System Design for Data Ingestion and Analytics System

Problem Statement

Design a system that ingests data from various sources, allowing consumers to request any combination of raw data and build analytics on top of it. The system should ensure data integrity, manage user access, and provide flexibility in data schema definitions.

Key Components

1. Data Ingestion Layer

• Description: Responsible for collecting data from various sources (e.g., APIs, databases, file uploads).
• Features:
  • Automated Ingestion: Scheduled or event-driven ingestion processes to pull data from external sources.
  • Data Validation: Check for missing fields and enforce schema constraints before storing data.

2. Authentication and Authorization

• Authentication:

  • Mechanism: Use OAuth2 or JWT for secure token-based authentication.
  • User Registration: Allow users to register and create accounts securely.

• Authorization:

  • Role-Based Access Control (RBAC): Define roles (e.g., admin, consumer) with specific permissions to access different parts of the system.
  • Access Control Lists (ACLs): Fine-grained control over which users can access or modify specific data sets.

3. Data Quality Management

• Sanity of Data:

  • Validation Rules: Implement validation rules to ensure data meets predefined standards (e.g., data types, ranges).

• Handling Duplicates:

  • Deduplication Logic: Identify and remove duplicate records during the ingestion process based on unique identifiers (e.g., timestamps, IDs).

• Discarding Data with Missing Fields:

  • Field Validation: Automatically discard records that do not meet the minimum required fields for processing.

4. Registration of Participants and External Systems

• Participant Registration:

  • Provide an interface for users to register as participants in the system.
  • Capture necessary metadata about participants (e.g., contact information, organization).

• External Systems Registration:

  • Allow external systems to register with the ingestion system.
  • Capture connection details and authentication mechanisms for secure data exchange.

5. Schema Definition and Mapping

• Dynamic Schema Definition:

  • Enable consumers to define their own schemas for the incoming data.
  • Provide a user-friendly interface for mapping fields and specifying data types.

• Schema Versioning:

  • Support versioning of schemas to handle changes over time without disrupting existing consumers.

6. Consumer Interaction Layer

• Data Query Interface:

  • Provide a RESTful API or GraphQL endpoint for consumers to query the ingested data.
  • Allow consumers to specify filters, aggregations, and analytics functions.

• Analytics Layer:

  • Integrate with BI tools or provide built-in analytics capabilities for users to visualize and analyze the ingested data.

Conclusion

This system design provides a robust framework for ingesting data from various sources while ensuring data quality, security, and flexibility. By implementing strong authentication and authorization mechanisms, managing data integrity, and allowing consumers to define schemas, the system can effectively support diverse analytical needs while maintaining high standards of data governance.