Reportedly a fact has been introduced that Apache Spark's market share is been around 4.9 to 5 %. So there is a lot in the market for everyone to gain. Many of the international firms have opened the doors for the research and development with the Apache Spark. With the absolute preparation through Apache Spark interview questions and practising the advanced technology, you can definitely achieve your dream job of an Apache Spark developer.
Apart from the fundamental proficiency, a candidate might also be asked skills in Hadoop, hive, Sqoop and many more. You will get a perfect combination of Apache spark interview questions for fresher as well as experienced candidates here. In the most specific segment like Spark SQL programming, there are enough job opportunities. If you have given a thought to it then keep yourself assure with your skills and below listed Apache Spark interview questions.
Apache Spark is basically a processing framework which is extremely fast and convenient to use. With an advanced execution engine supporting it offers the cyclic data flow and in-memory computation. Apache spark can also run on Hadoop, cloud or standalone. It is capable to access the diverse data including Cassandra, HDFS, HBase.
On a general note, the most essential features of Apache Spark are-
Yes there are several segments on which they can be differentiated. Few of them are-
|Speed||It is almost 100 times faster than Hadoop||It has moderate speed|
|Processing||Offers real time and batch processing functionality||It offers batch processing only|
|Difficulty||It has high level modules hence it is easy||It is tough to learn|
|Recovery||It allows the partition recovery||MapReduce|
|Interactivity||It has interactive modes||Other than Pig and Hive, it has no interactive mode|
Apache Spark supports the languages Java, Python, Scala and R. among them Scala and Python have interactive shares for Apache Spark and Scala shell can be easily accessed through the ./bin/spark-shell and Python can be accessed through ./bin/pyspark. Among them, Scala is the most popular because Apache Spark is written in Scala.
The benefits are –
The YARN is a key feature in Spark which provides a central resource management for most of the operational deliveries across a cluster. It is also a container manager like Mesos. Spark can easily run on YARN which eventually emphasizes a binary distribution of Apache Spark built on its support.
Apache Spark is far better than MapReduce but still learning MapReduce is essential. MapReduce is a paradigm which is even used by Spark as big data tools. When the data is large and grows bigger, in that case, MapReduce is much relevant. Data tools like pig and hive convert their message queries into MapReduce in order to optimize them properly.
Partitions are done in order to simplify the data as they are the logical distribution of entire data. It is similar to the split in MapReduce. In order to enhance the processing speed, this logical distribution is carried out. Each and every association in Apache Spark is a partitioned RDD.
As the major logical data units in Apache Spark, RDD possesses a distributed collection of data. It is a read-only data structure and you cannot change the original format but it can always be transformed into a different form with the changes. The two operations which are supported by RDD are -
There are three different cluster manager in Spark which are as-
The write only variables which are initially executed once and send to the workers are accumulators. On the basis of the logic written, these workers will be updated and sent back to the driver which will process it on the basis of logic. A driver has the potential to exercise accumulator’s value.
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