The core differences between batch ETL and real-time ETL lie in processing frequency, latency, use cases, and underlying technologies. Batch ETL processes data in scheduled intervals (e.g., hourly or daily), handling large volumes of accumulated data in bulk. Real-time ETL processes data continuously, often within milliseconds of generation, prioritizing low latency over bulk efficiency. For example, a retail company might use batch ETL to generate daily sales reports, while a banking system relies on real-time ETL to detect fraudulent transactions as they occur.
Technologically, batch ETL tools like Apache Spark or AWS Glue optimize for bulk data operations, leveraging parallel processing for efficiency. These systems often rely on stored procedures or scheduled jobs to transform and load data. Real-time ETL, however, uses streaming frameworks like Apache Kafka or Apache Flink to process data incrementally. These tools handle data streams from sources like IoT sensors or application logs, applying lightweight transformations (e.g., filtering or aggregation) to maintain speed. For instance, a ride-sharing app might use Kafka to update driver locations in real-time, whereas a data warehouse ingestion pipeline would use Spark for nightly batch loads.
Operational complexity and trade-offs also differ. Batch ETL simplifies error handling, as rerunning a failed job reprocesses the entire dataset, ensuring consistency. Real-time ETL requires robust error recovery mechanisms, such as checkpointing or exactly-once processing, to avoid data loss or duplicates. Additionally, batch workflows often prioritize data integrity through ACID transactions, while real-time systems may use eventual consistency to balance speed and accuracy. For example, a healthcare analytics system might use batch ETL for historical patient data analysis, ensuring accuracy, while real-time ETL monitors ICU device data to trigger immediate alerts. Cost-wise, batch processing can leverage cheaper storage (e.g., cloud object storage), whereas real-time systems demand scalable infrastructure (e.g., Kubernetes clusters) to handle constant data flow.