## 5 Conclusions  
This paper addresses the research question of how to leverage big data frameworks and tools to build a comprehensive platform that addresses the needs of novice users in the e-commerce domain in Saudi Arabia. In particular, this paper presented a big e-commerce data platform based on lambda architecture. The requirements of applications related to e-commerce data are discussed to determine the appropriate components for building an e-commerce platform. The platform was hosted on a cloud service considering the lambda architecture principles and design for performing batch and real-time processing of e-commerce applications. By utilizing batch and real-time processing frameworks, the proposed platform can handle enormous amounts of e-commerce data, collecting and storing it in a cloud data lake. This allows the storage of various e-commerce-related data, including customer information, transactions, social feeds, weather, and image and video data. Furthermore, for industries currently relying on data warehouses, the proposed cloud data lake enables the integration of such data warehouses, allowing the data to be loaded into data warehouses or consumed directly by analytics and business intelligence software and tools. Furthermore, businesses that wish to establish a data warehouse can obtain the required data from the data lake. Three analytical applications are implemented to validate the effectiveness of the big e-commerce data platform. The first application was to identify the periods in which customers prefer to place orders, whilst the second application was to verify the big e-commerce data platform is presented. In this application, a visualization to analyze the distribution of the locations where orders were placed is presented. The third application was to forecast time series data. We consider the case of forecast sales of stores and how it can be a better tool for the planning and management for e-commerce merchants. Such applications could provide insights that could potentially assist with numerous business aspects. Furthermore, viewing and acting on the insight results and findings promotes informed decisions that potentially benefit the e-commerce industry. Moreover, our current implementation of the proposed platform uses cloud-based storage services (e.g., Google Cloud Storage), which allows integration with other software systems via RESTful APIs and language-specific SDKs. The impact of the presented big e-commerce data platform extends the presented applications, and as additional e-commerce-related data become available, the platform becomes more proficient in performing further analytical applications. The limitations of the presented work can be summarized as: (1) chances of a \"data swamp\" increase as more data become available; however, proper organization of the data lake and including multiple Flume agents is helpful. (2) The data lake may be vulnerable to security threats, data breaches, and cyberattacks, unless it is safeguarded via modern technologies, as securing the platform is an ongoing process that is beyond the scope of this work. Future research could investigate the best security practices to implement and secure the platform as a whole. Also, analytical applications while including numerous data resources to further understand customer behavior patterns, enhance experience, and streamline various processes can be studied.  
Conceptualization, A.M., A.A. ([PERSON]) and T.M.Q.; writing--original draft, A.M., A.A. ([PERSON]) and T.M.Q.; writing--review and editing, A.M. and T.M.Q.; project administration, A.A. ([PERSON]); funding acquisition, A.A. ([PERSON]). All authors have read and agreed to the published version of the manuscript. This research was funded by Salla Research & Innovation Centre, Makkah, grant number [SRI222]. Data Availability Statement Data sharing is not applicable to this article. The authors declare no conflict of interest.