Spark has 3 data representation
- RDD(Resilient Distributed Database)
- Is a collection of elements, that can be divided across multiple nodes in a cluster for parallel processing.
- It is also fault tolerant collection of elements, which means it can automatically recover from failures.
- Is immutable, we can create RDD once but can’t change it.
2.Dataset:
- It is also a distributed collection of data.
- A Dataset can be constructed from JVM objects and then manipulated using functional transformations (map, flatMap, filter, etc.).
- Dataset API is only available in Scala and Java. It is not available in Python and R.
- DataFrame:
- Is a distributed collection of data organized into named columns.
- It is conceptually equivalent to a table in a relational database or a data frame.
- It is mostly used for structured data processing.
- In Scala, a DataFrame is represented by a Dataset of Rows.
- A DataFrame can be constructed by wide range of arrays for example, existing RDDs, Hive tables, database tables.
History of Spark API
The snapshot shows the history of dataframes.
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