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What are the steps to efficiently process 1 TB of data in Spark?

Spark/Big Datamedium0.5 min read

**Partitioning**: Input partitioned (e.g., date); repartition to 2–4x cores (e.g., 128–256 for 64 cores). **Format**: Parquet/ORC; columnar, predicate pushdown, compression. **Cluster**: 64–128 cores; executors 4–8 cores, 8–16GB....

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Frequency
Low
Asked at 1 company
Category
452
questions in Spark/Big Data
Difficulty Split
88E|81M|283H
in this category
Total Bank
1,863
across 7 categories
Asked at these companies
HashedIn
Key Concepts Tested
partitionsparksql

Why This Question Matters

This medium-level Spark/Big Data question appears frequently in data engineering interviews at companies like HashedIn. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (partition, spark, sql) will help you answer variations of this question confidently.

How to Approach This

Break this problem into components. Identify the core trade-offs involved, then walk the interviewer through your reasoning step by step. Demonstrate awareness of edge cases and production considerations - this is what separates good answers from great ones.

Expert Answer
96 words

Partitioning: Input partitioned (e.g., date); repartition to 2–4x cores (e.g., 128–256 for 64 cores).

Format: Parquet/ORC; columnar, predicate pushdown, compression.

Cluster: 64–128 cores; executors 4–8 cores, 8–16GB. Avoid spill; size memory.

Broadcast: Small lookup tables.

Predicate Pushdown: Filter on partition columns and row groups.

AQE: Enable for coalesce and skew.

Incremental: If possible, process only new/changed data.

Spot: Use spot for batch; 60–70% savings.

Scalability Trade-offs: 1TB / 128MB ≈ 8K partitions; default 200 shuffle partitions may underutilize. Tune spark.sql.files.maxPartitionBytes.

Cost Implications: 1TB at 10 min = ~100 core-hours. Spot + right-size = $5–15 for run.

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