What are the key differences between Apache Spark’s RDDs and DataFrames, and when would you choose to use one over the other in a Spark application?

1 Answers
Answered by suresh

```html

Key Differences between Apache Spark's RDDs and DataFrames

Key Differences between Apache Spark's RDDs and DataFrames

Apache Spark offers two main abstractions for working with distributed data: RDDs (Resilient Distributed Datasets) and DataFrames. Here are the key differences between them:

  • RDDs:
    • Low-level abstraction representing distributed collection of data.
    • Immutable and fault-tolerant.
    • Suitable for low-level transformations and actions on data.
  • DataFrames:
    • Higher-level abstraction representing distributed collection of data organized into named columns.
    • More optimized for structured query-like operations.
    • Supports SQL queries, aggregation, and filtering operations.

When to choose one over the other in a Spark application:

  • Use RDDs when you need fine-grained control over data and low-level transformations.
  • Use DataFrames when you require high-level optimizations and are working with structured data.

```

Answer for Question: What are the key differences between Apache Spark’s RDDs and DataFrames, and when would you choose to use one over the other in a Spark application?