Big Data Journal

Data has been increasing at an alarming rate. And this could be brought to control with special techniques or software. One of such is Hadoop. Being open-source software, Hadoop refers to the formulation of data sets which become difficult to be managed, processed and analysed. Delving into the details, let us understand the components of Hadoop. What is the component of Hadoop? There are mainly three components of Hadoop. You will be able to learn about the same in details at the best data science training institute in Bangalore . Hadoop Distributed File System The first component serves in the distribution of the data and further stores it into the distributed file system. This system is known as Hadoop Distributed File System or HDFS. Data is spread among machines in advance. For conducting the initial process, there is no data transfer needed. Computation occurs when the data gets stored wherever feasible. Map-Reduce (MapR) It is mainly used for high-level data pro

For a large community, Python continues to be the core choice of data scientists. It has become the base of programming language for data science/WebOps. In simpler words, Python is an essential skill for the development of modern applications. Python's rising popularity in the data science peripheral has acquired a special aspect for deep learning and machine learning libraries. It also includes tools like scikit-learn, Keras and TensorFlow. Data scientists have been using the base programming language for the development of exemplary data models. They plug directly into a production system. Starting from data collection to exploration, modelling and visualisation, Python's libraries NumPy and SciPy pave the segment to strong algorithms. This mainly entitles analysts and data scientists for delving into machine learning without any hidden glitches. As per the IEEE Spectrum report, Python has swiped off Java in terms of web and enterprise. This is because of the ri