3.9.1: Review Questions

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Page ID: 58293

Felix Amoruwa
De Anza College

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Review Questions

Multiple Choice

Which ingestion technique captures only new or modified records from a source database rather than re-extracting all data?
- a) ETL b) ELT c) CDC d) Batch dump
A financial institution needs to detect fraudulent credit card transactions within seconds. Which ingestion approach is most appropriate?
- a) Nightly batch ETL b) Weekly data dump c) Real-time streaming ingestion d) Manual data entry
In the ELT pattern, when does data transformation occur?
- a) Before loading into the target b) After loading into the target c) During extraction d) Never

Short Answer

Explain the key differences between data collection and data ingestion. Provide an example where both occur in sequence.
Compare batch and streaming ingestion across four dimensions: latency, complexity, cost, and use cases.
Describe three challenges of web scraping as a data collection method and how to mitigate each.
What is schema evolution, and why is it a challenge for data pipelines? How can ETL/ELT systems handle it?

True/False

True or False: APIs always provide data faster than web scraping because they are designed for data exchange.
True or False: The Lambda architecture uses both batch and streaming layers to balance completeness with low latency.
True or False: CDC eliminates the need for initial full data loads — only changes are ever captured.

Answer Key:

1-c, 2-c, 3-b, 4. Collection=gathering raw data (e.g., survey responses); Ingestion=loading into warehouse via ETL. Example: survey tool collects responses → nightly ETL ingests them into data warehouse. 5. Batch: higher latency, simpler, lower cost, reporting/BI. Streaming: low latency, complex, higher cost, fraud detection/real-time dashboards. 6. (1) Legal/ethical—respect robots.txt and ToS; (2) site structure changes break scrapers—build robust parsers; (3) anti-scraping measures—use rate limiting and rotating proxies responsibly. 7. Schema evolution=source data structure changes over time. Challenge: new columns break existing pipelines. Solutions: schema-on-read, flexible formats (JSON/Parquet), schema registries, automated migration. 8. Generally True (APIs are structured/sanctioned), though rate limits can slow them. 9. True. 10. False (CDC typically requires an initial full load, then captures ongoing changes).

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