Why Apache Spark Is So Important For The Big Data? |
Posted: July 3, 2018 |
If one thing that can sum up our times it is speed. It is the speed that we seek in everything, from motor cars to machines to computers everything must perform fast. No wonder we are living in a fast-paced world that changes even before we can adjust to it. This is even more so about the data and data analysis that is generated every moment. It is much more significant than we can comprehend. But this is how it is in the times of internet and internet of things where everything boils down to data streams. Indeed, the internet is the biggest source of big data, and since everything today must conform to the internet, every business needs to be part of this big data pool. You need to collate, analyze, understand and make use of it to make your business run and compete. That is the stark reality, and if you don’t, you would be left behind. To make sense of such big data in our businesses, hospitals, colleges, airports, military installations, we need a tool that can perform this task accurately and faster. This is where open source analytics come into the picture. Apache Spark has emerged as number one choice of data analyst when it comes to big data analytics. It is fast, easy and secure and can make a hell of a difference to your understanding of your own business. Apache Spark is currently is very hot, and many organizations are using it to do more with their data. It is an Analytics Operating System. It is an open source tool with over 400 developers contributing to developing Apache Spark use cases. Spark is an open source tool that the AMP Lab at UC Berkeley developed. Apache Spark is a general-purpose engine for large-scale data processing, up to 1000s of nodes. It is an in-memory distributed computing engine that is versatile to any environment. This enables users and developers to build models, iterate faster and gather deep insights into your data. Spark’s main feature is its Resilient Distributed Datasets (RDDs). This allows collections of objects to be stored in memory or disk across a cluster, which automatically rebuilds on failure. Its in-memory primitive’s offer up to 100 times faster performances. Spark lets data scientists and developers work together on a unified platform. It enables developers to execute Python or Scala code across a cluster instead to one machine. Users can load data into a cluster’s memory, and they can query it again and again. Spark is an advanced analytics tool that is very useful for machine learning algorithms because of these clusters. Spark by sector includes consumer-packaged goods (CPG), insurance, media and entertainment, pharmaceuticals, retailers, automotive. Which constitute main use cases. But, there are also many use cases for Spark where high-velocity, high-volume data is generated in Such scenarios, Apache Spark use cases include fraud detection, log processing as part of IT Operations Analytics, sensor data processing and of course data related to the Internet of Things.
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