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Apache Spark Developer Salary in India: For Freshers & Experienced [2023]

Every company today wants to crunch numbers and learn what its data is hiding. The patterns and trends that data show can have a huge impact on the marketing strategies and quality of products and services that the company offers to its customer base. But, before they start analyzing trends, data is captured, stored, and cached.

Where is the data cached and stored? How is it stored? How can companies ensure that their marketers, researchers, and other team members have access to relevant data as and when required? All this and more is possible with the help of Apache Spark

Spark

The following article will help you understand what is Spark, as well as the average Spark salary across various states of India. It will also shed light on a Spark developer job description and the required skill set that every Spark developer is expected to have. 

What is Apache Spark?

Technically, it is an open-source, distributed, cluster-computing framework that is written in Java. Apache Spark is a data-processing engine that is used for caching data of several petabytes. The cached data is stored across multiple operations to facilitate interactive querying; quickly and securely.  The data processing framework performs tasks and distributes data across multiple computers.

Spark can work on its own or in collaboration with other distributed computing tools to determine the nodes and sequence of the execution of tasks. Spark databases are used by every major industry in the world, including tech industries, BFSI sector, e-commerce, iGaming and sports companies, education institutes, telecommunication companies, and even governments. Most Data Scientists and Data Engineers work with Spark.

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Features Of Apache Spark

Now that you have a basic understanding of what is Spark, let’s take a look at some of the top features of this technology that make it so popular in the business world. 

  • Fast Processing- When it comes to Big Data, speed is one of the most important components. Big Data, which is characterised by its volume and variety, needs to be processed at a higher speed. This is where Apache spark comes into play. With the help of this technology, you can now process Big Data almost ten to one times faster than other technologies such as Hadoop. Spark’s ability can be attributed to the Resilient Distributed Dataset, which helps reduce the time consumption of basic tasks such as reading and writing operations. 
  • Flexibility- Yet another feature of Apache spark that makes it highly desirable is that it can support multiple languages. This means that developers can now write applications in different languages, such as Java, Python, or Scala, without the need to confirm to only one specific language. 
  • Real-time Processing- Contrary to most other technologies that only use stored data, Apache spark allows real-time data processing. This means that you can now get instant outcomes with the help of Apache Spark. 
  • Enhanced Analytics- Last but not least, Apache Spark is equipped with various features, such as complex analytics and machine learning algorithms, that can enhance the performance of analytics much faster and more efficiently. 

All these features undoubtedly make Apache Spark one of the most sought-after technologies. With that said, let’s take a look at some of the use cases of this technology.

Use Cases Of Apache Spark

Be it the financial sector or the healthcare industry, the use cases of Apache Spark have grown manifold over the years. More and more industries have switched to this technology to perform advanced analytics. One such example might include Yahoo, which relies heavily on Apache Spark to curate personalized content for its users. Simultaneously, other enterprises such as Nickelodeon and MTV have also been able to successfully harness the power of Apache Spark to enhance their customer experience. Before moving on to the Spark developer job description, here are some of the top use cases of Apache Spark.

  • Financial Sector- In the financial sector, Apache Spark is mainly used to put forward new financial products to their target consumers. Furthermore, this technology also allows business owners to understand customer behaviour, by analysing their movements on various social media platforms. This, in turn, helps them to identify the key issues of the said users in a much more efficient and faster manner.
  • Healthcare Industry- Apache Spark especially bores quite huge responsibilities in the healthcare industry, as with the help of this technology, professionals try to put together the past clinical data of a patient, along with their current one, to get a more comprehensive picture. 
  • Manufacturing and Retail- Last but not least, Apache Spark is also heavily used in both manufacturing and retail industries to provide personalised services to their customers.

With that said, let’s take a closer look at the average Spark salary all over India, as well as the Spark developer job description mentioned below. 

Who is a Spark Developer?

A Spark Developer is a software developer with Apache Spark skills. While Android Developers help to write code for a new app, Spark Developers create code to ensure that Big Data is available – it is all about ensuring the relevant data is available in the shortest time possible when a query is raised.

The Apache Spark Developers have a deep understanding of Java and Scala language. Technical expertise for concepts related to object-oriented programming helps the Developers to optimize performance and distribution.   

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Spark Developer Salary in India

Undoubtedly, Spark is one of the most sought skills for companies handling Big Data. But, the question that is often asked is – What is the salary that I can hope to get? The specializations in the field clearly indicate a higher salary for Spark Developer in comparison to Android Developers. 

