GDPR and similar data privacy laws impose specific technical obligations on data engineering pipelines that process personal data. Data engineers must implement the right to erasure (deleting a person's data when requested), data minimisation (not storing more personal data than necessary), purpose limitation (not using personal data for analytics without explicit consent), and data portability (providing a person's data in a machine-readable format). These are core pipeline design constraints that must be incorporated from the first schema design, not added as compliance afterthoughts.
Data residency requirements specify that certain categories of personal data must be stored and processed within a specific geographic jurisdiction. For cloud data engineering, residency requirements translate into infrastructure choices: the Snowflake account, S3 bucket, Airflow deployment, and BI tool must be provisioned in the correct AWS region or Azure region to ensure data never transits through a non-compliant geography. Multi-region pipelines that move data across regional boundaries may inadvertently violate residency requirements — validate region compliance as a CI/CD gate.
Analogy🏏Cricket
🏏 Think of it like cricket: OLTP is the IPL's live ticketing counter — it handles thousands of simultaneous seat reservations, each requiring a precise single-seat record update with immediate confirmation. Speed per transaction and data consistency under concurrent updates are everything. OLAP is the IPL's season statistics department — it runs complex analytical queries across every ball bowled in every match of every season to produce the published rankings, economy rates, and historical comparisons. No one books a seat through the statistics department, and no broadcaster calls the ticketing counter for Bumrah's career economy rate. The two workloads demand completely different systems. Just as the ticketing counter is built for speed and correctness on one seat at a time and would buckle if asked to tally a decade of attendance mid-sale, an OLTP row-store excels at single-record writes but chokes on full-table aggregation; and just as the statistics department pores over millions of past deliveries but would be hopeless at booking a live seat under contention, the OLAP columnar engine sweeps billions of rows yet is the wrong tool for a fast single-row update. The physical design of each — row-oriented for the counter, columnar for the stats desk — is what makes it superb at its own job and unfit for the other's.
🏏 Showing the Cricket analogy — a Cricket version isn’t available for this concept yet.