Pavlo Comparison 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.

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.

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.

In an attempt to manage their data correctly, organizations are realizing the importance of Hadoop for the expansion and growth of business. According to a study done by Gartner, an organization loses approximately 8.2 Million USD annually through poor data quality. This happens when 99 percent of the organizations have their data strategies in place. The reason behind this is simple – the organizations are unable to trace the bad data that exists within their data. This is one problem which can be easily solved by adopting Hadoop testing methods which allows you to validate all of your data at increased testing speeds and boosts your data coverage resulting in better data quality.

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

Hadoop is one of the open source frameworks, is used as extension to big data analytics framework which are used by a large group of vendors. This type of framework makes work easy for the companies how they?re going to store and can use the data within the digital products as well as physical products (James, M. et al. 2011). We can analyze data using Hadoop, which is emerging as solution to

In your business, you have your own big data challenges. You have to turn heaps of data about various entities into actionable information. The reporting needs of institutions have evolved from simple single subject queries to data discovery and enterprise-wide analysis that tells a complete story across the institution. While the volume, variety and velocity of big data seem overwhelming, big data technology solutions hold great promise. The way I see it we can use this as one of the biggest asset for the company. We have the capacity to see patterns recounting in real time across complex systems. Huron is marshalling its resources to bring smarter computing to big data. With the Huron big data platform, we are enabling our clients to manage data in ways that were never thought possible before.

The main purpose of this report is to provide a critical review of the processes and own experiences of Hadoop within the context of the assignment which was given to us. The review concentrates on the discussion and evaluation of the overall steps followed during the progress of the project and the reasons for which we have chosen these particular steps. It also draws attention at the main points that were accomplished, both with respect to individual, and with respect to the group 's perspectives. Finally, it concentrates on the project 's progress in terms of changes for a future implementation.

.In this paper, we analyze the design choices that allowed modern scalable data management systems to achieve orders of magnitude higher levels of scalability compared to traditional databases. The challenge of building consistent, available, and scalable data management systems capable of serving petabytes of data for millions of users has confronted the data management research community as well as large internet enterprises. Current proposed solutions to scalable data management, driven primarily by prevalent application requirements, limit consistent access to only the granularity of single objects, rows, or keys, thereby trading off consistency for high scalability

Over the years it has become very essential to process large amounts of data with high precision and speed. This large amounts of data that can no more be processed using the Traditional Systems is called Big Data. Hadoop, a Linux based tools framework addresses three main problems faced when processing Big Data which the Traditional Systems cannot. The first problem is the speed of the data flow, the second is the size of the data and the last one is the format of data. Hadoop divides the data and computation into smaller pieces, sends it to different computers, then gathers the results to combine them and sends it to the application. This is done using Map Reduce and HDFS i.e., Hadoop Distributed File System. The data node and the name node part of the architecture fall under HDFS.

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.

Recent advancements in internet communication and in parallel computing grabbed the attention of a large number of commercial organizations and industries to adapt the recent changes in storage and retrieval methods. This includes the new data retrieval and mining schemas which enable the firms to provide their clients a wide space for carrying their job processing and storing of the personal data. Although the new storage innovations made the user data to accommodate the petabyte scale in size, the storing schemas are still on the research desk to compete with this adaptation. Some of the new research outcomes which gained a high popularity and become the need of the hour is the Hadoop. Hadoop is developed by Apache based on the papers of

Apache Spark is a general-purpose & lightning fast cluster computing system. It provides high- level API. For example, Java: Scala, Python and R. Apache Spark is a tool for Running Spark Applications. Spark is 100 times faster than Bigdata Hadoop and 10 times faster than accessing data from disk. On the other hand, Hadoop is an open source, Scalable, and Fault tolerant framework written in Java. It efficiently processes large volumes of data