2.4. Reflection Checkpoint: Core Data Concepts Mastery
Key Takeaways
Before proceeding, ensure you can:
- Immediately classify any data example as structured, semi-structured, or unstructured
- Explain why Parquet is preferred over JSON for analytics workloads (columnar vs. row, read optimization)
- Describe ACID properties without looking at notes and explain why they matter for transactions
- Distinguish which role builds pipelines (Data Engineer) versus which builds dashboards (Data Analyst)
- Determine when to use batch processing versus stream processing based on latency requirements
Scenario Synthesis
An e-commerce company collects product images (unstructured), customer reviews as JSON (semi-structured), and order transactions (structured). Orders must be processed in real-time with ACID compliance, while weekly sales reports require aggregating millions of rows.
Reflection Question: How would you classify each data type, and which workload pattern (OLTP vs. OLAP, Batch vs. Stream) applies to the order processing versus the weekly reporting?
Connecting Forward
In Phase 3, you'll apply these foundational concepts to relational databases on Azure. You'll see how structured data lives in Azure SQL services, how normalization prevents anomalies, and how SQL commands manipulate data. The OLTP concepts from this phase will directly inform your understanding of Azure SQL Database.
