Apache Spark is an open-source distributed general-purpose cluster computing framework. The following gives an interface for programming the complete cluster with the help of absolute information parallelism as well as fault tolerance. The Apache Spark has its architectural groundwork in RDD or Resilient Distributed Dataset.

1. APACHE SPARK
INTERVIEW
QUESTIONS
AND ANSWERS
2021
Presented by Sprintzeal
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2. BASIC APACHE
SPARK
INTERVIEW
QUESTIONS
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1.What do you understand by Apache Spark?
Apache Spark is a cluster computing framework
that operates on a set of commodity hardware as
well as performs unification of data which means
and writing and reading of numerous data that to
from multiple sources. In Spark, a task is a work
that can either be a reduced task or a map task. The
context of Spark takes care of the implementation
of the job which also provides APIs in a variety of
languages. The languages are Scala, Python, and
Java.
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3. 2.HOW CAN YOU DIFFERENTIATE SPARK AND MAPREDUCE? WHICH ONE
IS FASTER AMONG SPARK AND MAPREDUCE?
The light offering doesn’t take place in the case of Spark due to which
there is no compulsory rule that reduces would come after the map.
Spark operates at a faster speed because it keeps the information in
memory as much as possible
There is a difference between Spark and MapReduce. In MapReduce, the
intermediate information will be stored in the HDFS. This takes a lot of time
for the user to access the information from a source.
We can say that Spark is faster as compared to MapReduce. There are certain
reasons which justify why Spark is faster than MapReduce. The reasons are:
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3.Say how much you know about the architecture of Apache
Spark. How can you run the applications of Apache Spark?
The Apache Spark application is generally composed of two programs
which are the Workers program and the Driver program. The function
of these two programs varies from each other. There lies a cluster
manager in between the two programs whose work is to interact with
two cluster nodes. The contact of Spark Content and Worker Nodes
can be maintained with the help of the cluster manager. The Spark
Context leads whereas the workers of the Spark follow the Spark
context.
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4.How can you define RDD?
RDD stands for Resilient Distributed
Datasets. RDD helps the user to
distribute the data across all the nodes. If
the user carries a huge amount of data
and if is not essential to store the data in
a single system, the user can spread the
information across all the nodes. The
partition or division can be called as a
subset of data which will needs to be
processed by a particular task.
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description

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