Transitioning from Batch ETL to Streaming Data Processing for Enhanced Advertising Integration

The organization relies on traditional batch processing systems for data retrieval, transformation, and distribution, leading to high resource consumption, delayed data delivery, and difficulty handling variable data volumes from multiple third-party sources. These limitations hinder real-time responsiveness and increase operational costs, especially as data throughput and third-party integration complexity grow.

A mid-sized digital advertising organization specializing in data-driven marketing solutions, managing large-scale data integrations and ad campaigns.

• Replace existing batch ETL processes with a real-time, streaming data processing architecture.
• Reduce infrastructure costs by minimizing reliance on large cluster resources, enabling resource-safe, single-machine processing.
• Increase processing speed to handle near-infinite data streams, ensuring timely data delivery to third-party services.
• Improve system resilience, fault tolerance, and error handling capabilities.
• Enable flexible, parallelized data processing stages corresponding to different third-party integration requirements.
• Achieve scalable memory management with constant, predictable resource consumption regardless of data volume.

• Implement real-time data retrieval from core data sources in a streaming manner.
• Utilize a message-driven architecture to process data with high responsiveness using high-level APIs.
• Support parallel processing of data streams at various stages, optimizing VM core utilization.
• Connect with multiple third-party data ingestion methods, including parallel HTTP calls and file-based uploads.
• Ability to process and merge data streams into single files or parallel streams for distribution.
• Incorporate fault-tolerance, backpressure management, and error recovery mechanisms.

• Data sources providing streaming data in real-time
• Third-party services receiving processed data via HTTP or file uploads
• Cloud storage platforms like GCS or S3 for data archival
• Existing data platforms or management systems for data input

• Scalability to handle near-infinite data streams without degradation
• Consistent, predictable memory footprint during processing
• High availability and fault tolerance to ensure process resilience
• Parallel processing capabilities to maximize VM core utilization
• Secure data handling in compliance with relevant standards

The new streaming architecture is expected to significantly lower operational costs by reducing dependency on expensive cluster resources, improve data processing speeds, and enhance system resilience. It will enable the organization to handle virtually unlimited data streams, facilitate more flexible third-party integrations, and provide faster, more reliable data delivery—ultimately leading to improved client satisfaction, reduced runtime errors, and greater control over advertising data workflows.