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Explain database drivers/connectors and their use cases.

Spark/Big Datahard0.6 min readPremium
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
Gartner
Key Concepts Tested
optimizationpartitionpythonsparksql
Expert AnswerPremium
112 wordsInterview-ready
**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. Database drivers/connectors provide programmatic access to databases. JDBC: Java-based, used by Spark, Java apps; ODBC: language-agnostic; Python: pyodbc, psycopg2, mysql-connector; Spark: spark.read.jdbc() for bulk loads. Use cases: JDBC for Spark DataFrame reads from RDBMS; Kafka JDBC connector for CDC; ODBC for BI tools....
The complete answer continues with detailed implementation patterns, architectural trade-offs, and production-grade considerations. It covers performance optimization strategies, common pitfalls to avoid, and real-world examples from companies like Gartner. The answer also includes follow-up discussion points that interviewers commonly explore.

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