Apache Spark is a highly potent and yet malleable software built to do the majority of the heavy lifting in any big data project pipeline. Spark is so powerful that it can handle any data irrespective of its types or its size. That is the reason why more companies are adopting Apache Spark into their workflow. There are many ways one can master Apache Spark, but the most successful and widely adopted path is through Spark Certification.
However, one noteworthy thing is that even after earning an Apache Spark Certification, interviewers would question and judge you on the amount of practical knowledge you have and the finesse required in its application. For any successful Spark developer, both of these skills are crucial. Hence, it is recommended that you make your learning holistic with enough hands-on experience which will give you the confidence you need in applying this knowledge. So, let’s discuss the need for Spark and the boom of big data first.
How is the Big Data Market Growing?
Big data is actually data that comes in massive volumes. With the significant developments and breakthroughs in the world of the internet over the years, it is clear that huge amounts of data are getting produced every day. It is estimated that over 500+ terabytes of data are stored in the archives of Facebook daily. That is just one example of the sheer volume of data we create. Every click, every like, every share, and even the time we spend on any website are collected as data information.
This field was stagnated until we reached the computational power of the 21st century. The increment of processing power and its availability has really helped big data in gaining the momentum it enjoys today. The real reason for the growth of this field is not just computational power. The sheer value of the insights gained from the processing of big data makes it indispensable in the current tech-driven paradigm. To make handling of big data more manageable, Apache Spark was born.
What is Apache Spark?
Apache Spark is the framework created to process data regardless of its size, with more ease and speed. It can distribute the workload of handling the data among several computers and also supports other task distribution tools or frameworks. The property of handling the data with ease and supporting task distribution makes this framework very popular because, in any big data pipeline, the presence of these two features can make or break the project. Apache Spark does more than just these two. It also reduces the load on the programmers by providing a very easy to use API. In this API, you can find many vital codes already written for you, thereby eliminating the recurring menial tasks and allowing you to focus on the problem that matters. The fact that this framework can be easily applied and deployed with all the primary programming languages just amps up the importance of this framework.
Beginning small in the AMPLab at UC Berkeley in the year 2009, Apache Spark is now adopted by tech giants like Facebook, IBM, and even Microsoft in order to improve their big data handling stack.
How to Become a Spark Developer?
The difference between an average and a good Spark developer is massive. Considering the blinding speed at which the field of big data is growing, having just theoretical knowledge will just not suffice. Thus, to ensure that you become an industry-ready Spark developer, we have collected and condensed all the key steps that you should take in your journey to be an expert level Spark developer:
- You should begin your journey by choosing the right certification course. It would be your introduction to the world of Apache Spark. Make sure the course is comprehensive, and the teaching style of your teacher fits your learning ease. Certification is by far the easiest way to back the listing of any skill you write in your resume. Hence, it is recommended that you choose a Spark Certification Course.
- You should not leave everything to just your course. You should start working on a project using the knowledge you have gained through your certification. Make sure you complete an end to end project and try combining different tech stack to increase its weight on your resume.
- You can work on the two main basic features or building blocks of Spark, namely RDDs and DataFrames. Ensure that you are well-versed in the working of these two and that you can use either or both of them in your project without breaking a sweat.
- Try and understand how the integration of Spark works with different languages. For example, you can understand the workings of frameworks like PySpark, which is the Python module of Spark.
- You should also learn how to integrate Spark with other programming languages like Java.
- Once you have the basics all under your belt, you could move onto the various essential parts of Spark like SparkSQL, Spark GraphX, SparkR, SparkML-Lib, just to name a few.
- After your certification is done, you would be ready to make the giant leap. You are prepared for some examinations. You can apply for the CCA-175 accreditation or any other important Hadoop/Spark Certification exam of your choice.
- After you have gained all the certification and passed the exam, you would have amassed a decent amount of knowledge of this Apache Spark framework. Since you will have certifications and projects both backing you up, the road for getting a job would become very easy. Any recruiter will appreciate this aspect of your resume, and your chances of getting selected automatically increases.
The rise in the big data industry and lucrative packages are the two most prominent driving factors to become a Spark Developer. However, you would need to practice the knowledge gained from your Spark Certification. Developing projects is also an excellent way to practice and show off skills. If you play your cards right, you can become a professional Apache Spark developer.