Read: Apache Spark Streaming Tutorial For Beginners

The Demand for Apache Developers

A simple LinkedIn search can show you more than 3000 results, which is quite a lot, to say the least.

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Companies around the world are adopting Spark as their primary big data processing framework as it offers a lot of flexibility for developers to work in their preferred language. Some popular companies like Amazon, Yahoo, Alibaba, eBay, etc have invested in talent in Spark. Today, there are opportunities around the world as well as in India, which has led to an increase in job opportunities for the right talent. 

If you go to Naukri.com, you will find more than 60000 results for Spark developer and related roles which are a lot of options for you to choose from.

In-Demand Software Development Skills

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As mentioned above, you will have the opportunity to be working in different kinds of industries like Retail, Software, Media and Entertainment, Consulting, Healthcare, and more. It is really enthralling to know that every industry nowadays is adopting big data analytics and machine learning methodologies to give themselves a boost in this technologically advancing world.

There are also different kinds of roles you can perform in the big data field as a Spark specialist like Big Data – Lead Software Engineer, Big Data Developer, Principal Software Engineer, Data Scientist/Engineer or Management Analyst. It all depends on your skillsets and experience and your work ethics, and you should be willing to learn and grow to keep pace with the new technology.

The Median Salary for an Apache Spark Developer in India

Spark developers are so in demand that companies are willing to roll out the red carpet for them. Apart from offering a fantastic salary, and some even offer them flexible working hours. According to PayScale, the average salary for a Spark developer in India is more than Rs 7,20,000 per annum. The annual salary is inclusive of the bonus the company offers. 

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Factors Affecting Apache Developer Salary in India

The three main factors affecting the Apache Developer Salary in India are:

  • Company –  The bigger the brand, the higher is the salary that you can expect. An app’s functionality can make or break the reputation of a company, and brands are paying higher salaries to deserving candidates. 
  • Experience – One of the important factors to affect your salary is experience. It clearly shows that the candidate has the knowledge to work under pressure and handle bugs and issues easily and quickly. 
  • Location – Though many software developers are now offered flexible working options, the location continues to play an important factor in the final salary. 

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Apache Spark Developer Salary: Based On Company

Your salary will also depend upon the firm that you are working in. Companies like Cognizant, Accenture, and Infosys tend to pay in the good range, but you can earn quite a lot working in giants like Amazon, Microsoft, or Yahoo. But getting into these companies can be quite tough and may require some extra effort and skillsets for you to qualify. But the paycheck at the end of it all will be totally worth it!

Apache Spark is a trending skill right now, and companies are willing to pay more to acquire good spark developers to handle their big data.

Apache Spark Developer Salary: Based On Experience

Your salary will also depend on your skillset and experience. 

Experience plays a major role in any field. While you are starting, you may not have much practical knowledge, but as you work on different projects with different people, you will learn new techniques and methods to solve a certain problem at hand. This plays a vital role as you grow in your field. Apache Spark may get some major new features in the next updates that completely changes its working and handling process.

So, you should be able to quickly learn and adapt to those changes to stay and grow in your position. Generally, an entry-level Spark developer, you could be earning between Rs 6,00,000 to Rs 10,00,000 per annum while an experienced developer, the salary ranges between Rs 25,00,000 to Rs 40,00,000.

Let us compare salaries for Apache vs Hadoop

For Big Data handling, Hadoop has been the go-to technology. But it is Apache Spark that is becoming popular for its flexibility and scalability.

So how would you compare to a Hadoop developer if you choose Apache Spark?

According to Glassdoor, the average salary for a Hadoop developer in India is around Rs 4,91,000 per annum, which is considerably less than that of an Apache Spark developer mentioned above.

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And as more companies switch to Apache Spark, the numbers for Apache Spark will keep on increasing.

Apache Spark Developer Salary: Based On Location

Your earnings may also depend on the location that you are working in. 

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For example, if you are working in Bangalore as a Data Engineer with Apache Spark skills, you could be earning more than Rs 10,00,000 per annum on an average.

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While if you are a Data Scientist with Apache Spark skills in Hyderabad, you could earn more than Rs 8,00,000 per annum on an average.

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Major Roles and Responsibilities of an Apache Spark Developer?

An Apache Spark developer’s responsibilities include creating Spark/Scala jobs for data aggregation and transformation, producing unit tests for Spark helper and transformations methods, using all code writing Scaladoc-style documentation, and design data processing pipelines.

