StreamingETL

StreamingETL is an approach to data integration that ingests data from multiple sources in real time or near real time, applies transformations, and loads the results into a target system as events flow through the pipeline. Unlike traditional batch ETL, which processes data in scheduled runs, StreamingETL aims to deliver fresh data with low latency, often measured in seconds or milliseconds.

A typical StreamingETL pipeline consists of data sources, a streaming ingestion layer, a streaming processing layer, and a destination such as a data warehouse, data lake, or operational store. The processing layer applies transformations, enrichment, filtering, windowing, and possibly stateful operations, and must handle out-of-order events, late data, and failures with guarantees such as at-least-once or exactly-once delivery. The system often relies on a streaming platform or message broker to decouple producers and consumers.

Common technologies include Apache Kafka as the backbone, along with stream processors such as Apache Flink, Spark Structured Streaming, or cloud-native services like Google Cloud Dataflow, Amazon Kinesis Data Analytics, and Azure Stream Analytics. Storage targets include data lakes (S3/ADLS) and data warehouses that support streaming loads and time-based partitions.

Key challenges include maintaining data ordering, handling late-arriving data, schema evolution, backpressure, fault tolerance, and ensuring data quality. Design choices often involve latency-throughput trade-offs and guarantees such as at-least-once or exactly-once processing; idempotent writes and compensating actions help manage failures.

Use cases include real-time analytics dashboards, fraud detection, anomaly detection, monitoring and alerting, feature engineering for machine learning, and event-driven architectures.