969 research outputs found
Storage Solutions for Big Data Systems: A Qualitative Study and Comparison
Big data systems development is full of challenges in view of the variety of
application areas and domains that this technology promises to serve.
Typically, fundamental design decisions involved in big data systems design
include choosing appropriate storage and computing infrastructures. In this age
of heterogeneous systems that integrate different technologies for optimized
solution to a specific real world problem, big data system are not an exception
to any such rule. As far as the storage aspect of any big data system is
concerned, the primary facet in this regard is a storage infrastructure and
NoSQL seems to be the right technology that fulfills its requirements. However,
every big data application has variable data characteristics and thus, the
corresponding data fits into a different data model. This paper presents
feature and use case analysis and comparison of the four main data models
namely document oriented, key value, graph and wide column. Moreover, a feature
analysis of 80 NoSQL solutions has been provided, elaborating on the criteria
and points that a developer must consider while making a possible choice.
Typically, big data storage needs to communicate with the execution engine and
other processing and visualization technologies to create a comprehensive
solution. This brings forth second facet of big data storage, big data file
formats, into picture. The second half of the research paper compares the
advantages, shortcomings and possible use cases of available big data file
formats for Hadoop, which is the foundation for most big data computing
technologies. Decentralized storage and blockchain are seen as the next
generation of big data storage and its challenges and future prospects have
also been discussed
In-Memory and Column Storage Changes IS Curriculum
Random Access Memory (RAM) prices have been dropping precipitously. This has given rise to the possibility of keeping all data gathered in RAM rather than utilizing disk storage. This technological capability, along with benefits associated with a columnar storage database system, reduces the benefit of Relational Database Management Systems (RDBMS) and eliminates the need for Online Transactional Processing (OLTP) and Online Analytical Processing (OLAP) activities to remain separate. The RDBMS was a required data structure due to the need to separate the daily OLTP activities from the OLAP analysis of that data. In-memory processing allows both activities simultaneously. Data analysis can be done at the speed of data capture. Relational databases are not the only option for organizations. In-Memory is emerging, and university curriculum needs to innovate and create skills associated with denormalization of existing database (legacy) systems to prepare for the next generation of data managers
In-Memory Databases
Táto práca sa zaoberá databázami pracujúcimi v pamäti a tiež konceptmi, ktoré boli vyvinuté na vytvorenie takýchto systémov, pretože dáta sú v týchto databázach uložené v hlavnej pamäti, ktorá je schopná spracovať data niekoľkokrát rýchlejšie, ale je to súčasne nestabilné pamäťové medium. Na podloženie týchto konceptov je v práci zhrnutý vývoj databázových systémov od počiatku ich vývoja až do súčasnosti. Prvými databázovými typmi boli hierarchické a sieťové databázy, ktoré boli už v 70. rokoch 20. storočia nahradené prvými relačnými databázami ktorých vývoj trvá až do dnes a v súčastnosti sú zastúpené hlavne OLTP a OLAP systémami. Ďalej sú spomenuté objektové, objektovo-relačné a NoSQL databázy a spomenuté je tiež rozširovanie Big Dát a možnosti ich spracovania. Pre porozumenie uloženia dát v hlavnej pamäti je predstavená pamäťová hierarchia od registrov procesoru, cez cache a hlavnú pamäť až po pevné disky spolu s informáciami o latencii a stabilite týchto pamäťových médií. Ďalej sú spomenuté možnosti usporiadania dát v pamäti a je vysvetlené riadkové a stĺpcové usporiadanie dát spolu s možnosťami ich využitia pre čo najvyšší výkon pri spracovaní dát. V tejto sekcii sú spomenuté aj kompresné techniky, ktoré slúžia na čo najúspornejšie využitie priestoru hlavnej pamäti. V nasledujúcej sekcii sú uvedené postupy, ktoré zabezpečujú, že zmeny v týchto databázach sú persistentné aj napriek tomu, že databáza beží na nestabilnom pamäťovom médiu. Popri tradičných technikách zabezpečujúcich trvanlivosť zmien je predstavený koncept diferenciálnej vyrovnávacej pamäte do ktorej sa