2020 was a year of data where big data and analytics made record-breaking progress through advanced technologies and outcome-centric analytics. The market prediction on big data further suggests that in upcoming years, business analytics will grow from $15 billion in 2015 to $203 billion at the end of 2022. No doubt, people are willing to gain more knowledge and skills in the field to take advantage of the opportunities available in the market. If you are also willing to take over the role of Spark professionals, then preparing with these top spark interview questions can give you a competitive edge in the job market.
Q1. What are the Features of Apache Spark?
Ans. 6 Best Features of Apache Spark
- Lighting-fast processing speed.
- Ease of use.
- It offers support for sophisticated analytics.
- Real-time stream processing.
- It is flexible.
- Active and expanding community.
Q2. What is Apache spark good for?
Ans: It utilizes in-memory caching and optimized query execution for fast queries against data of any size.
Q3. Explain the concept of RDD in Apache Spark?
Ans. 2 type or RDD in Apache Spark
- Hadoop Datasets
- Parallelized Collections
Q4. Various functions in Apache Spark?
Ans. Various functions of Spark Core are:
- Distributing, monitoring, and scheduling jobs on a cluster
- Interacting with storage systems
- Memory management and fault recovery
Q5. Components of the Spark Ecosystem?
- Spark Core
- Spark Streaming
- Spark SQL
Q6. Why do you need to prepare with spark interview questions?
Ans. As a professional when you appear in an interview, it is significant to know the right buzzwords to answer a question. With these top APAC spark interview questions, you can learn all the keywords you need to use to answer the industry-related questions to stand out in the crowd. In short, this spark interview questionnaire is your ticket to your next spark job.
Q7. How do you compare spark and Hadoop?
Ans. One of the first questions you can expect right after finishing your introduction is how do you differentiate or compare Spark and Hadoop. The trick to answering this question is to differentiate on the basis of the feature criteria. You can start with
|Decent speed to work
|Faster than Hadoop
|Both real time and batch processing
|Difficult to learn
|Easy to learn with high modules
|No interactive modes
|Has interactive modes
You can use the above table to present your answer in a systematic manner to leave a long-lasting impression.
Q8. Can you define Spark in your own words?
Ans. As a professional, it can be the easiest question you can come across but as mentioned, earlier systematic presentation of your answer is what actually matters. Therefore, start with the proper definition- APAC Spark is the open-source cluster computing framework that is used for real-time processing. The framework has a large active community and is considered the most successful project of APAC. There is a never-ending demand for Spark solution that has clearly made it the market leader for data processing. Big brands like Amazon, Yahoo, and eBay are some of the known Spark users.
Q9. Do you know what languages are supported by Spark? Which one is the most popular with Spark?
Ans. As a market leader, Spark supports a range of languages that include- Java, Python, Scala, R., and more. Among all the languages that Spark supports, Scala and Python are the most popular languages. On a further note, most of the spark is written in Scala as it is the most used language with Spark.
Q10. What do you understand by the term Yarn?
Ans. Just like Hadoop, Yarn is another feature of Spark that provides a central and resource management platform to ensure scalable operations. The spark can also run on Yarn the same way Hadoop can run on Yarn.
Q11. What is the lazy evolution in spark?
Ans. When you use Spark to operate on any database, it remembers the instructions. When a transformation for an instance- map () is called on an RDD it doesn’t instantly start performing. In spark, you have to provide an action to evaluate transformation, which in return aids to optimize the overall data processing. This feature is known as lazy evolution.
Q12. How do you perform automatic cleanups in Spark?
Ans. It is a basic question; you must answer it with utmost confidence. A one-liner would be great so, explain that automatic cleanup can be performed by setting the parameter spark.cleaner.ttlx.
Q13. Can you connect spark to Apache Mesos?
Ans. The shortest answer to this Apache interview question is “YES,” and once he asks you to elaborate it, you can start with 4 step process that includes-
- Configuring the Spark Driver program to connect with Apache Mesos
- Use the Spark binary package in a location that can be accessed by Mesos
- Install Spark at the same location you put Mesos
- Configure the spark.mesos.executor.home to point out the location where Spark is installed
Q14. What is shuffling in Spark? Do you know the cause behind it?
Ans. In spark, shuffling is the process of redistributing the data across different partitions that further leads to the data movement across executors. However, the shuffle process depends on comparison parameters you use and often occurs when you join two tables while performing bykey operations.
Q15. What are the functions supported by Spark core?
Ans. Spark core works like an engine for distributed processing for large data sets. The range of functionality supported by Spark core includes-
- Memory management
- Fault recovery
- Interacting with storage
- Task scheduling
There you go, hopefully, the above collection of most commonly asked, and conceptual spark interview questions is enough to prepare you for the upcoming job interview. However, if you feel like you need more information, then feel free to consult with professionals at the site.
Q16. What are the features of Apache Spark?
- High Processing Speed
- Dynamic Nature
- In-Memory Computation
- Fault Tolerance
- Stream Processing
- Lazy Evaluation
- Support Multiple Languages
- Hadoop Integration
- Supports Spark GraphX
- Cost Efficiency
- Active Developer’s Community
Q17. How is Apache Spark different from MapReduce?
Spark processes data in batches as well as in real-time
MapReduce processes data in batches only
Spark runs almost 100 times faster than Hadoop MapReduce
Hadoop MapReduce is slower when it comes to large scale data processing
Spark stores data in the RAM i.e. in-memory. So, it is easier to retrieve it
Hadoop MapReduce data is stored in HDFS and hence takes a long time to retrieve the data
Spark provides caching and in-memory data storage
Hadoop is highly disk-dependent
Q18. How can you connect Spark to Apache Mesos?
There are a total of 4 steps that can help you connect Spark to Apache Mesos.
- Configure the Spark Driver program to connect with Apache Mesos
- Put the Spark binary package in a location accessible by Mesos
- Install Spark in the same location as that of the Apache Mesos
- Configure the spark.mesos.executor.home property for pointing to the location where Spark is installed
- What is Apache Spark and what is it used for?
- How does Spark differ from Hadoop MapReduce?
- Can you explain the difference between an RDD and a DataFrame in Spark?
- How does Spark handle data partitioning and shuffling?
- Can you explain the role of the Spark driver and executors in a Spark application?
- How does Spark handle fault tolerance and recovery?
- Can you provide an example of using Spark SQL to query data stored in a Hive table?
- Can you explain how Spark Streaming works and give an example of its use case?
- Can you provide an example of using Spark MLlib to train and evaluate a machine learning model?
- Have you used any Spark integrations, such as with Kubernetes or Apache Flink, and if so, can you describe your experience with them?
These questions are intended to gauge your understanding of the core concepts and capabilities of Apache Spark. You should be able to provide detailed and accurate answers to demonstrate your knowledge of Spark and its various components.
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