Big Data & Microservices - Know How?

The world as we know it is moving towards machines — big time. But we cannot fully utilize the working of any machine without a lot of human interaction. In order to do that, we need some kind of intelligence for the machines. This is where artificial intelligence comes in. It is the concept of machines being smart enough to carry out numerous tasks without any human intervention. The terms  artificial intelligence  and  machine learning  often lead to confusion and many of us don't exactly know the difference between them. Hence, we end up using these terms interchangeably. Machine learning is basically the learning concepts of machines through which we can achieve artificial intelligence.  Deep learning  is the latest thing in the artificial intelligence field. It is one of the ways to implement machine learning to achieve AI. Most of us have seen AI-based movies with machines having their own intelligence like the  Terminator  series or  I, Robot . But in real life, the co

The term big data has created a lot of hype already in the business world. Hadoop and Spark are both big data frameworks; they provide some of the most popular tools used to carry out common big data-related tasks. In this article, we will cover the differences between Spark and Hadoop MapReduce. Introduction Spark :   It is an open-source big data framework. It provides a faster and more general-purpose data processing engine. Spark is basically designed for fast computation. It also covers a wide range of workloads — for example, batch, interactive, iterative, and streaming. Hadoop MapReduce :   It is also an open-source framework for writing applications. It also processes structured and unstructured data that are stored in HDFS. Hadoop MapReduce is designed in a way to process a large volume of data on a cluster of commodity hardware. MapReduce can process data in batch mode. Data Processing Spark :   Apache Spark is a good fit for both batch processing and stream proce