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 strong need for 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.
The 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:
- Apache Pig
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.
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 to process 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 achieves, 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 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 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.
Spark VS Hadoop Comparison
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 vs 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.
Hadoop vs Spark Programming Takeaways
Here is some key points about the difference between Spark and 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:
- Mechanical engineering companies
- Civil engineering
- Social science organizations
- Medical research, particularly involving statistical analysis
- Social media platforms and their user recommendation engines
- 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.
As for February 2019, Indeed.com has more than 18,000 jobs listed for positions to be filled in the USA, whereas LinkedIn.com lists more than 1K of job offers listed within or near European Union. In terms of salary, Spark developer will judged based on their skills, experience, and expertise within the requested domain.
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 Spark developers 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 vs Hadoop Cost Tendencies
Typically, two technologies that are available within the same business domain are paying the same amount of money. Let’s see whether this rule is applicable in Hadoop vs Spark comparison.
Spark Developer Salary
Salary for Apache Spark Developer in Selected Countries
In the USA, a company can hire spark programmer for an average salary of $77,192. The US and Switzerland offer much higher compensations, meaning the Spark cost in those countries is 25 to 45% higher compared to the expenses a company has to account for when they need to hire spark developer in Canada, Germany or Sweden. Companies investments in terms of Spark team can vary, as a team can be composed of developers with various knowledge and experiences. Generally, Spark development requires a typical team composition with some assistance from data engineers.
Can You Hire Spark Developer on Freelance?
Freelance Spark programming is not much cheaper than average salaries in many countries. We’ve checked one of the platforms for developers that fall under the following criteria:
- Have 90+% success rate;
- Possess at least conversational or fluent English;
- Have at least 100+ hours billed, meaning they have some type of experience approved by clients.
Typically, such developers are asking for hourly compensation rate between $35 and $99. Let’s take an average $50 per hour and compare it to a regular spark developer salary.
$50 (hourly rate) ✖️35 (hours a week) ✖️52 (weeks a year) ＝ $91,000
Well, considering the fact that an average spark developer salary in the USA reaches $77,000, this doesn’t make any sense. Spark developer for hire (those, who are working on freelance platforms) charges even more than their colleagues who work as in-house engineers. So, here is a question to CEOs and CTOs: does it make any sense to hire spark developer out there, if they are not legally bound to anything and charge even more than some in-house experts?
How Much Does It Cost to Hire Hadoop Developer?
If you are looking to hire Hadoop engineer, you should consider the cost of development. Following a similar approach as with Spark engineers, we have compiled a comparison for the selected countries.
As you can see, one can hire Hadoop programmer for an average compensation of $78,985 per year in the USA. UAE offer similar compensation, whereas the UK and Switzerland offer much higher rates of $92,756 and $136,479 respectively. One can hire Hadoop engineer for smaller compensations in Canada, Germany, and Belgium. The lowest rates to hire Hadoop developer are in Sweden.
Can You Hire Hadoop Developer via Freelance Platforms?
Among the reasons the force companies to hire Hadoop programmer as a freelancer, is a common belief that it is cost-efficient. Following the same scheme as with Spark developer, we have seen the same picture. A typical freelance developer with Hadoop in their arsenal charge between $40 and $95 per hour of their work, with a median rate of $50-$65. It means that annual expenses will reach $90,000-$118,00 for the 35-hour work week.
Comparing Spark vs Hadoop Cost
As you can see from the chart below, most countries offer similar compensation rates to both Spark and Hadoop developers.
In the USA, the difference in rates is less than 3%, Canada has almost 11% difference in compensation and Belgium has less than 1% difference in Spark vs Hadoop salaries. However, Germany pays 21% less to Hadoop developers, compared to the Spark engineers. However, Switzerland offers 22% more money to Hadoop software developers than to Spark specialists. In the UK, you can hire Spark engineer for relatively the same amount of money as you would have to hire Hadoop engineer.
Spark Developer Resume
While browsing through numerous Spark developer resumes, one may wonder what skills and expertise should a great developer have? Here is what you should look for in Spark developer resume:
- Knowledge of Java. Hands-on experience with Python, Scala, and R would be a huge plus.
- Practical experience with Spark programming.
- Experience with various Apache projects (i.e. Kafka and Zookeeper).
- Experience with Data Mining (including tools like Apache Mahout, KNIME and Rapid Miner).
- Quantitative Analysis and knowledge of tools like SAS and SPSS can be extremely helpful.
- Knowledge of SQL and NoSQL, experience with various databases.
In many cases, experience with Spark web service can be a benefit.
Here’s a sample resume for an experienced Spark Developer.
And while a Spark developer resume is just a business card, only an interview will help one understand whether a particular Apache Spark consultant has enough theoretical knowledge and hands-on practice to qualify for the projects.
Hadoop Developer Resume
Here is a sample resume with the list of skills to look in Hadoop developer.
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.
Choosing a Perfect Spark Provider
If the company understand their need for spark programming, they are most likely going to choose to outsource. And here is the question: how do you choose a decent Spark agency to work with?
First of all, Apache Spark does not have any official partners, so anyone claiming to be Spark IT company shall not be trusted.
Spark as a service falls under large-scale data processing (a.k.a big data), so a good partner will have experience in the field. Also, Spark offshore agency should have experience of working with companies worldwide, as it will be an indicator of the ability to work in a cross-cultural environment well.
Spark Development 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.