Iop Essay

Hadoop \cite{white2012hadoop} is an open-source framework for distributed storage and data-intensive processing, first developed by Yahoo!. It has two core projects: Hadoop Distributed File System (HDFS) and MapReduce programming model \cite{dean2008mapreduce}. HDFS is a distributed file system that splits and stores data on nodes throughout a cluster, with a number of replicas. It provides an extremely reliable, fault-tolerant, consistent, efficient and cost-effective way to store a large amount of data. The MapReduce model consists of two key functions: Mapper and Reducer. The Mapper processes input data splits in parallel through different map tasks and sends sorted, shuffled outputs to the Reducers that in turn groups and processes them using a reduce task for each group.

MapReduce Parallel programming model if we ever get a chance. In Hadoop, there are two nodes in the cluster when using the algorithm, Master node and Slave node. Master node runs Namenode, Datanode, Jobtracker and Task tracker processes. Slave node runs the Datanode and Task tracker processes. Namenode manages partitioning of input dataset into blocks and on which node it has to store. Lastly, there are two core components of Hadoop: HDFS layer and MapReduce layer. The MapReduce layer read from and write into HDFS storage and processes data in parallel.

The Hadoop employs MapReduce paradigm of computing which targets batch-job processing. It does not directly support the real time query execution i.e OLTP. Hadoop can be integrated with Apache Hive that supports HiveQL query language which supports query firing, but still not provide OLTP tasks (such as updates and deletion at row level) and has late response time (in minutes) due to absence of pipeline

An important characteristic of Hadoop is the partitioning of data and computation across many (thousands) of hosts, and the execution of application computations in parallel close to their data. A Hadoop cluster scales computation capacity, storage capacity and I/O bandwidth by simply adding commodity servers. Hadoop clusters at Yahoo! span 40,000 servers, and store 40 petabytes of application data, with the largest cluster

Hadoop is a free, Java-based programming framework that supports the processing of large data sets in a Parallel and distributed computing environment. It makes Use of the commodity hardware Hadoop is Highly Scalable and Fault Tolerant. Hadoop runs in cluster and eliminates the use of a Super computer. Hadoop is the widely used big data processing engine with a simple master slave setup. Big Data in most companies are processed by Hadoop by submitting the jobs to Master. The Master distributes the job to its cluster and process map and reduce tasks sequencially.But nowdays the growing data need and the and competition between Service Providers leads to the increased submission of jobs to the Master. This Concurrent job submission on Hadoop forces us to do Scheduling on Hadoop Cluster so that the response time will be acceptable for each job.

Since the 1970’s databases and report generators have been used to aid business decisions. In the 1990’s technology in this area improved. Now technology such as Hadoop has gone another step with the ability to store and process the data within the same system which sparked new buzz about “big data”. Big Data is roughly the collection of large amounts of data – sourced internally or externally - applied as a tool – stored, managed, and analyzed - for an organization to set or meet certain goals.

Research topic was derived from the understanding of query processing in MySQL and Hadoop, the database performance issues, performance tuning and the importance of database performance. Thus, it was decided to develop a comparative analysis to observe the effectiveness of the performance of MySQL (non cluster) and Hadoop in structured and unstructured dataset (Rosalia, 2015). Furthermore, the analysis included a comparison between those two platforms in two variance of data size.

Observing the past trends we can easily figure out the growth rate of Hadoop related jobs. This number is much higher when compared to software testing jobs. The maximum growth rate of software testing jobs has been 1.6 percent approximately as opposed to Hadoop based testing jobs which has been recorded to be a whopping 5 percent. There are certain limitations in the current testing practices when testing applications in finding solutions for Big Data problems. These reasons have made testing professionals to steer towards the Hadoop platform. One of the major reasons is that the software testing approaches are driven by data (for e.g. skewness of data, data sets size and mismatch etc.)