ukladajú všetky zmeny v a taktiež je popísaný proces spájania dát z tejto vyrovnávacej pamäti a dát z hlavného úložiska. V ďalšej sekcii práce je prehľad existujúcich databáz, ktoré pracujú v pamäti ako SAP HANA, Times Ten od Oracle ale aj hybridných systémov, ktoré pracujú primárne na disku, ale sú schopné pracovať aj v pamäti. Jedným z takýchto systémov je SQLite. Táto sekcia porovnáva jednotlivé systémy, hodnotí nakoľko využívajú koncepty predstavené v predchádzajúcich kapitolách, a na jej konci je tabuľka kde sú prehľadne zobrazené informácie o týchto systémoch. Ďalšie časti práce sa týkajú už samotného testovania výkonnosti týchto databáz. Zo začiatku sú popísané testovacie dáta pochádzajúce z DBLP databázy a spôsob ich získania a transformácie do použiteľnej formy pre testovanie. Ďalej je popísaná metodika testovania, ktorá sa deli na dve časti. Prvá časť porovnáva výkon databázy pracujúcej v disku s databázou pracujúcou v pamäti. Pre tento účel bola využitá databáza SQLite a možnosť spustenia databázy v pamäti. Druhá časť testovania sa zaoberá porovnaním výkonu riadkového a stĺpcového usporiadania dát v databáze pracujúcej v pamäti. Na tento účel bola využitá databáza SAP HANA, ktorá umožňuje ukladať dáta v oboch usporiadaniach. Výsledkom práce je analýza výsledkov, ktoré boli získané pomocou týchto testov.This bachelor thesis deals with in-memory databases and concepts that were developed to create such systems. To lay the base ground for in-memory concepts, the thesis summarizes the development of the most used database systems. The data layouts like the column and the row layout are introduced together with the compression and storage techniques used to maintain persistence of the in-memory databases. The other parts contain the overview of the existing in-memory database systems and describe the benchmarks used to test the performance of the in-memory databases. At the end, the thesis analyses the results of benchmarks.
In-memory business intelligence: a Wits context
The organisational demand for real-time, flexible and cheaper approaches to Business Intelligence is
impacting the Business Intelligence ecosystem. In-memory databases, in-memory analytics, the
availability of 64 bit computing power, as well as the reduced costs of memory, are enabling
technologies to meet this demand. This research report examines whether these technologies will
have an evolutionary or a revolutionary impact on traditional Business Intelligence implementations.
An in-memory analytic solution was developed for University of the Witwatersrand Procurement
Office, to evaluate the benefits claimed for the in-memory approach for Business intelligence, in the
development, reporting and analysis processes. A survey was used to collect data on the users'
experience when using an in-memory solution. The results indicate that the in-memory solution
offers a fast, flexible and visually rich user experience. However, there are certain key steps of the
traditional BI approach that cannot be omitted. The conclusion reached is that the in-memory
approach to Business Intelligence can co-exist with the traditional Business Intelligence approach, so
that the merits of both approaches can be leveraged to enhance value for an organisation
A Comparison of Query Execution Speeds for Large Amounts of Data Using Various DBMS Engines Executing on Selected RAM and CPU Configurations
In modern economies, most important business decisions are based on detailed analysis of available data. In order to obtain a rapid response from analytical tools, data should be pre-aggregated over dimensions that are of most interest to each business. Sometimes however, important decisions may require analysis of business data over seemingly less important dimensions which have not been pre-aggregated during the ETL process. On these occasions, the ad-hoc "online" aggregation is performed whose execution time is dependent on the overall DBMS performance. This paper describes how the performance of several commercial and non-commercial DBMSs was tested by running queries designed for data analysis using "ad-hoc" aggregations over large volumes of data. Each DBMS was installed on a separate virtual machine and was run on several computers, and two amounts of RAM memory were allocated for each test. Measurements of query execution times were recorded which demonstrated that, as expected, column-oriented databases out-performed classical row-oriented database systems
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