Dealing with large volumes of data is a necessity in today’s data-centric world; the task at hand demands a lot of power. Fresh challenges are thrown towards the developers and analysts every once in a while. The tools need to be constantly re-empowered to deal with these challenges. Apache Spark is one such tool that packs real power. We will find out how and why.
Some context
The amount of digital information has grown unstoppably in the last couple of decades more so in the current decade. It was around the break of the decade when the business organizations of all shapes and sizes in some parts of the world took note of the inherent possibilities of data. The world changed really fast from that juncture. Collecting and analyzing data seemed imperative in order to gain a competitive edge but the cost of data storage was immense. Distributed file system came to the rescue and we were in the age of Hadoop. Now data was stored and processed at a low cost but we needed more speed and flexibility and there came Spark.
What is Spark?
Spark is a very fast, distributed computational framework which can access data from diverse data sources and process them with almost real-time efficiency.
What is so special about Spark?
Spark has really taken the game of big data analytics one step forward in terms of efficiency, flexibility, and application.
Let us talk about efficiency first
Spark is famous for the speed it provides when it comes to data processing. The most recent edition of Spark i.e. Spark 2.3 has brought the latency in Spark streaming down to less than 1ms. One instance should better relate the importance of this speed. The financial industry has been making the most notable usage of data analytics for risk reduction and credit validation. The process of assessing a credit application used to take 10-15 days; with Spark in place, it takes 2-5 seconds. Consequently, the demand for candidates with Spark big data training has significantly increased since 2015.
The flexibility allowed by Spark
Spark can work with cluster managers like Hadoop, all by itself or on Apache Mesos. But that is not the end. Spark is developed in Scala which is a popular programming language. But it can be programmed in R or Python as well. This feature is really advantageous for data science professionals who are better acquainted with R or Python. The cloud platform built on top of Spark called Delta has also increased its flexibility.
In terms of application, Spark is all set to reach new heights
A new project launched by Databricks (the original creators of Spark) aims at unifying artificial intelligence and big data on Spark. This will put data storage, processing, machine learning and AI implementation on the same table. Understandably this development changes the scenario further in favor of Spark professionals. Getting Spark big data training might be the best decision for your career at this point.
