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Hiring Apache Spark developers: Interview questions, skills & more
You probably already know that Apache Spark is a popular open-source framework for large-scale data processing. But what you might not realize is that it's one of the most important technologies in the world of big data and analytics, with a wide range of applications from machine learning to natural language processing.
Because of this, there's a growing demand for experienced Apache Spark developers. But what makes an experienced developer? And how can you find one?
Let's take a look at some key things to keep in mind when hiring a developer who specializes in this powerful tool.
First things first: what is Apache Spark?
Apache Spark is an open-source distributed computing framework that is used in a variety of different ways. It comes with a set of libraries and tools, but it also offers a programming interface that makes it easy to use in your own applications. This makes Spark extremely useful and popular in the world of big data analytics and artificial intelligence.
Unfortunately, this popularity has led to a growing shortage of Apache Spark developers. With more companies looking for Spark developers than there are people who know how to do the job well, it's important for businesses to understand how they can get the most out of their hiring process when it comes to finding the right person for their needs.
Yusuf Yigit, a Senior Developer with experience in Apache Spark reckons that Spark is vital when working with large amounts of data.
“Apache Spark is a mandatory tool when it comes to processing big data and reporting. Spark can process lot amount of big data in a distributed way working with multiple slave machines.”Yusuf Yigit
Here's what we've learned about hiring Apache Spark developers so far:
- They have strong knowledge of big data technologies like Hadoop and NoSQL databases.
- They know how to write code that runs on multiple machines at once, so they can process large amounts of data in parallel (that's what makes it fast).
- They're familiar with distributed systems and how they work together as a whole—for example, they may know how to use Kafka or Akka Streams as part of their solution.
Regarding the popularity of Apache Spark, as per Stack Overflow's 2023 survey, 4.09% of professional developers use it, and these respondents are out of 67 231 total respondents.
Companies who use Apache Spark
Spark is used by companies like Netflix and Alibaba to process enormous amounts of information every day, so if you're looking for someone who knows how to work with large datasets, this could be the right place to look. Other companies who use Spark
What makes Apache Spark stand out?
- Spark is a good fit for companies looking to scale their data-intensive workloads and improve their ability to deliver real-time, data-driven insights that can be used to make critical business decisions.
- Spark's architecture makes it an excellent tool for processing large amounts of data in real time and is widely used by companies like Amazon, Alibaba, Netflix and Uber.
- Apache Spark was created by the AMPLab at UC Berkeley as a successor to MapReduce, which was designed to operate on a single machine.
- Spark's open source platform offers a wide range of capabilities including SQL queries, streaming analytics, machine learning and graph processing that can be used in any programming language or platform (Java, Scala, Python or R).
“Spark is the exceptional way of map-reduce without taking care of the heavy computation load that brings big data.”Yusuf Yigit
What kind of skills do I need in my developer?
The most important thing when hiring an Apache Spark developer is their knowledge of the language itself. You want someone who has a few years of experience using Scala or Java (or both), as well as Python, R or SQL (or all four). These languages are used extensively in the field of data science – so if someone has experience with them already, they'll likely have no problem learning about Apache Spark as well!
Abdulhakeem Omotolani Yaqoob, a Data Engineer with experience in Spark, says that one of the leading skills in the Big Data Family.
“Apache Spark is a very great skill to have in today's market. With the continous growth of data, businesses are struggling with managing and computing this large volume of data. One of such solutions is the use of Apache Spark.”Abdulhakeem Omotolani Yaqoob
Things to consider before hiring
When it comes to software engineers, Spark developers can be hired for both remote and on-site positions, and there are many ways to find them. You'll likely need a recruiter or staffing agency to help you out with this part. But, once you have their help, it's best to have a standard set of questions ready so that you can send them over in an email.
You'll be able to ask questions about their professional background and history with Spark, of course, but also about their personal lives. In addition to getting a sense for the candidate's culture fit and personality, this type of information can help you determine whether they'll be happy in your specific work environment.
Know which kind of developer you want
Developers have different levels of expertise and experience; therefore, it is important that you understand what level of expertise your company needs before hiring a developer. You should also consider what technologies the developer should be good at using in order to get the best results for your project.
Understand their skills
It’s important that you understand what job skills the developer has so that they can complete your project successfully without any problems or delays. It will also help you avoid problems after hiring them if they don’t have any experience with the technology or skill set needed for your project. Because then it would be impossible for them to complete the tasks on hand.
Test their technical skills
When hiring an Apache Spark developer, it's important to make sure they have specific skills for the job. This includes having a solid understanding of data science concepts and the ability to implement them using Apache Spark. Technical skills can be hard to evaluate during an interview because they're often not taught in school or covered by many certifications. It's best to ask potential hires questions like "What do you think of big data?" or "What tools do you use when working with big data?" These questions will give you a better idea of what kind of background an applicant has when it comes to working with large amounts of data.
