5 Reasons on When to use RDDs
- You want low-level transformation and actions and control on your dataset;
- Your data is unstructured, such as media streams or streams of text;
- You want to manipulate your data with functional programming constructs than domain specific expressions;
- You don’t care about imposing a schema, such as columnar format while processing or accessing data attributes by name or column; and
- You can forgo some optimization and performance benefits available with DataFrames and Datasets for structured and semi-structured data.
What happens to RDDs in Apache Spark 2.0?Are RDDs being relegated as second class citizens? Are they being deprecated? The answer is a resounding NO! What’s more is you can seamlessly move between DataFrame or Dataset and RDDs at will—by simple API method calls—and DataFrames and Datasets are built on top of RDDs.
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