3.9: Assessments
- Page ID
- 58292
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Chapter 3 Learning Objectives
- Distinguish between data collection and data ingestion, explaining how each serves a different purpose in the data pipeline and how they work together.
- Identify and compare common data collection methods — including manual data entry, surveys, sensors/IoT, web scraping, APIs, transaction system exports, and logs — and describe the advantages and challenges of each.
- Explain the difference between batch and streaming ingestion, including when each approach is appropriate and how hybrid approaches (Lambda, Kappa, micro-batching) combine both.
- Compare ETL and ELT pipelines, describing how each processes data, where transformations occur, and the trade-offs in flexibility, compliance, and performance.
- Describe Change Data Capture (CDC) and explain how it enables near-real-time data synchronization by capturing only incremental changes from source systems.
- Explain the role of message queues and streaming platforms (e.g., Kafka) in decoupling data producers from consumers, buffering data, and enabling scalable ingestion architectures.
- Identify key challenges in data ingestion — schema evolution, scalability, latency, and data quality — and describe best practices for addressing each.
- Recognize common data pipeline architecture patterns — traditional ETL warehouse, data lake/lakehouse ELT, Lambda, Kappa, and event-driven — and explain the scenarios each pattern is best suited for.