Performance Of Data Processing Together On Relational Databases And Hadoop Frameworks

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.

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

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.

Hadoop is an Apache open source software (java framework). It runs on cluster of commodity machines and provides both distributed storage and distributed processing of huge data sets. It is capable of processing data sizes ranging from Gigabytes to Petabytes.

Abstract— The Data which is structured and unstructured and is so large with massive volume that it is not possible by traditional database system to process this data is termed as Big Data. The governance, organization and administration of the big data is known as Big Data Management. For reporting and analysis purposes we use data warehouse techniques to process data. These are the central repositories from disparate data sources. Now Big Data Management also requires the data warehousing techniques for future predictions and reporting. So in this paper we touched certain issues of data warehousing usage in Big Data management, its applications as well as limitations also and tried to give the ways data warehousing is useful in Big Data Management.

Big Data and Analytics systems are fast emerging as one of the most critical system in an organization’s IT environment. But with such a huge amount of data, there come many performance challenges. If Big Data systems cannot be used to make or forecast critical business decisions, or provide insights into business values hidden under huge amounts of data at the right time, then these systems lose their relevance. This article talks about some of the critical performance considerations in a technology agnostic way. These should be read as generic guidelines, which can be used by any Big Data professional to ensure that the final system meets all performance requirements.

Hadoop is an open-source framework which is used for processing and analyzing big data. It consists of a Hadoop Distributed File system and MapReduce. (‘Data Lake’, 2015)

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.

Hadoop is one of the most recognized terms in an ocean of big data buzzwords. In short, it is the most efficient tool available to store and analyze data.

This document contains all the information about the advantages and disadvantages of different database models.

Once the data isdata are stored in Hadoop, the big data applications can be used to analyze the data. Figure 4.3 shows a simple standalone Hadoop architecture.

Spark is an in memory cluster computing framework which falls under the open source Hadoop project but does not follow the two stage map-reduce paradigm which is done in Hadoop distributed file system (HDFS) and which is meant and designed to be faster. Spark, instead support a wide range of computations and applications which includes interactive queries, stream processing, batch processing, iterative algorithms and streaming by extending the idea of MapReduce model. The execution time is the most important factor for every process which processes large amount of data. While considering large amount of data, the time it usually takes for the exploration of data and execution of queries can be thought of in terms of

For this coursework two kinds of data models can be used. The object oriented data model, Object Oriented Database Management System(OODBMS), or the relational data model, Relational Database Management System(RDBMS). The differences between these two models and the data model to be used are described in this chapter.

A DBMS contains four parts, the data model, the data definition language (DDL), the data manipulation language (DML), and the data dictionary. To begin with, the data model describes how data are logically constructed. Next, programmers use DDL to specify the types of information and structure of the database, which is the important connection between the logical (how to perceive data) and physical views (how data are stored and processed) of the database. Thirdly, DML is used to manipulate the data, where end users or programmers are allowed to extract data in order to satisfy information requests and develop applications. Manipulative verbs are included in DML, such as ‘modify, delete or select’. In addition, the data dictionary is used for the storage of data elements and data characteristics, including ownership, physical representation, authorization, and security (technology guide). The use of a DBMS

RDBMS stores the data in the form of tables and can be retrieved from the tables with the help of SQL Language. SQL was invented only after the invention of RDBMS. SQL – Sequential Query Language. Most common RDBMS’s are My SQL, DB2, Oracle, and Microsoft SQL. The reason for using RDBMS rather than a DBMS is that DBMS stores data in the form of Files whereas RDBMS stores data in the form of Tables containing rows and columns and there exists a primary key relationship such that the data stored in multiple tables can be retrieved using SQL. As RDBMS is a relational database model, the data is stored in the tables in the form of relations. RDBMS supports Distributed database and is designed to handle large amounts of data.