How Spark Developer Can Be Compared to Hadoop Developer

The rise of platforms like Hadoop and Apache Spark has moved the state of the art beyond mere programming, to the next frontier. If data scientist salary trends for these specialized skills are anything to go by, we’re fast moving past that frontier and beyond. The age of the data scientist has now arrived, with Forbes reporting that about 53% of very large businesses are adopting big data.

In the future, so the thinking goes, data, and the ability to work with it, will be as good as gold. While the claim seems outrageous, rightly, it hearkens to a nuanced insight that many are missing in the debate on the importance of data science. To help understand the role of big data technologies, we will make a comparison between two important technologies and big data roles:

  • Spark Developers
  • Hadoop Developers

Hadoop and Spark Increase Data Scientist Salary

Along with the high need of big data in the enterprise has come an increase in salaries for data scientists and related roles. Still, the recent developments have met with skeptics who wonder why this new technology is so important, and how exactly Hadoop Developers can be compared to Spark Devs.

Significance of Big Data

If you are unclear of the significance of the new data science now being rolled out by tech leaders, a quick look at the history of the field will help you understand where we are at. Hopefully, you will also gain a real, exciting sense of where things are headed. The frontier, in short, is a new age where data yields insights that have always been contained in siloed digital records, but have, thus far, been hidden from us, due to the lack of our tools.

A Turning Point for Big Data

Before 2012, roughly, give or take a few years, our ability to process information in large quantities and draw intelligence from it, was really shoddy. Not for lack of effort, but because certain breakthroughs had not yet occurred in our technology. 2012 was the year multi-paradigm processing tools like Spark were launched, and Hadoop saw greater adoption.

The rise of these large scale analysis platforms, on one hand, and new database paradigms for large scale processing, on the other hand, changed everything. These new big-data platforms include:

  • Hive
  • Apache Pig
  • HBase
  • Cassandra
  • Kafka
  • MongoDB

Below, we look at Hadoop and Spark. We illustrate how they allow businesses and technicians to draw actionable insights from digital information.

Hadoop, Big Data, and MapReduce

Hadoop is a technology developed in 2005 by a pair of computer scientists, Doug Cutting and Mike Cafarella. Hadoop works with big data, which is defined as large quantities of structured and unstructured records that cannot be processed by traditional information processing tools. This is data in the hundreds and thousands of terabytes which needs to be processed via batch processes or in real time to draw insight.

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Hadoop MapReduce

Hadoop MapReduce logo

Hadoop Developers entered the market first, before there were any Spark Developers. Both, however, do similar types of information processing work. In many ways, Hadoop developers laid the groundwork for future data science developers.

To understand why the ability of processing large quantities of information via Hadoop MapReduce was such a big breakthrough, consider one practical example. A hospital collects information on its patients. Somewhere in those thousands of terabytes of total records are some insights on the hospital’s actions that could enhance the health and survivability of its patients.

However, a traditional database tool and file system do not allow the hospital to sequence all this information and draw smart insights from it. In fact, a traditional database only allows imperative, “dumb” SQL queries that return a record or set of records.

Practical Achievements of Big Data Techniques

What Hadoop and the tools that have come after it achieve, is to run deep, insight-seeking queries and processes that surface correlations as well as inferences from the aggregation of records. A lot of math and statistical tooling is used in this process, to a level not available in traditional databases such as Oracle and SQL Server. A big technological advance has appeared in the form of the new tools.

Therefore, using Hadoop Map Reduce and other information infrastructure tools, we can come up with accurate predictions and explanations about relations in our data, such as which treatments are really improving the recovery of patients in the hospital.

This level of insight is highly useful as it allows to improve existing real-world systems and conceive new ones that use machine-level decision making to achieve better outcomes. This is the explanation behind the unleashing of big data in all its forms on the world today. Given the power of the new technology, it has to be applied in every field imaginable because the gains will vastly improve human life.

Spark Framework

Spark framework developers

Spark is a newer technology than Hadoop. It was developed in 2012 to provide vastly improved real time large scale processing, among other things. Hadoop had a real weakness in its meager real-time data processing capability.

The developers of Spark made the decision to build a system that could operate in real-time and, therefore, be more interactive and iterative. Spark has excellent support for streaming data that has to be processed back on demand.

Hadoop, on the other hand, specialized in batch processing. In the Hadoop MapReduce process, datasets have to first be distributed across its distributed file system. A Hadoop job then has to be initiated via code. The job then executes in the background. Finally, the job returns its results.

Comparison of Spark and Hadoop

With Spark, the developer can pass in data in real-time from an application or API. Spark will then process this stream in memory, without writing it to a file system, and return results immediately.

This makes Apache Spark a much better tool for tasks requiring immediate results. In such machine intelligence decision-making engines, the ability to input unique data and get it processed ad hoc, rather than waiting for a long-running Hadoop batch job, will be invaluable.

