Question 1

Write an SQL query to find the second-highest salary from an employee table.

Accepted Answer

**Using subquery with MAX**:
```sql
SELECT MAX(salary) AS second_highest
FROM employee
WHERE salary < (SELECT MAX(salary) FROM employee);
```

**Using LIMIT/OFFSET** (MySQL, PostgreSQL):
```sql
SELECT DISTINCT salary
FROM employee
ORDER BY salary DESC
LIMIT 1 OFFSET 1;
```

**Using DENSE_RANK** (ANSI SQL, most robust):
```sql
SELECT salary
FROM (
  SELECT salary, DENSE_RANK() OVER (ORDER BY salary DESC) AS rk
  FROM employee
) t
WHERE rk = 2;
```

**Architectural Logic & Trade-offs**:
- **Subque...

Question 2

Demonstrate the difference between DENSE_RANK() and RANK()

Accepted Answer

**RANK()**: Same rank for ties; skips subsequent ranks (e.g., 1, 2, 2, 4, 5). **DENSE_RANK()**: Same rank for ties; no gaps (e.g., 1, 2, 2, 3, 4). **Why it matters**: RANK preserves "position" semantics (e.g., 4th place); DENSE_RANK gives consecutive integers useful for filtering (e.g., TOP 10). **Example**: `SELECT name, salary, RANK() OVER (ORDER BY salary DESC) AS rk, DENSE_RANK() OVER (ORDER BY salary DESC) AS dense_rk FROM employee`....

Question 3

Discuss differences between ROW_NUMBER(), RANK(), and DENSE_RANK(), and provide examples from your projects.

Accepted Answer

**ROW_NUMBER()**: Unique sequential numbers (1, 2, 3...); no ties—deterministic only with ORDER BY uniqueness. **RANK()**: Same rank for ties; skips (1, 2, 2, 4). **DENSE_RANK()**: Same rank for ties; no gaps (1, 2, 2, 3). **Project examples**: ROW_NUMBER() to deduplicate events by (user_id, event_time) keeping first—critical when upstream sends duplicates. DENSE_RANK() for 'top 10 products per category' reports—avoids gaps when filtering....

Question 4

Explain the differences between a Data Lake and a Data Warehouse.

Accepted Answer

**Data Lake**: Low-cost object storage (S3, ADLS) for raw, semi-structured, unstructured data. Schema-on-read; used for exploratory analytics, ML, archival. **Data Warehouse**: Structured, curated storage optimized for SQL; schema-on-write; used for BI and reporting. **Why both exist**: Lakes offer flexibility and cost at scale; warehouses offer query performance and concurrency....

Question 5

Explain the differences between Data Warehouse, Data Lake, and Delta Lake

Accepted Answer

**Data Warehouse**: Structured, schema-on-write; optimized for SQL analytics (Snowflake, BigQuery). High compute cost, fast queries. **Data Lake**: Raw/semi-structured object storage (S3, ADLS); schema-on-read; low cost, flexible. **Delta Lake**: Open-source storage layer on a data lake adding ACID transactions, schema enforcement, time travel, upserts. **Why the distinction**: Warehouses scale compute and storage together; lakes decouple them....

Question 6

Explain the differences between Repartition and Coalesce. When would you use each?

Accepted Answer

**Repartition(n)**: Full shuffle; creates exactly n partitions. Can increase or decrease. **Coalesce(n)**: Merges partitions without full shuffle; only decreases. **Why it matters**: Shuffle is expensive—network and disk I/O. Coalesce avoids shuffle when reducing partitions by merging within existing partitions. **When Repartition**: Increasing partitions, fixing skew (repartition by key), or before a join to align partition counts....

Question 7

What is the difference between partitioning and bucketing in Spark, and when would you use bucketing?

Accepted Answer

**Partitioning**: Physically divides data by column values (e.g., date, region); enables partition pruning; one directory per partition value. **Bucketing**: Divides data within a partition into a fixed number of files via hash of bucketing column(s); co-locates same-key rows. **When to bucket**: Frequent joins or group-bys on a column (e.g., user_id). Same bucket count on both sides enables sort-merge join without shuffle....

Question 8

Can you explain the difference between OLTP and OLAP?

Accepted Answer

**OLTP**: Optimized for many small transactions (inserts, updates, deletes). Row-oriented, normalized, high concurrency. Examples: MySQL, PostgreSQL. **OLAP**: Optimized for complex analytical queries and aggregations on large datasets. Column-oriented, denormalized (star/snowflake). Examples: Snowflake, BigQuery, Redshift. **Why the split**: Different access patterns; mixing them degrades both. OLTP needs low latency and ACID; OLAP needs scan throughput....

Question 9

Describe a scenario where partitioning and bucketing would improve query performance.

Accepted Answer

Situation: An events table with billions of rows serving time-range and user-level analytics. Task: Achieve sub-second query latency while controlling storage and compute costs. Why Partitioning: Partition pruning at read time eliminates entire data scans—a query filtering by date_range only touches relevant partition dirs. This reduces I/O by orders of magnitude (e.g., 365 partitions → scan 1 vs all)....

Question 10

Describe a time when you had to optimize a slow SQL query. What steps did you take?

Accepted Answer

**Situation**: A critical exec report was timing out at 30+ minutes; SLA was 5 minutes. **Task**: Diagnose and fix without changing business logic. **Action**: I ran EXPLAIN (ANALYZE) and found: (1) missing index on join key causing full table scan, (2) cross join that could be inner join with a filter, (3) filter in HAVING that could move to WHERE to reduce rows early, (4) unnecessary ORDER BY on a subquery....

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