Calling the AWS API using the Boto3 package for Python is straight forward but it can be useful to have a cache of the responses. These two projects created an initial caching tool and an associated report generator.
Whilst the AWS Console is reasonably user-friendly, it can be difficult to paint a full picture of your AWS environment. This set of work sought to generate useful reports for readers such as netowrk and security folks. The goal was to produce summary information regarding key AWS-based resources and even (gulp) network diagrams.
Two repositories make up the resulting codebase:
- AWS Cache
A Python package that calls the AWS API (through Boto3) and stores the results in an SQLite database. The system uses the SQLAlchemy toolkit to store the data and manage relationships. This work also gave me the opportunity to create my first Python wheel.
- AWS Reporter
A sample script that uses the AWS Cache to generate reports on an AWS account. The script calls AWS Cache to build the cache then processes a set of Jinja 2 templates to produce a set of Asciidoctor documents. These are then easily transformed to HTML via a docker-based command.
Just running the
awscache command (made available via the AWS Cache package) with the right
credentials will generate an SQLite database with details of your key AWS resources - such as
IAM users/groups, KMS keys, Cloud Formation stacks, and Network topology (VPCs, VPNs, subnets etc).
You can then use this database file in various reporting/querying tools.
The AWS Reporter project utilises the cache to generate HTML-based reports. After a fair bit of effort I even managed to generate a diagram for each VPC that illustrates the various subnets, route tables, gateways and their relationships.
Unfortunately I can’t give you sample outputs - best not to reveal too much about the AWS environment.