Answer / Mahendra Singh Kotwal

A transformation on an RDD in Apache Spark creates a new logical plan without executing the operations immediately. It returns a new RDD with the transformed data and does not trigger any physical data processing until an action is called. Examples of transformations include map(), filter(), join(). An action, on the other hand, triggers the execution of the entire logical plan and generates a physical result such as collect(), count(), saveAsTextFile(). Transformations allow users to chain multiple operations together without executing them immediately, while actions force the computation to be executed.

Can you run spark without hadoop?

1 Answers  

---

How you can remove the element with a critical present in any other Rdd is Apache spark?

1 Answers  

---

What is a "Parquet" in Spark?

1 Answers  

---

Is there any benefit of learning mapreduce if spark is better than mapreduce?

1 Answers  

---

What is spark tool in big data?

1 Answers  

---

What is meant by in-memory processing in Spark?

1 Answers  

---

By Default, how many partitions are created in RDD in Apache Spark?

1 Answers  

---

Explain the operations of Apache Spark RDD?

1 Answers  

---

Is spark difficult to learn?

1 Answers  

---

What is the difference between persist

1 Answers  

---

How is hadoop different from spark?

1 Answers  

---

What is spark rdd?

1 Answers  

---