C) Any Cisco Catalyst hardware supporting IOX_CAF and AWS Greengrass SDK could be also leveraged - Reference in link below. https://developer.cisco.com/ecosystem/cpp/solutions/177022/

1. Setting up the AWS IoT Analytics channel, pipeline, and data store 2 Ingesting MQTT baaed sample pubSub data into the AWS IoT Core, then to the IOT data pipeline channel and then evnetually to the IOT data analytics, Visualisation toosl. Creating a dataset with dataset content delivery to different S3 buckets. Kinldy refer illustration above.
2. Integrating IoT data with other data stored in your data lake on AWS IOT data pipeline channel. (Either by using Lakefromation tools or by using Glue Data catalog logic or by creating manual data pipelines).

For this use case implemenation in the AWS Cloud make sure that you have access to the following resources:

1. An AWS account in the same AWS Region where AWS IoT Analytics is available. The idea is to reduce the RTT for moving the data by selecting IOT analytics and S3 data storage buckets in same region.
2. This project uses a Cloudformation YAMl template which is in the US East (N. Virginia) Region.
3. However, you can choose another AWS Region where AWS IoT Analytics is available.
4. The AdministratorAccess policy granted to your AWS account (for production, we recommend restricting this further).
5. The AWS CLI installed and configured to use with your AWS account, along with Privelege, PKI key pairs for CLI access to your AWS Cloud account.
6. AWS IOT Python SDK and all the modules as defined in the requirements.txt to be installed.