Dotcom On Steroids GQG Partners

Feel free to cherry‑pick the parts you need; the whole thing can be reused as a template.

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1️⃣ Company & Sector

3. Data Sources – What They Provide

3. Comparative Analysis of Key Platforms

2.2 Comparative Summary

4. Scenario Analysis

4.1 Impact of a New Legislation Requiring Comprehensive Footnote Disclosure

Legislative Context: A forthcoming act mandates that all publicly listed companies disclose detailed footnotes covering regulatory compliance, environmental impact, and executive compensation in their annual reports. The disclosure format is standardized across all firms.

Implications for Data Collection:

• Data Volume Increase: The volume of text to be scraped will increase substantially. Automated pipelines must handle larger file sizes (e.g., PDF documents with extensive footnotes).



• Schema Expansion: The data model must incorporate new fields capturing the standardized footnote categories (regulatory, environmental, compensation). Each footnote may have a unique identifier and associated metadata (date, jurisdiction).



• Data Quality Assurance: Standardization reduces variability in formatting but introduces strict compliance requirements. Validation scripts should check adherence to the prescribed structure (e.g., mandatory presence of certain subheadings).



• Legal and Ethical Compliance: Since these footnotes may contain sensitive information about regulatory positions or compensation details, additional safeguards (access controls, data minimization) must be enforced.

3.2 Scenario B: Introduction of a New Data Source

Suppose the platform integrates a new external dataset providing ESG metrics (e.g., sustainability scores, carbon footprints). This source will deliver structured JSON files with its own schema.

Impact on Data Pipeline:

• Ingestion: Implement a dedicated data ingestion module that pulls or receives the JSON payloads via API calls or secure file transfer.



• Schema Mapping: Define a mapping layer translating the external JSON structure into the platform’s internal representation (e.g., converting `company_id` to the system’s UUID, normalizing date formats).



• Validation Rules: Extend validation logic to ensure that ESG metrics fall within acceptable ranges and adhere to business rules.



• Storage Layer: Persist the transformed data in appropriate database tables or NoSQL collections, ensuring referential integrity with existing company records.

Impact on Existing Features

• Data Retrieval: The API endpoint for fetching company details will now need to aggregate ESG metrics alongside existing financial and operational data. Care must be taken to maintain backward compatibility; clients expecting the original schema should receive it unchanged, while a new optional field or subresource exposes ESG data.



• Reporting & Analytics: Existing reports that compute financial ratios may now incorporate ESG indicators, potentially requiring updates to calculation logic and dashboards.



• User Interface (Front-End): The UI components displaying company profiles must be extended to show ESG scores. This might involve new tabs or widgets, ensuring they fit within the current layout without overwhelming users.

2. Refactoring Scenario

Original Design Decision:

The application’s domain model defines a `Customer` entity that contains an embedded collection of `Address` value objects directly as an array property (`addresses`). The data layer persists this by serializing the entire addresses array into a single JSON column in the relational database.

Refactoring to Use Separate Entities and Relations:

Rationale

• Normalization & Queryability: Storing addresses in separate rows allows efficient queries (e.g., find all customers residing at a specific city) without loading the whole array.


• Scalability: As the number of addresses per customer grows, serializing into JSON can lead to large blobs that degrade performance.


• Domain Flexibility: Addresses may become entities themselves (with lifecycle events, validation, etc.) and could be shared among multiple customers or other aggregates.

Steps

1. Create Address Entity

class Address extends BaseEntity
private int $id;
private string $street;
private string $city;
private string $postalCode;
// getters/setters

```

1. Modify Customer Entity

```php
class Customer extends BaseEntity
private Collection $addresses; // e.g., Doctrine ArrayCollection

public function addAddress(Address $address): void { ... }
public function removeAddress(int $id): void { ... }


```

1. Update Repositories & Persistence Layer

- For rockchat.com ORM: define one-to-many relationship.
- For NoSQL or other persistence, ensure data model reflects embedded documents or references accordingly.

1. Adjust Domain Services / Application Logic

- Ensure validation logic still applies (e.g., address uniqueness per customer).

1. Refactor Tests

- Update integration/acceptance tests to use new API.

1. Update Documentation / Client Code

- Provide migration guides or backward‑compatibility layer if needed.

1. Run Full Regression Suite

1. Deploy Incrementally

4. What If the Change Breaks an Invariant?

If the refactor inadvertently violates a key invariant (e.g., a product must always have a non‑negative stock), you should:

1. Detect Early – Use property‑based tests to generate edge cases; failure indicates missing guard.


2. Guard in Domain Model – Move invariant enforcement into constructors or factory methods so that invalid objects can never be created.


3. Add Defensive Checks – In critical paths, verify preconditions before performing operations.


4. Fail Fast – Throw a domain‑specific exception (e.g., `InvalidStockException`) instead of silently proceeding.


5. Revert if Necessary – If the invariant cannot be restored easily, roll back to a previous stable version and fix the root cause.

3. Test Suite Skeleton

Below is a self‑contained skeleton that captures all core concepts: domain entities (`User`, `Product`), repository interfaces, service layer, and test classes for both unit tests (mocked dependencies) and integration tests (real in‑memory repositories). The code uses Java 17+ features such as records, sealed classes, and the modern JUnit 5 / Mockito APIs.