3.9.3: Hands-On Lab Exercise

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Page ID: 58295

Felix Amoruwa
De Anza College

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Lab: Designing a Data Collection and Ingestion Pipeline

Scenario: EventPulse is a concert ticketing platform. They need to collect and ingest data from: (1) ticket sales transactions (SQL database), (2) website clickstream (server logs), (3) social media mentions (Twitter API), (4) venue IoT sensors (temperature, crowd density), and (5) customer satisfaction surveys (online forms).

Instructions:

Source Classification (15 min): For each data source, classify: collection method, data format, internal/external, structured/semi/unstructured, and estimated volume.
Ingestion Design (25 min): For each source, specify: batch or streaming, ingestion pattern (ETL/ELT/CDC), target system, and frequency. Create an architecture diagram.
Pipeline Challenges (15 min): Identify 3 specific challenges this pipeline will face (schema evolution, latency, quality) and propose solutions.
Quality Gates (15 min): Design data quality checks that run during ingestion for at least 3 sources.
Scalability Plan (20 min): EventPulse expects 10x traffic during major concert announcements. How does each pipeline component handle the spike?

Deliverables:

Source classification table, architecture diagram, challenge/solution document, quality gate specifications, scalability plan.

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