Reviewed by Aditya Kumar · Last reviewed 2026-03-25
**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. Connect to Salesforce without manual credentials: (1) OAuth 2.0—use Connected App, store refresh token in secrets (Databricks Secrets,...
This hard-level Spark/Big Data question appears frequently in data engineering interviews at companies like Presidio. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (optimization, partition, python) will help you answer variations of this question confidently.
This is a senior-level question that tests architectural thinking. Lead with the high-level design, then drill into specifics. Discuss trade-offs explicitly - there is rarely one correct answer. Show awareness of scale, fault tolerance, and operational complexity.
Why it matters: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams.
Connect to Salesforce without manual credentials: (1) OAuth 2.0—use Connected App, store refresh token in secrets (Databricks Secrets, AWS Secrets Manager). (2) Named Credentials—Salesforce feature for delegated auth. (3) Server-to-Server (JWT)—for server integration. (4) Databricks: Store client_id, client_secret, refresh_token in secret scope; reference in notebook via dbutils.secrets.get(). Example: Use simple_salesforce or salesforce_bulk Python lib with token from secrets. Best practice: Rotate tokens; use least-privilege; never log credentials.
Scalability trade-offs: Partition/parallelism limits; single points of failure; horizontal vs vertical scaling. Cost implications: Sizing, spot vs reserved, optimization ROI.
Red Flag: Credentials in code. Pro-Move: 'OAuth; Secrets; rotate tokens.'
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According to DataEngPrep.tech, this is one of the most frequently asked Spark/Big Data interview questions, reported at 1 company. DataEngPrep.tech maintains an editor-reviewed database of 1,863 data engineering interview questions across 7 categories.