Building Real-time Data Lakes: Streaming ELT with Kafka, Spark Structured Streaming, and dbt
In today’s fast-paced digital economy, businesses thrive on data. However, traditional batch processing often falls short when immediate insights are required to react to market changes, personalize customer experiences, or detect anomalies in real-time. This is where the concept of a Real-time Data Lake comes into play, providing a powerful architecture for ingesting, processing, and analyzing vast streams of data as it arrives. By combining the strengths of Apache Kafka for data ingestion, Apache Spark Structured Streaming for processing, and dbt for data transformation and modeling, organizations can build robust and scalable real-time ELT (Extract, Load, Transform) pipelines.