Explain handling very large datasets

Quick Overview

This question evaluates a candidate's competence in ingesting and processing very large datasets, covering storage formats and partitioning, memory and compute constraints, schema evolution, data quality checks, indexing strategies, tool selection in SQL/Python ecosystems, and code-level performance optimizations.

Explain handling very large datasets

Company: Instacart

Role: Data Scientist

Category: Data Manipulation (SQL/Python)

Difficulty: medium

Interview Round: HR Screen

Describe a project where you ingested and processed a dataset of at least 500 million rows or 1 TB end-to-end. Detail storage formats and partitioning, memory and compute constraints, schema evolution, data quality checks, indexing strategies, and tools chosen (e.g., Spark SQL vs. Pandas vs. BigQuery) and why. Provide before/after run times and cost, and a code-level optimization you used (e.g., vectorization, predicate pushdown, window functions, bucketing). How would your approach change if limited to a single machine with 32 GB RAM?

Quick Answer: This question evaluates a candidate's competence in ingesting and processing very large datasets, covering storage formats and partitioning, memory and compute constraints, schema evolution, data quality checks, indexing strategies, tool selection in SQL/Python ecosystems, and code-level performance optimizations.

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Oct 13, 2025, 9:49 PM
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Describe a project where you ingested and processed a dataset of at least 500 million rows or 1 TB end-to-end. Detail storage formats and partitioning, memory and compute constraints, schema evolution, data quality checks, indexing strategies, and tools chosen (e.g., Spark SQL vs. Pandas vs. BigQuery) and why. Provide before/after run times and cost, and a code-level optimization you used (e.g., vectorization, predicate pushdown, window functions, bucketing). How would your approach change if limited to a single machine with 32 GB RAM?

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