Data Pipeline Design Patterns

Table of Contents

• Overview
• Repository Structure
• Key Patterns
  • Extraction Patterns
  • Behavioral Patterns
  • Structural Patterns
• Implementation Examples
• Coding Best Practices
• Getting Started
• Contributing
• License
• Contact

Overview

This repository contains a comprehensive collection of data pipeline design patterns, implementation examples, and best practices for building efficient, scalable, and maintainable data pipelines.

Repository Structure

1. dataflow-patterns: Contains detailed explanations and examples of various data pipeline patterns.

   • extraction: Patterns for data extraction
   • behavioral: Patterns for different required pipeline behaviors
   • structural: Patterns for pipeline structure
   • source: Considerations for different source patterns
   • sink: Considerations for different sink patterns

2. coding-patterns: Includes coding best practices and helper functions for data pipeline development.

   • python-helpers: Python-specific patterns and utilities
   • scala-helpers: Scala-specific patterns and utilities

Key Patterns

Extraction Patterns

1. Full Snapshot Pull: Pull entire dataset at regular intervals.
2. Streaming: Process records in real-time or near real-time.
3. Time Ranged Pull: Pull data for a specific time frame.
4. Lookback Pull: Pull aggregate metrics for a past period.

Behavioral Patterns

1. Self-healing Pipelines: Automatically recover from failures and process missed data.

Structural Patterns

1. Multi-hop Pipelines: Keep data separated at different levels of "cleanliness".
2. Disconnected Pipelines: Independent workflows with implicit dependencies.
3. Conditional/Dynamic Pipelines: Adapt based on runtime conditions or inputs.

Implementation Examples

The repository provides implementation examples in both Python and Scala.

• Python ETL Pipeline Example
• Scala ETL Pipeline Example

Coding Best Practices

1. Traits in Scala: Powerful way to achieve multiple inheritance and compose behavior.
2. Miscellaneous Tips: Additional tips and best practices for data pipeline development.

Getting Started

1. Clone the repository
2. Navigate through the dataflow-patterns directory to explore different patterns
3. Check the coding-patterns directory for best practices and helper functions
4. Refer to the implementation examples to understand how to apply these patterns in your projects

Contributing

Contributions are welcome. Please follow these steps:

1. Fork the repository
2. Create a new branch for your feature
3. Commit your changes
4. Push to the branch
5. Create a new Pull Request

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contact

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