Databricks Certified Data Engineer - Professional
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In the context of Apache Spark, data partitioning plays a crucial role in optimizing data processing workflows. Consider a scenario where you are working with a large dataset that exhibits significant skewness in data distribution, leading to uneven workload distribution across nodes. Your goal is to optimize the performance of a Spark job by selecting an appropriate partitioning strategy. Given the constraints of minimizing processing time and ensuring balanced workload distribution, which of the following strategies would you choose and why? Please select the best option from the choices provided below.
Explanation:
Implementing a custom partitioning strategy, such as hash partitioning or range partitioning, is the most effective approach to address data skewness in a large dataset. This strategy ensures that data is evenly distributed across partitions, which helps in balancing the workload across nodes and significantly improves the performance of data processing tasks. While the 'repartition' and 'coalesce' functions can adjust the number of partitions, they do not inherently solve the problem of data skewness. Avoiding repartitioning may lead to inefficient use of resources due to uneven workload distribution. Therefore, a custom partitioning strategy is the best choice for optimizing performance in scenarios with skewed data distribution.