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