3.5. Reflection Checkpoint: Data Ingestion Mastery

Key Takeaways

Before proceeding to Phase 3, ensure you can:

• Choose between full and incremental loads based on data volume and change tracking capabilities
• Explain Type 2 SCD and implement it to preserve historical dimension values
• Select the appropriate data store (Lakehouse, Warehouse, KQL Database) based on query language and workload
• Differentiate between database mirroring (Azure SQL, Cosmos DB) and metadata mirroring (Databricks)
• Configure Spark Structured Streaming with proper checkpointing to Delta tables
• Select the appropriate windowing function (tumbling, hopping, session) for streaming aggregations

Connecting Forward

In Phase 3, you'll learn to monitor and optimize the data pipelines you've built. The ingestion patterns from Phase 2 become the subjects of performance tuning and error handling in Phase 3.

Self-Assessment Questions

1. Your daily sales pipeline processes 50 million records, but only 100,000 change each day. The source database has a ModifiedDateTime column. What loading pattern would you implement, and how would you track progress?

2. A customer dimension needs to track address changes for historical sales analysis by region. A colleague recommends Type 1 SCD for simplicity. Why might this cause problems, and what would you recommend instead?

3. Your real-time dashboard shows sensor data with 5-minute latency, but the business requires sub-second updates. The current architecture uses batch notebooks. What components would you change to achieve the latency requirement?