Data Engineering Cheat Sheet 2026

The 30 highest-yield Data Engineering facts, distilled from real exam questions. Print it, save it as a PDF, or study it here — free, no sign-up.

1. A columnar format like Parquet improves analytical query speed mainly because it allows: → Reading only the columns a query needs
2. What is the purpose of a staging area in a data warehouse load process? → To temporarily hold raw extracted data before transformation and loading
3. Reducing shuffle volume in a distributed aggregation is best achieved by: → Pre-aggregating (combiner/map-side) before the shuffle
4. In an ELT pipeline, which component's processing power is most critical for the performance of the data transformation tasks? → The target data warehouse or data lake.
5. Which scenario most justifies using a streaming ingestion pattern instead of batch? → Fraud detection that must react within seconds
6. Which Azure service provides hierarchical namespace support optimized for big-data analytics? → Azure Data Lake Storage Gen2
7. A full-load ingestion is most appropriate when: → The source table is small or lacks a reliable change-tracking column
8. Which architecture uses both a batch layer and a speed layer to serve queries? → Lambda architecture
9. When comparing Apache Spark and Hadoop MapReduce for a big data project, which of the following is a primary advantage of using Spark? → Superior performance for iterative algorithms and interactive queries.
10. Which condition typically prevents an index from being used on a column? → Wrapping the column in a function in the WHERE clause
11. Which of the following is a core mechanism for achieving fault tolerance in a distributed data processing system? → Data replication
12. Which normal form requires that every non-key attribute is fully functionally dependent on the entire primary key, not just part of it? → Second Normal Form (2NF)
13. What does 'denormalization' do to a database design? → Intentionally adds redundancy to improve read/query performance
14. What does 'data lineage' tracking in orchestration provide? → A record of how data flows and transforms across tasks
15. A late-arriving dimension means a fact references a dimension member that: → Does not yet exist in the dimension when the fact loads
16. Which regulation governs the protection of personal data for residents of the European Union? → GDPR
17. What is the primary advantage of using a wide table (denormalized) in analytical workloads? → Fewer joins required at query time, improving performance
18. Why does idempotency matter in at-least-once processing? → It makes duplicate processing produce the same result
19. EXPLAIN ANALYZE differs from plain EXPLAIN because it: → Actually runs the query and reports real timings
20. Data skew in a distributed join causes which symptom? → A few tasks run far longer than the rest
21. Which approach grants permissions based on attributes like department, location, and clearance? → Attribute-Based Access Control (ABAC)
22. Which storage choice best supports globally distributed, low-latency reads of a content catalog? → Object storage fronted by a CDN
23. In a workflow scheduler, what is 'backfilling'? → Running a DAG for past dates that were missed or newly added
24. Bucketing a table by the join key primarily improves performance by: → Avoiding a shuffle since matching keys are co-located
25. An SCD Type 3 dimension tracks change by: → Storing a limited 'previous value' column alongside the current value
26. When would you choose ORC over Parquet as your data storage format? → When the primary processing engine is Apache Hive
27. What is the main trade-off of multi-region replication for a storage bucket? → Higher availability and lower read latency at increased cost
28. In Apache Flink, the purpose of allowed lateness after a watermark is to: → Update window results when late events still arrive
29. When estimated rows differ wildly from actual rows in a plan, the fix is usually to: → Update/refresh table statistics
30. Which technique helps a query engine skip files without opening them in a cloud data lake? → Min/max column statistics and partition pruning