Question 1

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 2

Explain your cloud-based data pipeline on AWS

Accepted Answer

Situation: Built data platform for analytics and ML. Architecture: S3 landing (API Gateway + Lambda, Kinesis Firehose) → Glue crawlers → Glue ETL (Parquet, curated) → Redshift/Athena. Streaming: Kinesis → Lambda/Kinesis Analytics → S3. Step Functions for multi-step. Medallion (raw/silver/gold). Result: Single lake; Glue Catalog; partition by date; encrypted; Terraform....

Question 3

Data Security in BFSI - encryption, IAM, auditing

Accepted Answer

**BFSI requirements**: Encryption at rest and in transit. IAM with MFA. Audit logging. Data masking for non-prod. Compliance: PCI-DSS, SOC2. Tokenization for PII. **Architecture**: Zero trust; classify data; least privilege....

Question 4

Data Storage and Retrieval Optimization techniques

Accepted Answer

**Techniques**: Partitioning (prune); columnar (Parquet, ORC); compression; indexing; caching. Query optimization (predicate pushdown, column pruning). Tier by access frequency. **Best practice**: Right format + partition + compression....

Question 5

Spark Coding: Using explode() Function to flatten nested arrays

Accepted Answer

**Why Explode:** Nested JSON (items array per order) → one row per item for joins/aggregation. Normalization for star schema.

**Functions:** explode()—one-to-many; null/empty arrays drop row. explode_outer()—keeps row, null for empty. posexplode()—adds position index. For multiple array cols: arrays_zip then explode.

**Scalability Risk:** Large arrays cause row explosion—1 row with 10K items → 10K rows. Can cause skew. Use explode then repartition or broadcast the small side for joins....

Question 6

Data Modeling and Airflow Scheduling - star schema, cron, backfill

Accepted Answer

**Architectural Logic**: Star schema + cron + backfill form a production batch pattern. **Star Schema**: Fact tables with conformed dimensions; optimizes BI query patterns. **Cron**: `schedule_interval='0 2 * * *'` (2 AM daily); align with source freshness. **Backfill**: `airflow dags backfill -s start_date -e end_date` or TriggerDagRun with execution_date. **Why catchup=False**: Prevents unintended historical runs; use explicit backfill DAG for controlled replays....

Question 7

Designing scalable data models - explain approach

Accepted Answer

**Section 1 — The Context (The 'Why')**
The primary challenge for this design in SQL is balancing scale, cost, and reliability. At scale, naive approaches fail: single points of failure cause cascades, schema evolution breaks consumers, and over-provisioning explodes cost. Failure modes include silent data loss from non-idempotent writes, cascading job failures from tight coupling, and operational burden from manual intervention....

Question 8

Kafka Basics - architecture, topics, partitions, producers, consumers, Zookeeper

Accepted Answer

**Section 1 — The Context (The 'Why')**
The primary challenge for this design in SQL is balancing scale, cost, and reliability. At scale, naive approaches fail: single points of failure cause cascades, schema evolution breaks consumers, and over-provisioning explodes cost. Failure modes include silent data loss from non-idempotent writes, cascading job failures from tight coupling, and operational burden from manual intervention....

Question 9

Query Performance in Redshift - optimization

Accepted Answer

Redshift optimization: (1) Sort keys (compound, interleaved). (2) Distribution keys—match join keys. (3) VACUUM and ANALYZE. (4) WLM for concurrency. (5) Column encoding. (6) Avoid excessive redistribution. (7) Use staging for large loads. (8) Late-binding views for schema flexibility. **Why it matters**: Design choices compound at scale—wrong approach can cause 100× overhead. **Scalability trade-offs**: Profile before optimizing; validate on sample then full....

Question 10

SQL Problem - multiple table joins and window functions

Accepted Answer

Multiple tables + window: join fact to dims, then apply window (PARTITION BY dim, ORDER BY date). Example: SELECT f.*, d.name, SUM(f.amount) OVER (PARTITION BY f.customer_id ORDER BY f.date) running_total FROM fact f JOIN dim_customer d ON f.customer_id = d.id. Use CTEs: with joined as (select ... joins), windowed as (select *, row_number() over (...) from joined) select * from windowed where ... **Why it matters**: Design choices compound at scale—wrong approach can cause 100× overhead....

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