DynamoDB-Real Time Ingestion & Real Time Analytics

Khilawar Verma

Abstract: This article offers a refreshing departure from traditional, cumbersome data ingestion practices by introducing a lean and real-time DynamoDB-based framework tailored to address long-standing operational bottlenecks in data pipelines. It is evident that existing ingestion models, often reliant on Change Data Capture (CDC) and file-based merges, have proven fragile, time-consuming, and riddled with maintenance challenges. This new framework, however, leverages AWS-native tools such as DynamoDB streams and Lambda functions to create a low-latency, highly resilient pipeline that simplifies cross-account data replication. What stands out is the dynamic schema evaluation, which means that developers no longer need to halt progress due to schema shifts-a common stumbling block in older systems. The framework?s ability to maintain near-perfect data parity while providing robust monitoring and error recovery reflects a meaningful shift towards operational agility. This suggests that not only is data made available in near real-time, but it also empowers data engineers and analysts to move beyond verification and firefighting, focusing instead on deriving timely insights. Taken together, these innovations point to a practical yet powerful evolution in data ingestion, offering an almost plug-and-play solution that can be deployed swiftly without the typical overhead of complex ETL processes.

Keywords: DynamoDB, AWS Public Cloud, Data Ingestion, BigData, Analytics, Data Pipelines