Challenges in Streaming ETL Pipelines for High-Frequency Data Ingestion and Real-Time Processing

The transition from traditional ETL (Extract, Transform, Load) pipelines to streaming ETL pipelines seeks to enhance the accessibility and usability of data by everyone in the organization, including non-technical organizational members. Traditional batch ETL pipelines, which process data at the end of a batch cycle, are ETL unsuitable for high-frequency data ingestion and real-time applications. Streaming ETL handles data in real-time as it is generated and accessed, allowing continuous processing of high-frequency data as it arrives. The paper analyzes the challenges in designing and implementing streaming ETL pipelines for high-frequency data ingestion and real-time processing. A review of existing literature led to the identification of the major streaming ETL challenges and their solutions. Some of the major challenges in streaming ETL pipelines include the ingestion of high-frequency data, achieving low-latency data transformation, recovering from faults, privacy and security, real-time data visualization, and availability of the required skills. Some strategies for addressing the challenges include backpressure and buffering, distributed messaging systems, windowing, lightweight serialization formats, checkpointing, idempotent operations, and watermarking. Organizations should address these challenges to unlock the full potential of streaming ETL pipelines