In-Memory Data processing using Redis Database

Kaur, Gurpreet; Kaur, Jatinder

doi:10.5120/ijca2018916589

Cited by 3 publications

(2 citation statements)

References 8 publications

(13 reference statements)

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“…As a speed layer database, Apache Cassandra [30], Redis [39], Apache HBase [40], MongoDB [41] could be utilized. These databases can handle real-time data ingestion, as well as random read and write operations.…”

Section: Common Technologies For Lambda Architecture Layersmentioning

confidence: 99%

Performance Analysis of Lambda Architecture-Based Big-Data Systems on Air/Ground Surveillance Application with ADS-B Data

Demirezen,

Navruz

2023

Sensors

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This study introduces a novel methodology designed to assess the accuracy of data processing in the Lambda Architecture (LA), an advanced big-data framework qualified for processing streaming (data in motion) and batch (data at rest) data. Distinct from prior studies that have focused on hardware performance and scalability evaluations, our research uniquely targets the intricate aspects of data-processing accuracy within the various layers of LA. The salient contribution of this study lies in its empirical approach. For the first time, we provide empirical evidence that validates previously theoretical assertions about LA, which have remained largely unexamined due to LA’s intricate design. Our methodology encompasses the evaluation of prospective technologies across all levels of LA, the examination of layer-specific design limitations, and the implementation of a uniform software development framework across multiple layers. Specifically, our methodology employs a unique set of metrics, including data latency and processing accuracy under various conditions, which serve as critical indicators of LA’s accurate data-processing performance. Our findings compellingly illustrate LA’s “eventual consistency”. Despite potential transient inconsistencies during real-time processing in the Speed Layer (SL), the system ultimately converges to deliver precise and reliable results, as informed by the comprehensive computations of the Batch Layer (BL). This empirical validation not only confirms but also quantifies the claims posited by previous theoretical discourse, with our results indicating a 100% accuracy rate under various severe data-ingestion scenarios. We applied this methodology in a practical case study involving air/ground surveillance, a domain where data accuracy is paramount. This application demonstrates the effectiveness of the methodology using real-world data-intake scenarios, therefore distinguishing this study from hardware-centric evaluations. This study not only contributes to the existing body of knowledge on LA but also addresses a significant literature gap. By offering a novel, empirically supported methodology for testing LA, a methodology with potential applicability to other big-data architectures, this study sets a precedent for future research in this area, advancing beyond previous work that lacked empirical validation.

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Section: Common Technologies For Lambda Architecture Layersmentioning

confidence: 99%

Performance Analysis of Lambda Architecture-Based Big-Data Systems on Air/Ground Surveillance Application with ADS-B Data

Demirezen,

Navruz

2023

Sensors

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“…NoSQL, in its practice, is an efficient choice for simplicity, high work analytics, distributed scalability, and good adaptability, which certainly makes the process of storing and retrieving data easier [15]. Furthermore, the performance of query execution speed and the use of NoSQL database storage using MongoDB and Redis has been researched to be better than RDBMS with the percentage of processing time in the range of nanoseconds or milliseconds [16], [17]. RDBMS and NoSQL have their respective advantages based on the type of data that needs to be used.…”

Section: Introductionmentioning

confidence: 99%

Data storage model in low-cost mobile applications

Sukarsa

Antara

Buana

et al. 2022

IJEECS

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<span lang="EN-US">Mobile applications that have data transactions between users require a database relational database management system (RDBMS) and RESTful API operating on the hosting service so that all users can access the data. Renting a hosting service is not cheap and creating a RESTful API takes plenty of time. As an alternative to hosting, a free version of the Cloud Firestore service gives full access rights to the database and has an application programming interface (API) to manage data or access data. However, the free version of Cloud Firestore has limitations in terms of storage capacity, read, write, and delete processes. Therefore, redesigning process of the database was carried out into a low-cost version of the database model consisting of SQLite database and a low-cost version of the NoSQL database to overcome this problem. The goal is to reduce storage space usage and read, write, and delete processing on Cloud Firestore. The low-cost version of the database was tested with 6,030 data. The results obtained were savings of 47.27% storage usage, 83.08% write usage, 91.26% read process usage, and 83.19% delete process usage compared to the test results of the relational database model.</span>

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Paradigm Shift From Monolithic to Microservices

Saxena,

Bhowmik

2023

2023 IEEE International Conference on Recent Advances in Systems Science and Engineering (RASSE)

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In-Memory Data processing using Redis Database

Cited by 3 publications

References 8 publications

Performance Analysis of Lambda Architecture-Based Big-Data Systems on Air/Ground Surveillance Application with ADS-B Data

Performance Analysis of Lambda Architecture-Based Big-Data Systems on Air/Ground Surveillance Application with ADS-B Data

Data storage model in low-cost mobile applications

Paradigm Shift From Monolithic to Microservices

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