‘Lightning-fast cluster computing’ – that’s the slogan of Apache Spark, one of the world’s most popular big data processing frameworks. It has witnessed rapid growth in the last few years, with companies like e-bay, Yahoo, Facebook, Airbnb, and Netflix adopting the framework for their significant data needs.
With the rise of the Internet-of-Things (IoT) and social media’s ubiquitous use, there has been a spike in data volumes. According to a Gartner estimate, there are around 6.4 billion devices plugged into the Internet, generating about 2.5 exabytes of data every day.
Big data techniques and tools help companies manage all this data, ranging from our bank transactions to our activity on social networks like Facebook and Twitter.
And then there are questions to be answered about this data: how can we detect fraud in our bank transactions, which advertisement in Facebook gets the most clicks, and so on.
To answer these questions, large volumes have to be processed quickly, and this is where Spark enters the picture.
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With the release of Spark 2.0 last summer, the framework is becoming more mature. It has reached a point where tech junkies are not the only people who are aware of the Spark phenomenon – business leaders are waking up to its potential. IBM has made a significant commitment to it and calls it ‘potentially the most significant open source project of the next decade.’ The success of Spark in projects like personalized DNA Analysis, also contributes to the belief that it works well in real-life projects.
So why do businesses invest in Spark?
Spark started life in 2009 at the University of California in Berkeley as a project by Matei Zaharia. Matei created Spark when working on his Ph.D. at Berkeley’s AMPLab, an institute that researches big data analytics. He is currently the Chief Technologist of Databricks, which is a company that helps clients with cloud-based big data processing using Spark.
Spark was open-sourced in 2010 and was donated to the Apache software foundation in 2013. It is now a top-level Apache project and the largest open source project in the data processing.
There are a couple of reasons why Spark is so popular and why there is a massive demand for people with Spark skills -
From my own experience as a data scientist, I know that Spark has gained in popularity across verticals. There has been a steep rise in Spark-related projects over the last few years. I have seen requests, for example, from clients to “migrate our current solution in python/R to Spark to improve performance.”
If you’re new to this field and would like to learn Spark, online courses are your best bet. Simplilearn’s Apache Spark certification training course covers Scala programming, Spark streaming, machine learning, and shell scripting with 30 demos, an industry project, and 32 hours of live instructor-led training.
I hope this article has given you an idea about Apache Spark and its use. Please let me know about any comments or questions you might have.
Ger Inberg is a freelance data scientist with a background in software development. He is currently helping clients in the field of machine learning and data visualization.
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