In addition, the second big advantage of Spark is that it is blazing fast when it comes to processing large quantities of information. Contrast this with Hadoop, which can take several hours for a single job to run. The difference is telling, and is why many developers interested in big data are now choosing Spark over Hadoop. In 2016, Spark 2.0 was released, giving the platform significant performance improvements, which have been improved further upon since then.

This is not to say that Apache Spark is perfect, or the best large scale information processing tool for every job. Rather, it builds on Hadoop’s earlier progress, and addresses weaknesses in Hadoop’s approach.

Spark vs Hadoop Strengths and Weaknesses

At the same time, Apache Spark does have some weaknesses and problem areas of its own. For example, its use of in-memory processing means that it consumes large amounts of computer memory.

With very large digital information sets, you will need to provision enough quantities of memory for Apache Spark to smoothly process the data. Such large memory requirements can potentially get very expensive. In the absence of this, you will have unresponsive processing. Contrast this to Hadoop, which uses low to moderate amounts of memory, even on cheap, commodity hardware.

Tradeoffs of Spark and Hadoop

The tradeoffs are notable. You must understand, however, that Spark makes the tradeoff consciously, in order to focus on being excellent at real-time information processing. Apache Spark will not write the records to a filesystem for processing because that would slow down the retrieval of results.

Retrieving records from a distributed file system requires delicate data collection and aggregation operations which would throttle the throughput of feeding back results to the user.

Overall, we can thus see that the two platforms focus on contrasting ends of the large scale information processing problem space. Hadoop focuses more on running long batch processes slowly, on a distributed file system, but using low memory. Spark, on the other hand, gives vastly improved real-time streaming of information, while consuming a proportionately larger allocation of memory resources.

spark engineering vs hadoop

Apache Spark Developer Demand

Demand for Spark developers is high in cutting edge businesses that are building out their data processing infrastructure. You will thus find Spark developers in a range of industries and organization types where they collaborate with domain experts to build data-processing software.

A partial list of places where you can find Spark developers deployed includes:

  • Hospitals
  • Banks
  • Mechanical engineering companies
  • Civil engineering
  • Social science organizations
  • Medical research, particularly involving statistical analysis
  • Social media platforms and their user recommendation engines
  • Robotics
  • Smart digital assistants and machine learning deployments

In other words, where cutting edge work of a data-related sort is being carried out in the economy, whether startups or large enterprises, you are likely to find Spark or Hadoop developers involved.

Spark Developer Salary

spark developer salary

Salary for Apache Spark Developer in Selected Countries

Above, we’ve charted salaries for Spark developers based on data from PayScale, Indeed, and AngelList. Salaries for Spark developers are trending very high in the US, while in Europe and Asia, they are following suit.

In China, for example, data engineers now earn more than investment bankers and doctorate graduates in other fields, according to the South China Morning Post. For Apache Spark, companies are in a special bind. Not only are salaries high, but the existing talent is simply not enough to fill the number of openings for data science. As a newer technology, developers have to first train on the technology and achieve proficiency. In the meantime, businesses looking to hire a Spark dev are pressed for talent.

It’s not all doom and gloom, however, because, as we pointed out earlier, Apache Spark developers in Eastern Europe can help alleviate the talent problems for businesses in the West. Countries like Ukraine have the edge when it comes to finding Spark and Hadoop developers. They not only have highly skilled graduates with the math and science background involved in statistical processing algorithms, but they also happen to have lower hiring costs for these professionals. This creates conducive conditions for companies in the West and elsewhere whose operations rely on data and, in particular, big data.

Spark Certification

A number of certification programs have mushroomed across the data engineering landscape. Certification holds tremendous value because it can authenticate a developer’s mastery of standard big data and Apache Spark principles. Organizations and developers with Apache Spark certification can, therefore, charge higher rates for their big data engineering services.

At the same time, certification by itself will not replace the need for hands-on project experience. For a developer to really become adept at big data, they must spend time working on deployable big data projects as part of a team. The reality is that real-world experience will continue to be the biggest indicator of quality Spark skill, but certification definitely helps.

Spark Developer Resume

Here’s a sample resume for an experienced Spark Developer.

spark developer resume

Spark Engineering with Mobilunity

When you reach out to Mobilunity, we are able to match your requirements with our existing teams of Apache Spark and Hadoop talent. Mobilunity dedicated developers have executed crucial big data projects involving multiple different industries. Their expertise, coupled with project-focused management and agile development processes, guarantee an outstanding level of execution. Your big data project will get kicked off after you agree to the staffing levels and determine the application level scope.

The process of assembling your team of Hadoop or Spark developers can be completed in a matter of days, allowing your project to move ahead quickly and on schedule.

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