With businesses generating big data at a rapid pace, analysing the data to leverage meaningful business insights is the need of the hour. There are several big data processing alternatives like Hadoop, Spark, Storm, etc. Spark is the next evolutionary change in big data processing environments as it provides batch as well as streaming capabilities making it a preferred choice of platform for speedy data analysis. With interest in enterprise adoption of Hadoop increasing constantly, Spark has joined the big data bandwagon enjoying a non-linear ramp up in the big data ecosystem. With companies showing interest in Spark adoption, here are 5 reasons as to why developers should ignite interest in learning Spark now.
According to the 2015 Data Science Salary Survey by O’Reilly, there exists a strong correlation between people who use Spark and Scala and the change in their salaries. The survey revealed that people with Apache Spark skills added $11,000 extra to the median salary, while Scala programming language had an impact of $4000 to the bottom line. Apache Spark developers earn highest average salary among other programmers using 10 of the most prominent Hadoop development tools. With real-time big data applications going mainstream and organizations producing data at an unprecedented rate -2016 is the best time for professionals to learn Apache Spark online and help companies do sophisticated data analysis.
Developers who have enjoyed the ride on Hadoop for distributed data analytics are now tapping big data with Apache Spark framework as it provides in-memory computing - rendering performance benefits to users over Hadoop’s cluster storage approach. Spark distributes and caches data in-memory and is a big hit among data scientists as it helps them write fast machine learning algorithms on large data sets. Apache Spark is implemented in Scala programming language that provides an exceptional platform for data processing. Apache Spark in one of the fastest growing big data community with more than 750 contributors from 200+ companies worldwide.
Apache Spark is opening up various opportunities for big data exploration and making it easier for organizations to solve different kinds of big data problems. Spark is the hottest technology now, not just among the data engineers but even majority of data scientists prefer to work with Spark. Apache Spark is a fascinating platform for data scientists with use cases spanning across investigative and operational analytics.
Data scientists are exhibiting interest in working with Spark because of its ability to store data resident in memory that helps speed up machine learning workloads unlike Hadoop MapReduce. Apache Spark has witnessed continuous upward trajectory in the big data ecosystem. With IBM’s recent announcement that it will educate more than 1 million data engineers and data scientists on Apache Spark – 2016 is definitely THE year to learn Spark and pursue a lucrative career.
After the inception of Hadoop, several organizations invested in novel computing clusters to make use of the technology. However, Apache Spark does not pose any limitations on investing in new computing clusters as organizations can use Spark on top of the existing Hadoop clusters.
Spark can run on Hadoop MapReduce as it can run on YARN and on HDFS. With high compatibility of Spark with Hadoop, companies are on the verge of hiring increased number of Spark developers as they do not have to re-invest on computing clusters because it can be integrated well with Hadoop. This also makes learning spark an added advantage for professionals with expertise in Hadoop skills.
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Spark will reinvigorate Hadoop, and in 2016, nine out of every 10 projects on Hadoop will be Spark-related projects. — said Monte Zweben, CEO of Splice Machine
With companies embracing the adoption of various adjacent big data technologies that complement Hadoop-Spark adoption rate is increasing exponentially. Spark is no more just a component of the big data Hadoop ecosystem but has become the go-to big data technology for enterprises across various verticals.
“Spark provides dramatically increased data processing speed compared to Hadoop and is now the largest big data open-source project.” said Apache Spark originator Matei Zaharia.
A recent survey on Spark adoption revealed that Spark community has had most of the contributions compared to other open source projects managed by Apache foundation. There is an increasing demand to support BI workloads using a combination of the two big data tools - Hadoop and Spark SQL.
The survey findings show that among Apache Spark adopters 68% of the companies are using Spark to render support for BI workloads. Spark’s clear value proposition is leading to an increased adoption rate by enterprises opening up lucrative opportunities for big data developers with Spark and Hadoop skills.
Big data predictions for 2016 expect Apache Spark to go its own way, creating a novel, vibrant ecosystem with popular cloud vendors releasing their individual Spark PaaS offerings.
Spark’s enterprise adoption is rising because of its potential to eclipse Hadoop as it is the best alternative to MapReduce - within the Hadoop framework or outside it. Similar to Hadoop, Apache Spark also requires technical expertise in object oriented programming concepts to program and run- thus opening up job opportunities for those who have hands-on working experience in Spark. Industry-wide Spark skills shortage is leading to a number open jobs and contracting opportunities for big data professionals.
For people who want to make a career on the forefront of big data technology, learning apache spark now will open up a lot of opportunities. There are several ways to bridge the skills gap for getting a data related jobs and finding a position as a Spark developer. The best way is to take a formal training that provides hands-on working experience and helps learning through hands on projects.
According to the popular IT job portal, Dice.com, a keyword search for the term “Spark Developer” showed 34617 listings as of 16th December, 2015.
Spark developers are so in-demand that companies are agreeing to bend the recruitment rules, offer attractive benefits and provide flexible work timings just to hire experts skilled in Apache Spark. According to indeed.com, the average salary for a Spark Developer in San Francisco is $128, 000 as of December 16, 2015. Indeed.com statistics reveal that the average salary for spark developers in San Francisco is 35% more than the average salaries for Spark developers in US.
According to O’Reilly, data engineers who have experience with Apache Spark and Storm earn the highest average salaries. Several recent salary surveys found that data engineers and data analysts having big data skills like Hadoop are earning close to $120,000/year, when compared to the average IT tech salary of $89,450. Apache Spark and Storm skilled professionals are pulling close to $150,000 in yearly salaries, when compared to the total average salary of data engineers which is $98,000. People with a keen desire to grow their big data career and earn high salaries - must learn Apache Spark online now.
As Spark continues to be used for interactive scale out data processing requirements and batch oriented needs, it is expected to play a vital role in the next generation scale out BI applications. Professionals need to undertake comprehensive hands-on training in Spark to become productive especially if they are newbies to Scala programming. It requires professionals to get comfortable with a new programming paradigm like Scala. However, one can also use Shark i.e. SQL on Shark to get started with learning Apache Spark.
Developers can also write code in Java, Python, R (SparkR) to craft an analytics workflow in Spark. ProjectPro provides Apache Spark certification on successful completion of hands-on Apache Spark training.
With the need for Spark developers growing in the industry, learning apache spark from trained industry experts can help professionals get good hands-on experience that is at par with the industry standards. Organizations are looking to hire Hadoop developers who have demonstrated expertise in implementing best practices for Apache Spark. On completion of ProjectPro’s Apache Spark training, students learn how to build increasingly sophisticated and complex solutions for organizations on top of Spark deployments. ProjectPro offers best-in class Apache Spark Certification for validating developer’s expertise in using Apache Spark for big data applications.
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