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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.
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