Question 1

What is the difference between SparkSession and SparkContext in Spark?

Accepted Answer

**SparkContext** (Spark 1.x): Low-level entry point for RDD operations. Manages cluster connections, configuration, and RDD creation. One active SparkContext per JVM. RDD-only.

**SparkSession** (Spark 2.0+): Unified entry point subsuming SparkContext, SQLContext, HiveContext, StreamingContext. Provides DataFrame, Dataset, SQL, and Structured Streaming APIs....

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

Write a Python function to check if a string is a palindrome.

Accepted Answer

**Architectural logic**: A palindrome reads the same forwards and backwards. We need to normalize (case, non-alphanumeric) and compare. **Approach 1 (string ops)**: `cleaned = "".join(c.lower() for c in s if c.isalnum()); return cleaned == cleaned[::-1]`—O(n) time, O(n) space. **Approach 2 (two-pointer)**: Compare from both ends; O(n) time, O(1) space if not normalizing....

Question 4

When would you architecturally choose Dataset[T] over DataFrame in a Scala Spark pipeline, and what are the scalability and portability trade-offs? Include type-safety benefits vs. operational constraints.

Accepted Answer

DataFrame is an untyped collection of Row objects with schema at runtime; Dataset[T] is typed with compile-time safety. In Scala, DataFrame = Dataset[Row]. Architectural why: Dataset enables domain modeling (e.g., Dataset[Order])—catch errors at compile time, better IDE support, and Catalyst can optimize typed encoders. Scalability: both use Tungsten and Catalyst; Dataset adds encoder overhead but marginal for most workloads. Portability trade-off: PySpark has only DataFrame—no typed Dataset....

Question 5

Design a cost-aware resource strategy for a Databricks workload with spiky and batch jobs. Explain Dynamic Resource Allocation, when to disable it, and how min/max executors and spot instances affect cost and SLAs.

Accepted Answer

**Section 1 — The Context (The 'Why')**
Databricks workload cost explodes when clusters run idle, jobs are over-provisioned, or spot preemption causes thrashing. The challenge is aligning DPU allocation to actual parallelism while maintaining SLA....

Question 6

Command to Read JSON Data and Options

Accepted Answer

**Spark**: spark.read.json('path') or spark.read.format('json').load('path'). **Options**: schema (explicit StructType for perf), multiLine (pretty-printed), dateFormat, primitivesAsString. **Nested**: Provide schema or from_json. **Streaming**: readStream.schema(schema).json(path)....

Question 7

Daily Data Volume - quantify

Accepted Answer

**Quantify**: Example—500GB raw, 200GB curated daily. Peak 2TB month-end. Include records and bytes. **Source**: Pipeline metrics (Glue, Dataflow). **Why**: Sizes infrastructure; capacity planning; cost....

Question 8

Describe a project you worked on, focusing on the data pipeline and your role.

Accepted Answer

**STAR**: **Situation**: Needed clickstream analytics. **Task**: Build pipeline. **Action**: Designed Kafka ingest, Flink processing, ClickHouse storage. Led design; implemented core components. **Result**: Sub-minute latency; 10M events/day....

LTIMindtree Data Engineer Interview Questions

+9 More Questions with Expert Answers

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LTIMindtree Data Engineer Interview Questions

+9 More Questions with Expert Answers

Other Companies