Lobbying Integration

#### Overview:
Integrate the scraping process for lobbying disclosures data. We will run the scraper every 6 months to refresh the information.

#### This project uses:

- python (with poetry)
- docker
- webscraping with selenium & beautiful soup
- AWS
- postgres
- sql

#### Resources:

- repo with Lobbying Scraper - https://github.com/maple-testimony/lobbying#readme (review the readme in that repo)
- google doc with context on this feature + info on the db/scraper -https://docs.google.com/document/d/1NdkWLmLMz9xw40esjjFUbjKs7Q_BvA4ifBUpc6G7nDs/edit
- schema: https://drive.google.com/file/d/1CFJr1WGkPFS1fcr8WG9z0E3o2vvSTHJl/view
- UI: components/bill/LobbyingTable.tsx
- Figma: [Lobbying](https://www.figma.com/file/mvjzP1pSwuZcHg2wZxVt1i/Lobbying---Feature-File-%5Bout-of-date%5D?type=design&node-id=513%3A2517&mode=dev)
- https://github.com/maple-testimony/infra.git
-
#### Scraping and Data Processing:

- Follow the instructions in the Lobbying Scraper README (above) to run the scraper in docker.
- Complete the infrastructure for AWS and deploy. Set the scrapers to run every 6mo, matching the schedule of MA lobbying disclosure release requirements.
-

#### Data Integration from AWS to Firestore:

- Develop a data integration pipeline from the aws postgres db to firestore.
- Collaborate with dev leads to create data structures and integrate the data

#### Testing and Integration:

- Enable the existing tests in the python project, and add any additional tests necessary,
- Implement error handling mechanisms to manage any potential data inconsistencies or errors during the data transfer process.

### Documentation and Maintenance:

- Document the integration process, including setup instructions, configurations, and data transfer procedures.

Start by reviewing the Lobbying Scraper README and the linked database and integration context, then inspect components/bill/LobbyingTable.tsx and the infra repository. Map the AWS-to-Postgres-to-Firestore flow, the six-month schedule, and the existing Python tests before defining the work. Done includes deployed infrastructure, data integration, error handling, passing tests, and setup documentation.

Written by the indexing model from the issue text.