When Spark operates on any dataset, it remembers the instructions. When a transformation such as a map() is called on an RDD, the operation is not performed instantly. Transformations in Spark are not evaluated until you perform an action, which aids in optimizing the overall data processing workflow, known as lazy evaluation.
Posted Date:- 2021-09-25 05:44:55
Illustrate some demerits of using Spark.
What do you understand by worker node?
What file systems does Spark support?
What are the different types of operators provided by the Apache GraphX library?
What is the role of Catalyst Optimizer in Spark SQL?
How can you connect Hive to Spark SQL?
How is machine learning implemented in Spark?
What are the different levels of persistence in Spark?
What do you mean by sliding window operation?
Explain Caching in Spark Streaming.
What do you understand about DStreams in Spark?
How to connect the azure storage account in the Databricks?
How to import third party jars or dependencies in the Databricks?
How is Streaming implemented in Spark? Explain with examples.
Name the components of Spark Ecosystem.
Define functions of SparkCore.
What do you understand by Transformations in Spark?
Define Partitions in Apache Spark.
What is Executor Memory in a Spark application?
How do we create RDDs in Spark?
Is there any benefit of learning MapReduce if Spark is better than MapReduce?
Do you need to install Spark on all nodes of YARN cluster?
What are the various functionalities supported by Spark Core?
How can you connect Spark to Apache Mesos?
What makes Spark good at low latency workloads like graph processing and Machine Learning?