Apart from this, developers are also expected to run data on distributed SQL, creating data pipelines, ingesting data into a database, running effective Machine Learning algorithms on a given dataset with appropriate scalability, work with graphs or data streams and much more.

Your roles as a developer may also depend on the position and institution you are working in. It may vary from project to project based on the requirements, and you should always be well versed with all the technicalities to tackle every situation.

Key Responsibilities of an Apache Spark Developer

  • Ability to define problems, collect data, establish facts, and draw valid conclusions with software code.
  • Clean, transform and analyze raw data from various mediation sources using Spark to provide ready to use data
  • Use of refactoring code, so that joins are done efficiently
  • Provide technical Spark platform architecture guidance.
  • Implement partitioning schemes to support defined use cases.
  • Lead deep-dive working sessions for the rapid resolution of Spark platform issues.

Skills of an Apache Spark Developer

To become an expert level Spark developer and thrive in the industry; you need to have a proper aim and follow the right path to acquire the skills that will take you ahead. 

  • If you are a beginner in Big Data analytics, you should start with some online training courses and certification. You should understand the concept behind big data and how to maneuver them to get to the result.
  • Experience is the best form of learning, so once you are done with the course, take up some projects on your own. Explore things by taking up different challenges. Understand Dataframes and RDDs – the major building blocks of Spark.
  • Spark can be used with many high-level programming languages like Python, Java, R, and Scala. Make sure you are proficient in at least one of them.
  • Knowledge and expertise in Spark features like SparksSQL, SparkML-Lib, Spark GraphX, SparkR, Spark Streaming will give you the confidence to do your job.
  • You can now go ahead and take the CCA-175 Hadoop and Spark Certification Examination to get yourself certified.

Why become an Apache Spark Developer?

  • If you want to get your hands in the big data field and are looking to thrive in it, Apache Spark is the best way to do it as it is opening up various opportunities for big data exploration. Its different methodologies are effective against different data problems and make it the hottest Big Data technology.
  • Spark outshines Hadoop as it can run on Hadoop MapReduce as it can run on YARN and on HDFS. Due to its high compatibility with Hadoop, companies are seeking a high number of Spark Developers.
  • Many companies are adopting Spark as an adjacent big data technology as it provides a great increase in speed for data processing compared to Hadoop.
  • Numerous new opportunities as technology advances and new companies switch to big data handling to meet their requirements.

Also Read: Data Scientist Salary in India

Conclusion

Apache Spark is a great tool to handle and process Big Data. It is a much-valued skill, and as the software industry marches towards better apps, the demand for these developers will keep growing. You should join the right course to acquire skill sets that will enable you to get the best Apache Spark Developer Salary in India.

If you are interested to know more about Big Data, check out our Advanced Certificate Programme in Big Data from IIIT Bangalore.

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What is the difference between Apache Hadoop and Apache Spark?

Apache Hadoop is a general-purpose distributed system which handles large data. It has several components such as HDFS, MapReduce, and YARN. On the other hand, Apache Spark is an open-source tool which uses RAM for processing data. The two have considerable differences. Hadoop is an open-source platform, and hence it is less expensive compared to Apache Spark, which relies on RAM and, hence, increases costs. Hadoop is suitable for batch processing, while Spark is good for interactive analysis. Hadoop can enable data processing in batch mode only, while Spark has the advantage of processing real-time data.

What do you mean by Veracity in Hadoop?

As the name suggests, veracity is related to the accuracy and trustworthiness of the data. Data veracity refers to the various issues with data such as biasedness, the reliability of data sources, noise in the data, accuracy of data, etc. It is one of the most essential characteristics of the Hadoop system. It also involves processing, analysing, and interpreting the data in the desired way to yield meaningful results. However, it may sometimes get difficult to track the data source and quality when multiple datasets are combined. Hence, veracity is one area which still needs improvement.

What languages are supported by Apache Spark?

Apache Spark is a fast, open-source operating system. It supports 4 languages: Scala, Java, Python, and R. All of these languages have several pros and cons based on which developers choose their preferred language. Writing code in Scala and Python is comparatively easier than writing in Java. Scala processes and analyses data 10 times faster than that of Python and is more maintainable than Python, a statistically-typed language. Due to these reasons, Scala is the most popularly used programming language for Spark. On the other hand, Python is preferred over Scala in machine learning because it has advanced tools for Data Science and Machine Learning. You should choose a language based on your experience and comfort.

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