A survey conducted in the US confirms that the big data professionals with Spark skills have got an average hike of $11000 in their median salary. If we are to consider these statistics from any corner of the world the only logical conclusion would be – Let us learn Spark. Well the big data project called Spark by the Apache Software foundation has shaken the analytics world with its scintillating speed so much so that, although a little oddly, Spark has become a competitor of Hadoop software suit.
Is there a conflict between Hadoop and Spark?
If you have been following the trends of the world of big data analytics you must know that Hadoop has for a considerable span of time been the most successful and most vastly used software system for big data operations. The advent of Spark has confused a lot of companies. Generalisations apart, the reality is, in spite of having some similar features Hadoop and Spark have both their unique plus points and can function very well together. So, all of you; who have made up your mind about getting Hadoop training, go ahead. Spark big data training can still be a nice icing on the cake even if you are already operating Hadoop oriented functions.
Deeper into the problem
Although this article is not about Hadoop vs Spark, the comparison is very real and happening all the time. And one must go a little deep into it to avoid rash conclusions. Well, the reason behind Hadoop’s ladder of success scaling right through the roof is the Hadoop Distributed File System or HDFS. At a time when companies were worried about their data yet could not afford the amount of storage space required, HDFS brought in an unimaginably handy solution in an affordable price. The other tools provided by Hadoop like Mapreduce were doing a decent job. Spark dropped in and took the world by storm with its speed. Spark copies the data into faster logical RAM memory from the distributed storage system. Spark’s in memory operations occur 100 times faster than similar Hadoop tools. But Spark does not offer its own distributed file storage. So Hadoop and Spark should work perfectly well with each other- HDFS for storing the data and Spark for analysing it in a flash.
It’s a matter of time before the whole world turns toward Spark
Since Spark is an open source software system, it is cheap. With the kind of speed and functionality offered by Spark it is only a matter of time that the whole world goes after Spark developers. The analytics industry is well set to encounter a global shortage of 100,000 professionals by 2020. Having Spark big data training does make you a preferable choice.
What makes Spark special?
Real time stream processing is becoming increasingly important among all the big data functions. It means analyzing the data just as it is captured and feeding it back to the user. Spark can make a lot of difference in this area with its speed. It is also adept in running machine learning algorithms. These are probably the most critical reasons behind the explosive popularity of Spark and the wide spread demand of Spark developers. And this should be adequate reason for you to undergo Spark big data training.
Hadoop and Spark to join hands
The Hadoop- spark integration is taking place all around. It is expected that most of the Hadoop projects will soon become Spark oriented projects. If you already have an idea of how large the market for Hadoop is you can easily catch the implication. If you do not have an idea – Hadoop’s market is predicted to reach $50 billion mark by 2020 at a CAGR of over 58%. If, as predicted 8 out of 10 Hadoop projects are Spark oriented Spark big data training is definitely something you need.
At any given point searching for Spark developer jobs on any job portal yields more than 40,000 results. The fraternity is growing fast and gaining strength and may soon eclipse Hadoop’s Mapreduce. You surely would like to get your hands practised in Spark operations before that point arrives.