Now that the businesses are generating big data so rapidly, the need of the hour is analyzing this data for leveraging meaningful business insights. A few Big Data processing alternatives are Spark, Hadoop, Storm, etc. The next evolutionary change in the Big Data processing environments is Spark. This is because of the batch and streaming capabilities that make it the perfect platform for speedy data analysis. Apache Spark is one of the best tools in the market for handling and processing Big Data. As the industry is marching towards better applications, the demand for Spark developers has increased. Now is the perfect time to get started with Apache Spark, thanks to the rising demand for Apache Spark Developers in India. Here is why:
1. Increased Access to Big Data
Thanks to Apache Spark, we now have new opportunities to explore big data. This had made it easier for companies to solve several types of big data problems. Currently, Spark is one of the hottest technologies, not just among Data Engineers, but also Data Scientists. With use cases spanning across operational and investigative analytics, Apache Spark has become a fascinated platform. More and more data scientists are interested in working with Spark; thanks to its capability of storing data resident in memory. This speeds up the machine learning workloads unlike in the case of Hadoop MapReduce.
In the Big Data ecosystem, Apache Spark has made a continuous upward trajectory. And it is going to be the same for a very long time. Learning Spark is going to make way for a lucrative career in Big Data.
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2. Making use of existing investments in Big Data
At the time of Hadoop’s inception, several companies invested in computing clusters to make the most out of the technology. However, for Apache Spark, companies won’t have to invest in new computing clusters. Spark can be used on the existing Hadoop clusters. Hadoop MapReduce can be used for running Spark as it can run on HDFS and YARN.
Thanks to the high compatibility of Hadoop with Spark, companies are looking for Spark developers as it can be integrated with Hadoop and companies don’t have to invest more money in computing clusters. So, learning Spark is an added advantage for candidates who already have Hadoop skills.
3. Pacing up with the growing adoption
Spark is not just a component of the Hadoop ecosystem but the go-to Big Data technology used by organizations across various verticals. Spark has been known to increase data processing speed dramatically as compared to Hadoop. This is why Spark is currently the largest, open-source big data project.
4. Demand will increase in the future
Spark has the potential for eclipsing Hadoop as it is the perfect alternative to MapReduce – either within the Hadoop framework or outside it. In order to be an Apache Spark developer, you need to be an expert in Object-Oriented Programming concepts.
Currently, there is an increasing number of job opportunities and not enough skilled Spark professionals. For anyone who wants to be at the forefront of Big Data technology, Apache Spark will open doors to a lot of opportunities. The best way to take advantage of these opportunities is through formal training where you will be learning the concepts and getting hands-on experience through projects. You can pursue Spark Certification training to get the knowledge and skills that are required to become a successful Spark Developer. Certification will not only help you enhance your knowledge but will also help you stand out from your competition.
5. Make big money
The demand for Spark developers is so high that companies are willing to bend the rules of recruitment and offer an attractive salary and flexible work timings for hiring skilled Apache Spark professionals. According to ZipRecruiter, the average annual salary of a Spark Developer in the US is $121,388.
Spark can be used for scaling out batch-oriented needs and data processing requirements. This is why it plays an important role in scale-out, next-generation BI applications. What you need is to get comprehensive training in Spark, especially if you are new to Scala programming. It will take you some time to get comfortable with Scala, a new programming paradigm. However, you can also use Shark (SQL on Shark) for getting started with Apache Spark. You will also need to have an understanding of Python, R, and Java for crafting an analytics workflow in Spark.
Today, every company is crunching numbers and trying to learn what the data can teach them. The trends and patterns hidden in the data can help the companies make their marketing strategies and improve the quality of their services and products. But, before they can make any big decisions, they need to capture data, store it, cache it, and analyze it. This is where Big Data comes into play. If you want to make a career in the field of Big Data and thrive in it, Apache Spark is the way for you. It will open up different opportunities to explore Big Data.
The different methodologies of Apache Spark will be effective for different data problems which makes it the hottest Big Data technology. Most importantly, since Spark runs on HDFS and on YARN and can run on Hadoop MapReduce, it outshines Hadoop. This is one of the primary reasons why companies are looking for Spark Developers. Some are also adopting Spark as the adjacent Big Data technology as it increases the speed for data processing as compared to Hadoop. As the technology advances and new companies switch over to Big data for handling their requirements, new opportunities will open up for Big Data developers.
If you want to start your career as an Apache Spark developer, the best way for you is through Spark Certification training. You will be learning from trained industry experts with hands-on experience. Also, organizations are looking for developers who have implemented Spark’s best practices. Once you complete the training, you will know how to create increasingly complex and sophisticated solutions for companies.