For analysis, advanced statistical tools are used and the experimenter can draw necessary conclusions and inferences. In business sectors, they have a huge amount of scattered data in terms of profits, loss, demand, supply, sales and production. The industrial, insurance, agricultural, banking, information technology, food industry, telecommunication, retail, utilities, travel, pharmacy and many more have challenges to manage their data. As the coin always has two sides, there are both advantages and a few disadvantages of data analysis. The key advantages of data analysis are- The organizations can immediately come across errors, the service provided after optimizing the system using data analysis reduces the chances of failure, saves time and leads to advancement. It is also used to compare strategies between two companies so as to reduce the prices and gaining attention of target customers, ultimately leading to maximization of profit and minimization of cost(as done in Game Theory). However, big data analysis sometimes becomes more tedious and disadvantageous because it uses software Hadoop which requires special provisions in the computers. For now use of Hadoop for real-time analysis is not available. The manner in which the data is collected and the decision making view can vary from one person to another. Here, the quality of data gets affected and leaves the data insufficient or inefficient. In order to tackle this problem, the researcher must be professional, well experienced and should have deep knowledge about the characteristic under study. Also, we need to update data from time to time so as to avoid the changes in trend caused by the past data especially, for the rapidly growing

The paper “A Comparison to Approaches to Large-Scale Data Analysis” by Pavlo, compares and analyze the MapReduce framework with the parallel DBMSs, for large scale data analysis. It benchmarks the open source Hadoop, build over MapReduce, with two parallel SQL databases, Vertica and a second system form a major relational vendor (DBMS-X), to conclude that parallel databases clearly outperform Hadoop on the same hardware over 100 nodes. Averaged across 5 tasks on 100 nodes, Vertica was 2.3 faster than DBMS-X which in turn was 3.2 times faster than MapReduce. In general, the parallel SQL DBMSs were significantly faster and required less code to implement each task, but took longer to tune and load the data. Finally, the paper talk about

I was tasked to help my fellow JETT co-workers to create probe images for the H-1 Flight Computer Control (FCC) Shop Replaceable Assembly (SRA). To create the pictures, four templates were made in Microsoft Visio to make the process simple. The YAW Axis Card Component Layout file was divided into 4 sections, front Top, front Bottom, back Top, and back Bottom. Each template consists of one of the divided parts of the Axis Card, an adjustable red box on the Axis Card image to highlight the area of the component, an empty box for the blow-up image, a small red box to indicate the side of the component to probe, and an arrow to emphasize the side to probe. A spreadsheet for each

Abstract - Hadoop Distributed File System, a Java based file system provides reliable and scalable storage for data. It is the key component to understand how a Hadoop cluster can be scaled over hundreds or thousands of nodes. The large amounts of data in Hadoop cluster is broken down to smaller blocks and distributed across small inexpensive servers using HDFS. Now, MapReduce functions are executed on these smaller blocks of data thus providing the scalability needed for big data processing. In this paper I will discuss in detail on Hadoop, the architecture of HDFS, how it functions and the advantages.

Hortonworks is a business computer software company based in Palo Alto, California. The company focuses on the development and support of Apache Hadoop, a framework that allows for the distributed processing of large data sets across clusters of computers. Architected, developed, and built completely in the open, Hortonworks Data Platform (HDP) provides Hadoop designed to meet the needs of enterprise data processing.HDP is a platform for multi-workload data processing across an array of processing methods from batch through interactive to real-time - all supported with solutions for governance, integration, security and operations.

Data has always been analyzed within companies and used to help benefit the future of businesses. However, the evolution of how the data stored, combined, analyzed and used to predict the pattern and tendencies of consumers has evolved as technology has seen numerous advancements throughout the past century. In the 1900s databases began as “computer hard disks” and in 1965, after many other discoveries including voice recognition, “the US Government plans the world’s first data center to store 742 million tax returns and 175 million sets of fingerprints on magnetic tape.” The evolution of data and how it evolved into forming large databases continues in 1991 when the internet began to pop up and “digital storage became more cost effective than paper. And with the constant increase of the data supplied digitally, Hadoop was created in 2005 and from that point forward there was “14.7 Exabytes of new information are produced this year" and this number is rapidly increasing with a lot of mobile devices the people in our society have today (Marr). The evolution of the internet and then the expansion of the number of mobile devices society has access to today led data to evolve and companies now need large central Database management systems in order to run an efficient and a successful business.

MapReduce: The MapReduce language establishes a base for Hadoop Eco System. It processes Hadoop Distributed File System (HDFS) on large clusters which are made of thousands of commodity hardware in a reliable and fault-tolerant manner. The operations of MapReduce are performed in Map and Reduce functions. The Map function works on a set of input values and transforms them into a set of key/value pairs. The reducer receives all the data for an individual "key" from all the mappers and applies Reduce function to achieve the final result.