Apart from the fact that Data Science is one of the highest paid and most popular fields of date, it is also important to note that it will continue to be more innovative and challenging for another decade or more. There will be enough data science jobs that can fetch you a handsome salary as well as opportunities to grow.
In this article, we will see the key differences between Hadoop vs Spark. Before that, let us get a fix on what gave way to Hadoop and Spark. As all of us know, the growth of the Internet has resulted in huge volumes of data being continuously generated – what is called Big Data. This data in both structured and unstructured form is generated from primary sources such as social networks, the Internet of Things and traditional transactional business. Distributed Computing a new generation of Data Management of the Big Data is a revolutionary innovation in hardware and software technology. Distributed data processing facilitates the storage, processing, and access of this high velocity, large volume, and a wide variety of data.
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Data Science is the buzzword for the current and perhaps the next few generations. If you want to read about Data science, this blog will certainly help you. While everyone is going gaga about data science and how to become a data scientist, it is important you know the difference between being a data scientist and a data analyst.
The world today is incomplete without data. Humongous amounts of data are generated by users every day. If this data can be somehow analyzed and interpreted to capture what the user wants and make innovations accordingly, we could bring in a revolutionary system where businesses can provide state-of-the-art solutions to the problems faced by a common man and that too at low costs. Better still, this system can improvise and improve itself to be more innovative by the day. This revolution is data science and involves data analytics, machine learning and much more.