Apache Spark is a data processing framework that can quickly perform processing tasks on very large data sets and distribute data processing tasks across multiple computers, either on its own or in conjunction with other distributed computing tools. These two characteristics are critical in the worlds of big data and machine learning, where massive computing power is required to crunch through large data stores. Spark also alleviates some of the programming burdens associated with these tasks by providing a simple API that abstracts away much of the grunt work of distributed computing and big data processing. This article on Top 99 Apache Spark Interview Questions and Answers 2021 will provide you with information on best Apache Spark interview questions and Spark coding interview questions.
As we all know, Apache Spark is a thriving technology these days. As a result, it is critical to understand every aspect of Apache Spark as well as Spark Interview Questions. As a result, this blog will undoubtedly assist you in this regard. This blog will cover every aspect of Spark, as well as possible frequently asked Spark Interview Questions. Furthermore, we will do our best to provide each Question, so that your search for the best and all Top Spark Interview Questions will end here.
Top 99 Apache Spark Interview Questions and Answers 2021
- What are the methods for creating RDD in Spark?
The methods for creating RDD in Spark are as follows:
- By making use of parallelized collection
- By importing an external dataset from an already-existing RDD
- What is Apache Spark?
Apache Spark is a user-friendly and adaptable data processing framework. Spark can run on Hadoop, independently, or in the cloud. It is capable of evaluating a wide range of data sources, including HDFS, Cassandra, and others.
- Describe three data sources that are available in SparkSQL.
- Parquet file
- What are Accumulators?
Accumulators are variables that can only be written to. They are only initialized once and then distributed to the workers. These workers will update based on the logic that has been written and will send it back to the driver.
- What are the contents of Spark Eco System?
- Spark Core is a foundational engine for large-scale parallel and distributed data processing.
- Spark Streaming: This component is used to stream data in real time.
- Spark SQL: Uses Spark’s functional programming API to integrate relational processing.
- GraphX: Allows for the computation of graphs and graph-parallel graphs.
- MLlib: Allows machine learning to be performed in Apache Spark.
- Name the features of using Apache Spark.
- Support for Advanced Analytic Aids in the Integration of Hadoop and Existing Hadoop Data
- It enables you to run an application up to 100 times faster in memory and ten times faster on disc in a Hadoop cluster.
- Explain Parquet File.
Parquet is a columnar file format that is supported by a wide range of other data processing systems. Spark SQL supports both read and write operations on Parquet files.
- Explain the use of Broadcast Variables.
- Broadcast variables allow programmers to cache a read-only variable on each machine rather than shipping a copy of it with tasks.
- You can also use them to efficiently distribute a copy of a large input dataset to each node.
- Broadcast algorithms can also help you save money on communication costs.
- What are the disadvantages of using Spark?
- When compared to Hadoop, Spark consumes a massive amount of data.
- Work must be distrusted across multiple clusters, so you can’t run everything on a single node.
- Developers must exercise extreme caution when running their applications in Spark.
- Record-based window criteria are not supported by Spark streaming.
- What are common uses of Apache Spark?
Apache Spark is used for the following tasks:
- Interactive machine learning
- Flow processing
- Analyzing and processing data
- Processing of sensor data
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