Data Processing & ETL

Relevant source files

The following files were used as context for generating this wiki page:

• services/data-processing/app.py
• services/api-gateway/app.py
• infrastructure/databases/docker-compose.yml
• docker-compose.yml
• k8s/deployment.yaml
• IMPLEMENTATION_COMPLETE.md

The Data Processing & ETL system in Aether-X Ultimate is a core microservice responsible for ingesting, transforming, and streaming data across the platform. It handles real-time data streaming using Apache Kafka and manages structured ETL (Extract, Transform, Load) pipelines to move data between various SQL and NoSQL databases. The service operates on port 8006 and is integrated with the centralized API Gateway for secure access and rate-limited ingestion.

Sources: services/data-processing/app.py:1-10, IMPLEMENTATION_COMPLETE.md:16-25, docker-compose.yml:118-125

System Architecture

The data processing architecture is built on a multi-threaded Python Flask application that interacts with a distributed infrastructure. It utilizes an asynchronous processing model where data is ingested into a queue and handled by dedicated stream processors.

Components

• Ingestion Engine: Receives data via REST API and assigns unique message IDs.
• Stream Processors: Background threads that pull data from the queue for transformation and enrichment.
• ETL Pipeline Engine: A synchronous execution engine for complex multi-step data movements.
• Persistence Layer: Integration with MySQL, PostgreSQL, MongoDB, Cassandra, and Redis.

Sources: services/data-processing/app.py:12-25, infrastructure/databases/docker-compose.yml:4-100

Data Flow Diagram

The following diagram illustrates the flow of data from external ingestion through the processing pipeline to final storage.

Data Ingestion & Streaming

The service provides endpoints for both ad-hoc ingestion and continuous stream processing. Ingested data is tracked with metadata including timestamps and source IP addresses.

API Endpoints for Ingestion

Endpoint	Method	Description
/ingest	POST	Ingests a single data payload into the processing queue.
/stream/process	POST	Starts a background thread for continuous stream processing.
/stream/stop/<id>	POST	Terminates a specific stream processor.
/stream/status	GET	Returns the status of all active processors and queue size.

Sources: services/data-processing/app.py:27-113, services/api-gateway/app.py:190-210

Stream Processing Logic

Stream processors run as daemon threads, ensuring they do not block the main application. They support configuration options such as enrich_data, which adds metadata like source IP and user-agent strings to the processed records.

def process_stream(processor_id, config):
    while True:
        if stream_processors.get(processor_id, {}).get('status') == 'stopped':
            break
        if not data_queue.empty():
            message = data_queue.get()
            processed_message = {
                'original_id': message['id'],
                'processed_data': apply_stream_processing(message['data'], config),
                'processed_at': datetime.utcnow().isoformat()
            }
            processed_data.append(processed_message)

Sources: services/data-processing/app.py:149-180

ETL Pipelines

ETL pipelines are defined as a series of discrete steps: extract, transform, and load. This allows for modular data movement between the different storage technologies used in the project, such as moving user data from MySQL to an Elasticsearch index for search.

Pipeline Steps

1. Extract: Supports fetching data from APIs or internal databases.
2. Transform: Includes logic for filtering sensitive data, aggregating numerical values, and normalizing datasets.
3. Load: Persists the resulting data into memory or external storage destinations.

Sources: services/data-processing/app.py:126-147, services/data-processing/app.py:182-220

ETL Sequence Diagram

This diagram shows the synchronous execution of an ETL pipeline request.

Infrastructure & Deployment

The data processing service is containerized and deployed within a Kubernetes cluster. It relies on a robust infrastructure stack for messaging and persistence.

Deployment Configuration

The service is deployed with the following resource specifications:

• Replicas: 2 pods for high availability.
• Memory: 512Mi request, 1Gi limit.
• CPU: 250m request, 500m limit.
• Health Checks: Liveness and readiness probes hitting /health on port 8006.

Sources: k8s/deployment.yaml:313-345, docker-compose.yml:118-132

Dependency Map

Dependency	Purpose	Connection
Apache Kafka	Message Broker	Real-time streaming
Redis	Caching	Temporary state storage
Elasticsearch	Search/Analytics	Processed data indexing
API Gateway	Routing	External endpoint access

Sources: docker-compose.yml:128-131, infrastructure/databases/docker-compose.yml:102-135

Conclusion

The Data Processing & ETL module provides the foundational capabilities for data-driven operations within Aether-X Ultimate. By combining flexible ETL pipelines with scalable Kafka-backed stream processing, the system ensures that data is consistently available, transformed, and ready for consumption by AI models and other microservices.

Sources: IMPLEMENTATION_COMPLETE.md:16-25, services/data-processing/app.py:1-10