Question 1

What architecture are you following in your current project, and why?

Accepted Answer

**Section 1 — The Context (The 'Why')**
The primary challenge in choosing and justifying an architecture is alignment between technical complexity, operational cost, and business latency requirements. A naive monolithic pipeline fails when schema evolution hits, when a single component becomes a bottleneck, or when teams scale—causing coordination overhead and deployment risk....

Question 2

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 3

How do you handle data using AWS S3?

Accepted Answer

Patterns: (1) Raw and curated zones; prefixes/partitions. (2) Glue + Athena for querying. (3) Lifecycle for cost. (4) Versioning for critical. (5) SSE-S3/KMS. Example: raw/, curated/; Parquet for analytics....

Question 4

What is your cluster configuration?

Accepted Answer

Tailor. Example: '20-node EMR r5.4xlarge—64 vCPU, 1TB RAM. Airflow: 4 Celery m5.large. Snowflake: Large batch, X-Small ad-hoc. Kafka: 6 brokers, rf=3.' Include: type, count, storage, key configs. Relate to workload.

Question 5

What is your data volume?

Accepted Answer

Tailor. Example: '50TB/month—20TB ingest, 30TB derived. Largest fact 5TB. 2B events/day streaming. Archive ~200TB.' Daily/monthly, event rate, growth. Round if proprietary.

Question 6

How do you sort a dictionary based on values?

Accepted Answer

**Code:** `dict(sorted(d.items(), key=lambda x: x[1]))`. Or operator.itemgetter(1). reverse=True for desc. **Why:** Sorted output. **Production:** Items() for Py3. **Cost:** O(n log n).

Question 7

What is the difference between list1 = list2 and list1.copy()?

Accepted Answer

**Assignment vs Copy:** list1 = list2 → alias, same object. list1 = list2.copy() (or list2[:]) → shallow copy, new list, same element refs. Nested structures shared in shallow copy.

**When to Use:** Alias when you want to modify original through both refs. Shallow copy when you need independent list but elements can be shared....

Question 8

Explain Fact Table and Star Schema.

Accepted Answer

**Architectural Logic**: Fact table + dimensions = star schema; optimizes analytical query patterns. **Fact Table**: Measures (amount, quantity); FKs to dimensions; large; grain defined by dimensions. Additive or semi-additive. **Star Schema**: Fact surrounded by dimension tables; denormalized dimensions; simple structure. **Example**: fact_sales (sale_id, date_key, product_key, store_key, amount, qty); dim_date, dim_product, dim_store....

Question 9

Write a SQL query to find house with Avg(score) > 70.

Accepted Answer

SELECT house_id FROM scores GROUP BY house_id HAVING AVG(score) > 70. Add house name via JOIN if in different table. **Why**: Simple aggregation with HAVING. **Production**: Consider minimum sample size—HAVING COUNT(*) >= 5 AND AVG(score) > 70 to avoid noise.

Question 10

Compare Spark SQL vs. Hive Performance.

Accepted Answer

**Why comparison matters**: Migration and tool choice depend on performance delta. **Spark SQL**: In-memory; Catalyst optimizer; 10–100x faster for ad-hoc. Reads Hive tables via Hive Metastore. **Hive**: On-disk; MapReduce/Tez; slower. **Scalability trade-offs**: Spark SQL scales with cluster; Hive scales but slower per query. **Cost implications**: Same data; Spark = less compute time = lower cost for same workload....

HCL Data Engineer Interview Questions

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HCL Data Engineer Interview Questions

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