Are you thinking of taking the Apache Spark certification training? Well, then brace yourself for the Big Data industry because Apache Spark has made quite the name in it!
It is the age of machine learning, Artificial Intelligence, and Data Science. Distribution and computation of massive volumes of data are easy, thanks to algorithms and technology. Most organizations deal with Big Data, and hence, it is imperative to have tools that quicken data processing and analytics. A promising name in this domain is Apache Spark, a cluster computing framework that meets organizations’ expectations through lightning-fast data processing, data querying, and data analytics report generation. An open-source framework, Apache Spark, can process and analyze massive data volumes while distributing data across a cluster of machines. No doubt, Apache Spark is regarded to be the future of the Big Data industry.
This article will focus on the top 15 Apache Spark questions asked during interviews and the various job roles that require the knowledge of Apache Spark.
Roles That Require Knowledge of Apache Spark
Almost every industry requires Big Data specialists. There is a bright career scope in IT, law enforcement, finance, farming, manufacturing, government, science, medicine, retail, and technical services. Whether you are interested in data management and storage or software development and security, the Big Data industry has something to fit your competency and job preferences. However, the knowledge of Apache Spark is a must for most job roles.
So before you sign up for an Apache Spark certification training, check out this list of the top job roles that require knowledge of Apache Spark:
- Big Data Engineer
- Business Intelligence Analyst
- Data Scientist
- Data Analyst
- Data Architect
- Data Modeler
- Data Warehouse Manager
- Database Manager
- Database Administrator
- Database Developer
- Security Engineer
Top 15 Apache Spark Interview Questions
The Apache Spark certification training is just the first step towards a bright career in the Big Data domain. But preparing for a job interview for Apache Spark Developer or Big Data Developer is another test altogether. So here is a compilation of questions related to the Apache Spark ecosystem that will help you crack the interviews:
1. What is Apache Spark?
Apache Spark is an open-source, cluster computing framework. It combines batch, streaming, and interactive analytics for the development of fast and unified Big Data applications.
2. What are some benefits of using Apache Spark?
- When it comes to large-scale data processing, Spark is significantly faster than Hadoop.
- The APIs of Apache Spark are easy to use for operation on large datasets.
- Spark is a unified package with libraries that support streaming data, SQL queries, graph processing, and machine learning.
3. What are some differences between Apache Spark and Hadoop?
- While Spark has an interactive mode, Hadoop MapReduce does not have an integrated interactive mode except Hive and Pig.
- Spark allows batch processing, streaming, and machine learning in the same cluster. With Hadoop, you can just process a stored batch of data.
- Spark caches the partial results over a memory of distributed workers. But Hadoop MapReduce is completely disk-oriented.
4. What are the languages that Apache Spark supports for the development of Big Data applications?
Apache Spark supports the following programming languages:
- Java
- Scala
- Python
- R
- Clojure
5. What are some of the common Spark ecosystems?
Spark ecosystems include:
- Spark SQL for SQL developers
- MLLib for machine learning algorithms
- GraphX, applicable for graph computation
- Spark Streaming, used for streaming data
- BlinkDB enables interactive queries over vast data.
6. What are sparse vectors in Apache Spark?
A sparse vector comprises two parallel arrays, one for values and the other for indices. These vectors save space by storing the non-zero entries.
7. What is RDD?
Resilient Distributed Datasets, or RDD, is one of Spark’s fundamental data structures. RDDs are read-only, partitioned, and distributed collections of immutable, catchable, lazily evaluated, and resilient data. The primary use of RDDs is for fault-tolerant, in-memory computations on big clusters.
8. What is meant by the immutability of RDD?
The property of immutability means that you can make no changes after creation and value assignment. By default, Spark is immutable. Hence, it does not allow modifications and updates.
9. What is the meaning of catchable?
Catchable in Spark means that instead of going to the disk, all the data is kept in memory for computation. Hence, Spark can catch the data almost 100 times faster as compared to Hadoop.
10. What is meant by ‘lazily evaluated’?
In Spark, ‘lazily evaluated’ means that when you execute a group of programs, it is not necessary to evaluate them immediately.
11. What are the different cluster managers in Apache Spark?
Apache Spark supports three different cluster managers:
- YARN
- Standalone deployments
- Apache Mesos
12. What is the responsibility of the Spark Engine?
The Spark engine is responsible for monitoring, distributing, and scheduling the application across the entire cluster.
13. What is the use of Spark Streaming?
Spark Streaming allows the real-time processing of the streaming data API. It can collect streaming data from various sources like web server log files, stock market data, social media data, or Hadoop ecosystems like Kafka and Flume.
14. How can you minimize data transfers in Spark?
Minimizing data transfers can help write fast and reliable Spark programs. It can be done by:
- Using accumulators
- Using broadcast variables
- Avoiding operations that trigger shuffles like repartition and ByKey.
15. What are accumulators and broadcast variables?
Accumulators are variables that help in updating data points in parallel across executors.
Broadcast variables are read-only variables that are cached on every machine and eliminate the need for shipping copies of a variable with every task.
Conclusion
Big Data has become an inseparable aspect of businesses. The quintillion bytes of data generated every day must be collected, stored, secured, and analyzed. This rapid growth in the Big Data domain has opened a massive job market that requires Big Data Specialists. In this scenario, having the Apache Spark certification training can be a feather in the cap of professionals aspiring to make it big in the Big Data industry. We hope that the questions discussed in this article will come in handy for those preparing for job interviews.
