The Big data is the name used ubiquitously now a day in distributed paradigm on the web. As the name point out it is the collection of sets of very large amounts of data in pet bytes, Exabyte etc. related systems as well as the algorithms used to analyze this enormous data. Hadoop technology as a big data processing technology has proven to be the go to solution for processing enormous data sets. MapReduce is a conspicuous solution for computations, which requirement one-pass to complete, but not exact efficient for use cases that need multi-pass for computations and algorithms. The Job output data between every stage has to be stored in the file system before the next stage can begin. Consequently, this method is slow, disk Input/output operations and due to replication. Additionally, Hadoop ecosystem doesn't have every component to ending a big data use case. Suppose we want to do an iterative job, you would have to stitch together a sequence of MapReduce jobs and execute them in sequence. Every this job has high-latency, and each depends upon the completion of the previous stage. Apache Spark is one of the most widely used open source processing engines for big data, with wealthy language-integrated APIs and an extensive range of libraries. Apache Spark is a usual framework for distributed computing that offers high performance for both batch and interactive processing. In this paper, we aimed to demonstrate a close-up view about Apache Spark and its features and working with Spark using Hadoop. We are in a nutshell discussing about the Resilient Distributed Datasets (RDD), RDD operations, features, and limitation. Spark can be used along with MapReduce in the same Hadoop cluster or can be used lonely as a processing framework. In the last comparative analysis between Spark and Hadoop and MapReduce in this paper.

The extent to which data is generated has shown a tremendous increase in the past decade because of social networks, sensor networks, geographic information systems, Financial Institutions, Supply chains. The storage capacities of computers have increased to stay competitive, but a big problem is that the access speeds of the disk has not improved to that extent to be at par with disk space improvement. Big Data comes to the rescue with a framework to analyse massive amounts of data in a distributed environment which is both horizontally and vertically scalable. Data sets with trillions of rows can be analysed very fast to provide valuable insights from data. Cloud service providers such as Amazon, Alibaba Cloud have made available robust infrastructure for Big Data. We study Apache Hive, Spark Mllib in profiling a Social Network Dataset and Collaborative Filtering algorithm in Spark Mllib for movie recommendations.

Sheer increase in volume of data over the last decade has triggered research in cluster computing frameworks that enable web enterprises to extract big insights from big data. While Apache Spark is gaining popularity for exhibiting superior scale-out performance on the commodity machines, the impact of data volume on the performance of Spark based data analytics in scale-up configuration is not well understood. We present a deep-dive analysis of Spark based applications on a large scale-up server machine. Our analysis reveals that Spark based data analytics are DRAM bound and do not benefit by using more than 12 cores for an executor. By enlarging input data size, application performance degrades significantly due to substantial increase in wait time during I/O operations and garbage collection, despite 10% better instruction retirement rate (due to lower L1 cache misses and higher core utilization). We match memory behavior with the garbage collector to improve performance of applications between 1.6x to 3x.

MapReduce and Spark are two very popular open source cluster computing frameworks for large scale data analytics. These frameworks hide the complexity of task parallelism and fault-tolerance, by exposing a simple programming API to users. In this paper, we evaluate the major architectural components in MapReduce and Spark frameworks including: shuffle, execution model, and caching, by using a set of important analytic workloads. To conduct a detailed analysis, we developed two profiling tools: (1) We correlate the task execution plan with the resource utilization for both MapReduce and Spark, and visually present this correlation; (2) We provide a breakdown of the task execution time for in-depth analysis. Through detailed experiments, we quantify the performance differences between MapReduce and Spark. Furthermore, we attribute these performance differences to different components which are architected differently in the two frameworks. We further expose the source of these performance differences by using a set of micro-benchmark experiments. Overall, our experiments show that Spark is about 2.5x, 5x, and 5x faster than MapReduce, for Word Count, k-means, and PageRank, respectively. The main causes of these speedups are the efficiency of the hash-based aggre-gation component for combine, as well as reduced CPU and disk overheads due to RDD caching in Spark. An exception to this is the Sort workload, for which MapReduce is 2x faster than Spark. We show that MapReduce's execution model is more efficient for shuffling data than Spark, thus making Sort run faster on MapRe-duce.

Master alternative Big Data technologies that can do what Hadoop can't: real-time analytics and iterative machine learning. When most technical professionals think of Big Data analytics today, they think of Hadoop. But there are many cutting-edge applications that Hadoop isn't well suited for, especially real-time analytics and contexts requiring the use of iterative machine learning algorithms. Fortunately, several powerful new technologies have been developed specifically for use cases such as these. Big Data Analytics Beyond Hadoop is the first guide specifically designed to help you take the next steps beyond Hadoop. Dr. Vijay Srinivas Agneeswaran introduces the breakthrough Berkeley Data Analysis Stack (BDAS) in detail, including its motivation, design, architecture, Mesos cluster management, performance, and more. He presents realistic use cases and up-to-date example code for: Spark, the next generation in-memory computing technology from UC Berkeley Storm, the parall...

While cluster computing frameworks are continuously evolving to provide real-time data analysis capabilities, Apache Spark has managed to be at the forefront of big data an-alytics for being a unified framework for both, batch and stream data processing. However, recent studies on micro-architectural characterization of in-memory data analytics are limited to only batch processing workloads. We compare micro-architectural performance of batch processing and stream processing workloads in Apache Spark using hardware performance counters on a dual socket server. In our evaluation experiments, we have found that while batch processing workloads are bounded on the latency of frequent data accesses to DRAM, stream processing workloads are curbed by L1 instruction cache misses. For data accesses we have found that simultaneous multi-threading is effective in hiding the data latencies. We have also observed that (i) data locality on NUMA nodes can improve the performance with up to 12%, (ii) disabling next-line L1-D prefetchers can reduce the execution time by up-to 15% and (iii) multiple small executors can provide up-to 36% speedup over single large executor.