Data Engineering

Data Pipeline Patterns

Batch Processing

• Scheduled Jobs: Run data processing at fixed intervals (hourly, daily, weekly)
• Use Cases: Historical analysis, reporting, data warehousing
• Tools: Apache Spark, Hadoop, Airflow, dbt
• Design Considerations: Latency tolerance, resource efficiency, cost optimization

Streaming Processing

• Real-time Ingestion: Process data as it arrives with low latency
• Use Cases: Real-time analytics, monitoring, fraud detection
• Tools: Apache Kafka, Apache Flink, Apache Storm, Apache Beam
• Design Considerations: Event ordering, exactly-once semantics, backpressure