Big data is growing in volume and importance. As companies worldwide scramble to make the most sense out of it to accurately predict the outcomes of their business efforts, the professionals who are helping organizations gain actionable insights are also reaping rich rewards. A fast-tracking career with fat compensation package can become a reality for you if you make use of the data analytics trend. Apache Spark and Scala are hot cakes in the analytics domain as an increasing number of companies are adopting this cluster computing framework and scripting language duo.
Spark has replaced Hadoop as the most sought-after big data engine. It can handle literally all types of queries at lighting fast speed as streaming and batch processing capabilities both are supported by Spark. Learning Apache Spark with Scala can be the best thing you can do for your career now.
How Would Knowledge of Spark and Scala Offer You a Competitive Edge?
Functional knowledge of Spark would benefit your career like nothing else. Why? The following points would help you analyse the immense potential of this duo for the perspective of your career.
1) Salaries Going Through the Roof
At the start of 2016, Spark Developers fetched $128,000 on an average as salary in San Francisico as per Indeed.com, the leading job portal. O’Reilly had conducted a salary survey for Data Science way back in 2014 which revealed that the median salary of Spark Developers is the highest among others who use the top 10 Big Data Analysis engines.
In 2015, O’Reilly’s survey again confirmed that developers who use Scala with Apache Spark were being paid the highest. Also, it was observed that knowledge of Spark added in excess of $11,000 to the median salary whereas Scala caused the salary to jump further by $4000. (Source: https://data-flair.training)
Five years down the line, you can just imagine the exponential growth in salaries as Spark continues to dominate the Big Data scenario having toppled the most popular Hadoop from its No.1 position.
2) Hadoop Compatible
Hadoop, after its launch, had seen aggressive investment from companies that wanted to monetize on the data science insights. After Spark had made it to the market, many companies found it economically not feasible to invest again in a new platform. Spark, however, is compatible with Hadoop as it can use the Hadoop hardware, leverage Yarn, i.e. Hadoop’s resource management layer, and facilitates the processing of data within Hadoop Distributed File System.
Apache spark training can serve you dual purpose. You can deliver productively on the Hadoop platform and can remain well-prepared for migration to Spark when your company decides to do so.
3) Diminishing Appeal of Hadoop
Companies are gradually adopting Spark and phasing out Hadoop. This is because Spark can process data 100X faster compared to Hadoop’s MapReduce. Programming in Spark with Scala is easier than MapReduce. On Spark, data is processed in-memory and all capabilities of Hadoop are supported. Spark is not restricted to one framework (like Hadoop is on MapReduce) and can embrace other components to facilitate quicker processing of voluminous amounts of data. So, you would remain relevant to the contemporary job market by learning Spark.
4) Quick Coding, Low Latency
Organizations worldwide are adopting Spark for another reason. Applications on Spark cluster framework can be written with Scala, R, Python, and Java. You can write and execute programs in a language you are comfortable with. The reason for preferring Scala is that it enjoys Java support. So, codes can be written concisely. A 100 lines MapReduce code on Hadoop can be written in just 5 to 6 lines in Scala and Spark.
5) High on Versatility, High on Demand
Frantic demand for industry professionals with knowledge of Spark is triggered by the fact that Spark offers solutions for every processing requirement. Customized big data apps can be quickly created in Spark, SQL can be used for analysing data with Spark SQL, real-time data processing is fast-tracked by Spark Streaming, ETL pipelines can be established, graphs can be processed with GraphX, and Machine Learning (ML) can be used for optimum predictability with MLlib library.
The time is ripe for capitalizing on the Spark wave until something more efficient makes it to the market. Data volume being generated online is growing by the day. Conventional methods are no longer capable of processing the same at the desired pace. Only Spark allows real-time data analysis at a blistering pace without being overly complex.
Train Yourself in Spark and Scala Now
Apache Spark is the toast of the industry. Securing a certification in this would help you land a lucrative job with high career advancement prospects easily. Subscribe now for Apache Spark and Scala course at a renowned institute and steer your career on the path of steady growth.