Tweet Toplama, Analiz ve Depolama için Platform Tasarımı (TweetCASP)

Year 2023, , 165 - 171, 18.10.2023

Tugba Beril Doguc , Ahmet Arif Aydın

https://doi.org/10.53070/bbd.1344271

Abstract

Bu büyük veri çağında, büyük miktarda verinin akışı, depolanması ve analizi çeşitli zorluklar ortaya çıkarır. Yararlı bilgilere ulaşmak için yoğun veri kullanan sistemlerin tasarımcıları tarafından çeşitli zorluklar ele alınmalıdır. Verilerin toplanması, saklanması ve analiz edilmesi, anlamlı öngörüler elde etmek için uygun veri işleme ve analitik teknolojilerinden oluşan bir toplama ve analiz platformu gerektirir. Bu çalışmada, Twitter'ın Streaming API'sini kullanarak kullanıcı tarafından girilen anahtar kelimelere dayalı olarak tweet'leri toplayan, akış verileri üzerinde gerçek zamanlı analitik için bir ortam sağlayan ve gelecekteki toplu iş odaklı görevleri yerine getirmek için verileri bir Apache Cassandra NoSQL veri deposunda kalıcı olarak depolayan TweetCASP (Tweet toplama, analiz ve depolama platformu) sistemini sunuyoruz. Ayrıca, TweetCASP, yazılım geliştiriciler, tasarımcılar ve bu alandaki araştırmacılar için veri toplama ve analiz için kullanılan veri yoğunluklu bir sistem örneği sunar.

Keywords

Büyük veri, veri yoğunluklu sistemler, gerçek zamanlı analiz, akış analitiği, NoSQL.

Designing a platform for Tweet Collection, Analytics and Storage (TweetCASP)

Abstract

In this era of big data, the streaming, storage, and analysis of large amounts of data present a variety of challenges. Several challenges must be addressed by designers of data-intensive systems in order to retrieve useful information. Collecting, storing, and analyzing data requires a collection and analytics platform comprised of an appropriate choice of data processing and analytics technologies in order to acquire meaningful insight. In this paper, we report on TweetCASP (Tweet Collection, Analytics and Storage Platfrom), which gathers tweets based on user-entered keywords using Twitter's Streaming API, providing an environment for real-time analytics on streaming data and permanently storing data in an Apache Cassandra NoSQL datastore to fulfill future batch-oriented data processing requirements. Moreover, The TweetCASP presents an example of a data-intensive system used by software developers, designers, and researchers for data collecting and analytics.

Keywords

Big data, data-intensive systems, real-time analytics, streaming analytics, NoSQL.

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