Marisync 🌊

Real-time oceanographic data integration and analysis platform using Databricks.

Overview

Marisync is a scalable platform for ingesting, processing, and analyzing oceanographic data from multiple sources in real-time. It leverages Scala's type safety and concurrency features along with Databricks' unified analytics platform.

Data Sources

• NOAA National Data Buoy Center (NDBC)
• Argo Float Network
• Ocean Observatories Initiative (OOI)
• Copernicus Marine Service (CMEMS)
• IOOS (Integrated Ocean Observing System)

Key Measurements

• Temperature (°C)
• Salinity (PSU)
• Dissolved Oxygen (mg/L)
• pH Levels
• Turbidity (NTU)
• Chlorophyll (μg/L)
• Current Speed (m/s)
• Current Direction (degrees)

Why Scala?

1. Type Safety: Early error detection through strong type system
2. Concurrency: Built-in support for handling multiple data streams
3. Spark Integration: Native language for Apache Spark
4. Performance: JVM-based with functional programming optimizations
5. Interoperability: Seamless Java library integration

Databricks Integration

• Real-time data processing using Structured Streaming
• Delta Lake for ACID transactions and time travel
• MLflow for model tracking and deployment
• Databricks Jobs for orchestration
• Unity Catalog for data governance

Architecture

graph LR
    A[Data Sources] --> B[Connectors]
    B --> C[Stream Processor]
    C --> D[Delta Lake]
    D --> E[ML Pipeline]
    E --> F[Analysis Dashboard]

Loading

Setup

1. Install dependencies:

   sbt compile
   pip install -r requirements.txt

2. Configure Databricks CLI:

   pip install databricks-cli
   databricks configure --token

3. Set environment variables:

   cp .env.template .env
   # Edit .env with your credentials

Development

Running Tests

sbt test

Local Development

sbt run

Databricks Deployment

databricks workspace import

Project Structure

marisync/
├── src/
│   ├── main/
│   │   ├── scala/
│   │   │   ├── connectors/    # Data source connectors
│   │   │   ├── pipeline/      # Stream processing
│   │   │   ├── models/        # Data models
│   │   │   └── utils/         # Common utilities
│   │   └── python/
│   │       └── ml/            # Machine learning models
│   └── test/
├── jobs/                      # Databricks job configs
└── notebooks/                 # Databricks notebooks

Contributing

Please read CONTRIBUTING.md for details on our code of conduct and the process for submitting pull requests.

License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

Technologies

• Languages: Scala, Python
• Processing: Apache Spark, Structured Streaming
• Storage: Delta Lake
• Platform: Databricks
• ML: MLflow, scikit-learn
• Data Sources: NDBC, Argo, OOI, CMEMS
• Testing: ScalaTest, Mockito
• Build Tool: sbt