Spark - mainly designed for Data Science - is considered as the largest Open source project for Data Processing. Let's talk about sparkling features of Apache Spark - Features set is more than enough to justify the pluses of using Apache Spark for Big Data Analytics, yet to justify the scenarios when and when not to use Spark is necessary to provide broader insights. Deployment modules that are co-related with Data Streaming, Machine Learning, Collaborative Filtering Interactive Analysis, and Fog Computing should surely use the perks of Apache Spark to experience a revolutionary change in decentralized storage, data processing, classification, clustering, data enrichment, complex session analysis, triggered event detection and ETL streaming. Spark is not fit for a multi-user environment. Spark as of now is not capable of handling more users concurrency, maybe in future updates this issue will be overcome.
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 blog, we will cover what is the difference between Spark and Hadoop MapReduce.
Apache Spark and Storm has become quite popular in recent times as the open-source choices for the organizations to support streaming analysis in the Hadoop Stack. What exactly are Hadoop, Spark and Storm frameworks? We will also learn about the similarities and differences among these frameworks. Hadoop is an open-source distributed processing framework. It is used for storing huge volumes of data and to run distributed analytics processes on various clusters.
The term Big Data has created a lot of hype already. Chief managers know that their marketing strategies are most likely to yield successful results when planned around big data analytics. For simple reasons, use of big data analytics helps improve business intelligence, boost lead generation efforts, provide personalized experiences to customers and turn them into loyal ones. However, it's a challenging task to make sense of vast amounts of data that exists in multi-structured formats like images, videos, weblogs, sensor data, etc. In order to store, process and analyze terabytes and even petabytes of such information, one needs to put into use big data frameworks.
Spark and Hadoop are two different frameworks, which have similarities and differences. Also, both of them have their unique pros and cons. So, which one is better; Spark or Handoop? There is no exact answer, because, these platforms are different for comparison, and everyone may find some new and useful features in both of them. So let's start from history of developing of these two.