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TimeStream - A Modern Data Lakehouse with Advanced ETL

🚀 Data Versioning | Quality Monitoring | Multi-Format Support | Optimized Queries

Overview

TimeStream is a modern data lakehouse solution that combines robust data versioning, quality monitoring, and optimized query performance. It provides a complete ETL pipeline with support for multiple data formats and automated quality checks.

Key Features

Core Capabilities

  • 🔄 Git-Like Data Versioning
    • Branch-based development
    • Atomic commits
    • Time travel queries
  • 🛡️ Data Quality
    • Automated validation with Great Expectations
    • Schema enforcement
    • Quality reports generation
  • 🚀 Performance Optimization
    • Z-ordered indexing
    • Intelligent partitioning
    • Query optimization

Advanced Features

  • 📊 Multi-Format Support
    • Parquet, Delta Lake, CSV, JSON
    • ORC and Avro compatibility
    • Format conversion utilities
  • 🔄 Snapshot Management
    • Automatic cleanup
    • Retention policies
    • Version history
  • 📈 Monitoring & Metrics
    • Performance tracking
    • Quality metrics
    • Usage analytics

Architecture

Tech Stack

Component Technology Purpose
Storage MinIO S3-compatible object storage
Table Format Apache Iceberg Versioned table management
Version Control Nessie Git-like data versioning
Processing Apache Spark Distributed computation
Quality Great Expectations Data validation
Format Support Delta Lake ACID transactions
Analytics Jupyter Data exploration

System Components

TimeStream/
├── etl/
│   ├── ingest.py        # Data ingestion
│   ├── transform.py     # Data transformation
│   ├── validate.py      # Quality validation
│   └── data_converter.py # Format conversion
├── config/
│   ├── iceberg_config.json
│   └── nessie_config.json
└── docker-compose.yml

Setup & Installation

Prerequisites

  • Docker and Docker Compose
  • Python 3.8+
  • 8GB+ RAM

Quick Start

  1. Clone Repository

    git clone https://github.com/your-org/TimeStream.git
cd TimeStream

  2. Install Dependencies

    pip install -r requirements.txt

  3. Start Services

    docker-compose up -d

  4. Initialize Components

    great_expectations init

Usage Guide

1. Data Ingestion

python etl/ingest.py
  • Supports multiple formats
  • Parallel processing
  • Progress monitoring

2. Data Transformation

python etl/transform.py
  • Optimized processing
  • Z-ordering
  • Partition management

3. Data Validation

python etl/validate.py
  • Quality checks
  • Schema validation
  • Error reporting

4. Format Conversion

python etl/data_converter.py
  • Multi-format support
  • Delta Lake integration
  • Optimized conversion

Configuration

MinIO Settings

{
  "endpoint": "localhost:9000",
  "access_key": "minioadmin",
  "secret_key": "minioadmin"
}

Iceberg Configuration

{
  "warehouse": "s3://timestream/",
  "catalog": "nessie"
}

Monitoring & Maintenance

Data Quality

  • Access reports: http://localhost:8080/great_expectations
  • View validation results
  • Track quality metrics

Performance

  • Spark UI: http://localhost:8080
  • MinIO Console: http://localhost:9001
  • Nessie API: http://localhost:19120

Maintenance

  • Snapshot cleanup: python etl/snapshot_cleanup.py
  • Version history
  • Storage optimization

Best Practices

Development Workflow

  1. Create feature branch
  2. Develop and test transformations
  3. Validate data quality
  4. Merge to main branch

Performance Optimization

  • Use appropriate partitioning
  • Enable Z-ordering for spatial data
  • Configure proper retention policies

Data Quality

  • Define comprehensive expectations
  • Monitor validation results
  • Address quality issues promptly

Troubleshooting

Common Issues

  • Service connectivity
  • Resource constraints
  • Version conflicts

Solutions

  • Check service logs
  • Verify configurations
  • Ensure sufficient resources

Contributing

  • Fork the repository
  • Create feature branch
  • Submit pull request

License

Apache License 2.0

Support

  • GitHub Issues
  • Documentation
  • Community Forums