SQL + Data Engineering crossover topics

Absolutely, these SQL + Data Engineering crossover topics are essential in modern interviews, especially for Data Engineers, Analytics Engineers, and Platform Engineers working with tools like Databricks, Snowflake, and BigQuery.

✅ SQL + Data Engineering Crossover Topics (With Real Use-Cases + Interview Tips)

🔷 1. Z-Ordering, Clustering, and Caching

Feature	Databricks	Snowflake	BigQuery	Purpose
Z-Ordering	✅ (on Delta Tables)	❌	❌	Optimizes predicate filtering
Clustering	❌	✅ (Clustering Keys)	✅ (Clustering Columns)	Avoids full scans
Caching	✅ (in-memory cache at table/query level)	✅ (result & warehouse cache)	✅ (query cache)	Speeds up repeated queries

💡 Interview Tip:
“How would you optimize a large fact table in Databricks for faster querying over a filter column?”
Answer: Use Z-ORDER BY on the filter column and OPTIMIZE to cluster data accordingly.

🔷 2. Delta Lake – Time Travel, ACID, and Data Lineage

Feature	Delta Lake	Use Case
ACID transactions	✅	Safe updates/inserts in data lakes
Time Travel	✅ (`VERSION AS OF` or `TIMESTAMP AS OF`)	Rollbacks, audits
Schema Evolution	✅ (`MERGE`, `ALTER TABLE`)	Auto-add columns in changing pipelines
Data Lineage	✅ (with Unity Catalog)	Audit trail and column-level traceability

💡 Interview Tip:
“How does Delta Lake handle concurrent writes and ensure consistency?”
Answer: By maintaining ACID transactions using the transaction log (_delta_log) and versioned files.

🔷 3. Lakehouse SQL vs Traditional SQL Patterns

Feature	Traditional RDBMS (Oracle/PostgreSQL)	Lakehouse SQL (Databricks/Snowflake)
Joins & Window Functions	✅	✅
Structured + Semi-Structured	❌ (limited)	✅ (JSON, Parquet, etc.)
Time Travel	❌	✅
Schema Evolution	Rigid (`ALTER TABLE`)	Flexible (`MERGE`, `Auto-merge`)
Data Types	Fixed schemas	Mixed (struct, arrays, maps)
Streaming + Batch together	❌	✅ (Delta Lake supports both)

💡 Interview Tip:
“How does SQL evolve in the Lakehouse architecture compared to traditional DWH?”

Answer: SQL now supports flexible schemas, time travel, native JSON handling, incremental ingestion, and auto-merge/update logic for both batch and streaming sources.

🔷 4. Data Engineering Usage of SQL

Component	Role of SQL
ETL Pipelines	Data extraction, transformation logic
Validation Checks	Null checks, duplicates, threshold tests
Reconciliation	Compare staging vs final tables
Audit Trails	Temporal or log-based views
Orchestration (Airflow)	Run SQL tasks, trigger next DAG steps

-- Example: Validate record count post-load
SELECT COUNT(*) FROM staging.orders
WHERE load_date = CURRENT_DATE;

💡 Interview Tip:
“Describe how SQL helps in ensuring data quality in your pipelines.”
Answer: Validation queries with threshold checks and row comparisons, embedded in DAGs (Airflow, Prefect) before triggering downstream jobs.

✅ Resources to Practice

✅ Dataset: [orders.csv], [users.csv], [events.json], [sales.parquet]
✅ Practice SQL: Use BigQuery Public Datasets or [Databricks Sample Datasets]
✅ Notebooks: Delta Lake + SQL versioning examples
✅ Challenges:
- “Optimize a slow query on a 10M-row Delta table”
- “Design a pipeline with schema evolution and upserts”

Here’s a One-Page SQL Interview Revision Sheet (Cheat Sheet) covering all critical topics—designed especially for Data Engineers, Analysts, and Developers facing SQL interviews.

🧠 SQL Interview Revision Sheet (2025)

🔹 1. SQL Core Topics

Topic	Key Concepts
DDL/DML/DCL/TCL	CREATE, INSERT, UPDATE, DELETE, GRANT, COMMIT, etc.
Constraints	PK, FK, UNIQUE, CHECK, NOT NULL
Data Types	VARCHAR, INT, DATE, JSON, etc.
Operators	=, <>, IN, EXISTS, LIKE, BETWEEN

🔹 2. Joins & Subqueries

Type	Description
INNER, LEFT, RIGHT, FULL	Table merging
CROSS JOIN	Cartesian product
SELF JOIN	Same table join
Correlated Subquery	Executes per row; expensive
Non-Correlated Subquery	Executes once

🔹 3. Advanced SQL Functions

Function Type	Examples
Aggregate	COUNT, SUM, AVG, MIN, MAX
Window	RANK, DENSE_RANK, ROW_NUMBER, LEAD, LAG
Analytical	PARTITION BY, ORDER BY with window
NVL / CASE	Handling NULLs and conditions
DECODE	Oracle’s CASE alternative

🔹 4. PL/SQL & Procedures

Topic	Notes
Triggers	BEFORE/AFTER INSERT/UPDATE/DELETE
Procedures	With IN/OUT parameters
Exception Handling	`WHEN OTHERS THEN...`
Control Flow	IF, LOOP, WHILE, FOR

🔹 5. Views & Indexing

Feature	Description
Views & Materialized Views	Virtual vs persisted views
Indexing	B-tree, Bitmap, Composite
EXPLAIN PLAN	Read cost, scan type
Index Hinting	`/+ INDEX(table index_name) /`

🔹 6. Partitioning & Performance

Partition Types	Notes
Range, Hash, List	Split large tables
Subpartitioning	Nested partitioning
Partition Pruning	Skip irrelevant partitions

🔹 7. Modern SQL (Cloud / Lakehouse)

Topic	Key Use
MERGE / UPSERT	Insert/update logic
Delta Lake SQL	Time Travel, ACID
Z-Order / Clustering	Databricks/Snowflake optimization
Caching	Query speed-up

🔹 8. Recursive Queries & Hierarchies

Query Type	Usage
WITH RECURSIVE	PostgreSQL, MySQL 8+
CONNECT BY PRIOR	Oracle-style hierarchies
LEVEL / SYS_CONNECT_BY_PATH	Tree traversal

🔹 9. Security & Injection Prevention

Concept	Best Practice
SQL Injection	Use bind variables / parameterized queries
User Privileges	GRANT, REVOKE carefully
Role-based Access	RBAC implementation

🔹 10. Set Ops, CTE, JSON/XML

Operation	Use
UNION / UNION ALL	Merge sets
INTERSECT / MINUS	Find common/diff rows
CTE	Modular queries using `WITH`
JSON/XML Query	JSON_TABLE, JSON_VALUE, XMLTABLE

🔹 11. SQL + Data Engineering

Concept	Importance
SQL in ETL	Transformations, validations
SQL + Airflow/SparkSQL	Scheduling & distributed SQL
Lakehouse SQL vs RDBMS	Semi-structured, schema evolution

💡 Common Interview Questions

Difference between RANK() and DENSE_RANK()?
How to debug a slow query?
Use case for CTE over subquery?
When to use window function?
Explain ACID in context of Delta Lake.

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