The challenges of building data systems with high concurrency

Big Data has evolved significantly since its inception in the 90s, with current challenges focusing on extracting meaningful insights from the vast amounts of data generated by systems such as web apps or IoT devices. While storage is manageable, processing this data in real-time for high concurrency, low-latency analytics remains difficult, requiring an understanding of data in motion, infrastructure management, and effective measurement strategies. Key issues include maintaining efficient data ingestion and concurrent reads, managing dynamic systems, handling massive scale events, and ensuring responsive frontend design. Additionally, schema changes and performance monitoring can further complicate real-time data processing, highlighting the complexity of building scalable data products that meet user demands without compromising performance.