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Understanding Data Science With Apache Spark

Data Science is one among the core technologies in the analytics industry which is on its way towards transforming the world. The demand for Big Data & Data Science has led to the development of advanced tools & sophisticated algorithms which enhanced the potential to harness the Big Data & thus it helps in developing advanced intelligent systems.

In this blog post, let’s discuss about the advantages of using Spark in Data Science.  

What is Apache Spark?

Back in 2011, when Apache foundation released Hadoop it become a major game changing event in the industry of Big Data. Over a short period, Hadoop has become the buzzword and a reigning technology. The Hadoop Big Data framework makes use of MapReduce which is quite effective in its functioning but has certain shortcomings. In order to tackle these issues Apache has come up with Spark that best addresses the issues & limitations in Hadoop framework.

Using Spark makes it easier for the analytics experts to carry out Machine Learning operations and SQL workloads in order to precisely access the datasets.

Advanced Features Of Spark For Data Science-

Let’s have a clear look at some of the most advanced features of Spark for Data Science

  • Lighting Fast Processing

One among the major noticeable features of Apache Spark is its lightening processing speed. It can process large volumes of Big Data at a lightning speed & accuracy. Compared to MapReduce, Spark can process the data 100 times more with higher precision. This simply reduces the read-write operations that are appended to the disk.

  • Spark is Dynamic

By using Spark, Data Scientists can work towards developing parallel application. This has become possible with the help of the 80 high-level operators which are embedded within the Spark platform.

  • Fault Tolerance

With the latest advancements & with the help of Spark RDD, Spark has now become full scale-scale fault-tolerance. There is just a minimal scope for the loose of data during the operations using Spark

  • Real-time streaming

Using Spark Data Science can make a better use of the existing platforms like Hadoop. This is simply because of the fact that using Spark data can be streamed in real-time as opposed to batch files.

Get to know more about such advanced concepts, trends & techniques involving Data Science by being a part of Kelly Technologies advanced Data Science Training In Hyderabad program.

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