Check if they’re a good cultural fit
It's also important that you make sure your new hire fits into your company culture before hiring them on permanently.
Hiring Apache Spark developers is a difficult task. There are many factors you need to consider before hiring one. If you fail to consider them, then it can lead to an expensive mistake. That’s why we are here to help you with this process.
Know their costs
The cost of hiring an Apache Spark developer depends on several factors such as location (where they live), experience level (how long they have been doing this professionally), etc.
Interview questions and answers
At this point in the hiring process, the hiring manager should have already discussed with the candidate their background and experience with Apache Spark. Now it's time to dig a little deeper and make sure they're a good fit for the role. Here are some questions to ask that will help you better understand how familiar they are with the basics of Spark, how they approach problem-solving, and how they'd handle specific situations as a member of your team.
1. What is RDD?
Expected answer: The Resilient Distributed Dataset (RDD) is at the core of Apache Spark's functionality. An RDD is essentially a collection of data across one or more nodes in a cluster stored in memory.
2. How is Apache Spark different from MapReduce?
Expected answer: Apache Spark is different from MapReduce because it uses a distributed, in-memory data processing engine, while MapReduce is disk-dependent.
3. List the types of Deploy Modes in Spark.
Expected answer: There are two types of Deploy Modes in Spark. Client Mode - In this mode, data can be processed by any client that is connected to the cluster.
Cluster Mode-In this mode, all processing is done on one machine. This makes it easy to debug and troubleshoot issues with your code, but you may need to run multiple instances of Apache Spark in order to handle large amounts of data.
4. What do you understand by Shuffling in Spark?
Expected answer: Shuffling means that data is moved from one partition to another during the execution of a Spark job until it reaches its final destination partition.
5. What are the important components of the Spark ecosystem?
Expected answer: Spark has two main components: the Spark Runtime, and Spark SQL.
The Spark Runtime is a Java library that contains all the basic building blocks for writing applications that use Spark. It provides distributed data storage, distributed execution, fault tolerance, and advanced analytics capabilities.
The Spark SQL module allows users to write structured data processing queries using the Hive Query Language (HQL) and SQL syntaxes.
6. What is a lazy evaluation in Spark?
Expected answer: Lazy evaluation works by only evaluating an RDD operation when its elements are needed. In contrast to eager evaluation, which evaluates an RDD operation immediately as soon as it's invoked, lazy evaluation delays execution until it's absolutely necessary (such as during a reduce operation).
7. How can you trigger automatic clean-ups in Spark to handle accumulated metadata?
Expected answer: Spark automatically cleans up old metadata by setting spark.cleaner.ttl (in seconds) to a value greater than 0 (the default is 1 hour). This ensures that any older metadata will be removed from memory after this time period has elapsed - however it does not clean up old files or directories on disk! You can manually trigger cleaning up of old files by calling 'spark.
8. What is a Parquet file and what are its advantages?
Expected answer: A Parquet file is a compressed columnar format for storing data in HDFS or HBase that has been optimized for fast query execution and efficient compression.
9. What is the role of accumulators in Spark?
Expected answer: Accumulators are variables that can be used for aggregating data. They can be used for calculations across multiple records, as well as for counting events or performing other mathematical operations on them. When used in combination with Kafka Connect, they allow you to keep track of all changes that have been made to a particular message.
10. Describe how model creation works with MLlib and how the model is applied.
Expected answer: When new data comes into Spark Streaming, it needs to be processed by an algorithm before it can be sent out to users. The algorithm used in this case is Machine Learning (ML). Once this is done, Spark applies the algorithm by creating a lineage graph which shows its progress through each stage of processing and finally sending out data once the whole process has been completed successfully without any errors occurring along the way.
11. What is a Lineage Graph?
Expected answer: Lineage graph helps you to find out how much time is spent within each component of your Spark application and what’s causing the overall performance issues in your system. You can also find out if any particular component is taking too much time than others and optimize that component accordingly if needed.
12. Explain Caching in Spark Streaming.
Expected answer: Caching is one of the most powerful features of Spark Streaming because it allows you to replay data after recovering from failures without having to reprocess all of the events since they were last processed (which would take hours).
So, if you are going to interview an Apache Spark developer for your company and need a quick-fire round of questions to ask, look no further. But make sure you have a big question bucket ready! Spark as a technology is full of tricky and edge cases. Any expert Apache Spark developer worth their salt should be able to answer all these questions with ease.
Apache Spark is a powerful tool to handle large-scale data processing in real time. So, getting in front of this technology and developers using it is crucial to ensuring your team remains relevant and competitive.