Data Engineer Interview Questions and Answers
Experienced / Expert level questions & answers
Ques 1. Explain the concept of partitioning in a distributed database.
Partitioning involves dividing a large table into smaller, more manageable parts based on certain criteria. It helps in parallel processing and efficient data retrieval.
Example:
Partitioning a table based on date, so each partition contains data for a specific time range.
Ques 2. What is the CAP theorem, and how does it relate to distributed databases?
The CAP theorem states that a distributed system cannot simultaneously provide all three guarantees: Consistency, Availability, and Partition tolerance. Distributed databases must trade off between these guarantees.
Example:
Choosing between consistency and availability in a distributed database during a network partition.
Ques 3. Explain the concept of data sharding in a distributed database.
Data sharding involves dividing a database into smaller, independent parts (shards) that can be distributed across multiple servers. It helps improve scalability and performance.
Example:
Sharding a user database based on geographic regions to distribute the load and enhance query performance.
Ques 4. How do you handle data skew in a distributed computing environment?
Data skew occurs when certain partitions or shards have significantly more data than others. Techniques to handle data skew include re-partitioning, data pre-processing, and using advanced algorithms for data distribution.
Example:
Re-partitioning a dataset based on a different key to distribute the data more evenly in a Spark job.
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