How do I approach Data Manipulation (SQL/Python) interview questions?

Data Manipulation (SQL/Python) questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master data manipulation (sql/python) interviews.

What difficulty level is this interview question?

This is a Medium difficulty Data Manipulation (SQL/Python) question, commonly asked during HR Screen rounds at Instacart.

What role is this question designed for?

This question is commonly asked for Data Scientist candidates at Instacart during technical interviews.

Explain handling very large datasets

Last updated: Mar 29, 2026

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.

Instacart

Oct 13, 2025, 9:49 PM

Data Scientist

HR Screen

Data Manipulation (SQL/Python)

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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