## 1 Introduction
Given the popularity of cryptocurrencies, there is a vast number of recent studies and projects focused on analyzing data from social media and cryptocurrencies in real time utilizing novel data processing tools and methodologies. [PERSON] et al. [19] proposed a distributed architectural design for handling large volumes of data from Twitter and Bitcoin transactions in real time to predict price fluctuations; by means of a combined machine learning and lexicon approach, they determined the sentiments of the tweets and related them with the price of Bitcoin to predict the next minute's price. [PERSON] [20] utilized a lambda architecture to process and visualize real-time data regarding cryptocurrencies' prices. On the other hand, [PERSON] et al. [21] proposed an architecture for real-time cryptocurrency data processing and analysis based on the lambda architectural approach to obtain insights through the relation of different data sources such as social media, cryptocurrencies, and the stock market. A kappa architecture was proposed by [PERSON] and [PERSON] [18] to process real-time data from Twitter to visualize analytics, such as trends and tweet volume. In addition, a relation between tweets and cryptocurrencies' prices was studied by [PERSON] et al. [22] as a way to predict the direction of the price variation, from which it was found that the volume of tweets is more significant than their sentiments. It was also found by [PERSON] and [PERSON] [23] that the volume of tweets correlates with Bitcoin prices. [PERSON] et al. [24] found that an increase in Bitcoin's price led to a higher number of tweets which again would drive the price further up [25]. Some other studies focused only on the relation between tweets and cryptocurrencies, leaving in the second term the methods involved in the management and processing of data. [PERSON] et al. [26] found that there is a causal relationship between the uncertainty associated with sentiments in social media and cryptocurrency returns. In addition, we have found evidence of works that aim to identify behavioral patterns regarding cryptocurrencies by means of clustering algorithms. [PERSON] et al. [27] applied a \(k\)-means clustering approach to identify suspicious transactions of Ethereum. [PERSON] et al. [28] identified four types of cryptocurrency users through the application of \(k\)-means clustering and support vector machines (SVMs) on Bitcoin and Ethereum transactional data. [PERSON] et al. (2019) used \(k\)-means to classify positive and negative publications from Twitter related to Bitcoin. The previous research serves as a reference and basis for our study; although similar approaches have been proposed, to the best of our knowledge there is no evidence of related papers that utilize an iterative kappa architecture to process, relate and manage data from Twitter and cryptocurrency markets in real time. In this context, this study proposes the application of a novel kappa architecture, derived from the lambda architecture, for processing and analyzing real-time data from Twitter and the cryptocurrency market. It integrates a temporary batch step which allows the relation of data from different data sources in a specific time span. The proposed architecture focuses on the processing of data in real time while looking for insights and patterns regarding the number of tweets, their sentiment, and the number, type, and volume of cryptocurrency transactions. Data are collected through application programming interfaces (APIs) and streamed to be processed and stored in a document-oriented No-SQL database (MongoDBTM). Afterward, data are related with the purpose of finding meaningful patterns. The present work aims to demonstrate the use and benefits of the proposed architecture as a choice for relating data from cryptocurrencies and social media while identifying patterns in real time; for that purpose, data from a defined period of time are used. This paper is organized as follows: Section 2 describes in detail the materials and methods used for the study's development.