Web Server Business Intelligence Advancement Workshop

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Web Server Business Intelligence Advancement Workshop

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By Athira Nambiar Athira Nambiar Scilit Preprints.org Google Scholar * and Divyansh Mundra Divyansh Mundra Scilit Preprints.org Google Scholar

Received: September 28, 2022 / Revised: October 28, 2022 / Accepted: November 2, 2022 / Published: November 7, 2022

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(This article belongs to the review papers of Special Issues in Big Data, Cloud-Based Data Analytics, and Learning Systems)

Data is the lifeblood of any organization. In today’s world, organizations recognize the vital role of data in modern business intelligence systems to make meaningful decisions and remain competitive in the field. Efficient and optimal data analysis gives a competitive edge to its performance and services. Major organizations generate, collect and process large amounts of data, which falls under the category of big data. Managing and analyzing the volume and variety of big data is a cumbersome process. At the same time, appropriate use of an organization’s vast collection of information can generate meaningful insights into business tactics. In this regard, two of the popular data management systems in the area of ​​big data analytics (i.e., data warehouse and data lake) act as platforms to accumulate the big data generated and used by organizations. Although seemingly similar, both differ in terms of their features and applications. This article provides a detailed overview of the roles of data warehouses and data lakes in modern enterprise data management. We detail the definitions, characteristics, and related work for the respective data management frameworks. In addition, we explain the architectural and design considerations of the current state of the art. Finally, we provide insight into challenges and promising research directions for the future.

Big Data; data storage; data lake; enterprise data management; OLAP; ETL tools; metadata; cloud computing; the Internet of Things

Big data analytics is one of the buzzwords in today’s digital world. This involves examining big data and discovering hidden patterns, correlations, etc. available in data [1]. Big data analytics extracts and analyzes random data sets, turning them into meaningful information. According to statistics, the total amount of data generated in the world in 2021 was about 79 zettabytes, and this is expected to double by 2025 [2]. This unprecedented amount of data was the result of a data explosion that occurred in the last decade, where data interactions increased by 5000% [3].

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Big Data deals with the volume, variety and velocity of data to process and provides veracity (insight) and value to the data. These are known as the 5 Vs of big data [4]. An unprecedented amount of diverse data is acquired, stored and processed with high data quality for various application areas. These include commercial transactions, real-time streaming, social media, video analytics and text mining, creating a huge amount of semi- or unstructured data to be stored in different silos of information [5]. Effective integration and analysis of this multiple data across silos is required to divulge a complete insight into the database. This is an open research topic of interest.

Big Data and its related emerging technologies have changed the way e-commerce and e-services work and opened new frontiers in business analysis and related research [6]. Big data analytics systems play an important role in modern business management, from product distribution to sales and marketing, and by analyzing hidden trends, similarities and other insights, and enabling companies to analyze and optimize their data to find new opportunities [7]. ]. Because organizations with better and more accurate data can make informed business decisions by looking at market trends and customer preferences, they can gain competitive advantages over others. Therefore, organizations are investing heavily in artificial intelligence (AI) and big data technologies to drive towards digital transformation and data-driven decision-making, which ultimately leads to advanced business intelligence [6]. According to reports, the global big data analytics and business intelligence software markets are expected to grow by USD 68 billion and USD 17.6 billion respectively by 2024–2025 [8].

Big data archives exist in many forms according to the requirements of corporations [9]. An effective data warehouse must unify, curate, evaluate and deploy a huge amount of data resources to improve analysis and query performance. Based on the nature and scenario of the application, there are many different types of data stores other than traditional relational databases. Two of the popular data warehouses among them are enterprise data warehouses and data lakes [10, 11, 12].

A data warehouse (DW) is a data warehouse that stores structured, filtered and processed data that has been processed for a specific purpose, while a data lake (DL) is a vast group of data for which the purpose is not is defined [9] ]. In detail, data warehouses store large amounts of data collected from various sources, usually using predefined schemas. Typically, a DW is a purpose-built relational database running on specialized hardware, either on premises or in the cloud [13]. DWs have been widely used to store enterprise data and power business intelligence and analytics applications [14, 15, 16].

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Data lakes (DLs) have emerged as big data repositories that store raw data and provide a rich list of functionalities with the help of metadata descriptions [10]. Although DL is also a form of enterprise data storage, it does not inherently include the same analysis features commonly associated with data warehouses. Instead, they are repositories that store raw data in their original formats and provide a common access interface. From the lake, data can flow downstream to a DW to be processed, packaged and ready for consumption. As a relatively new concept, there has been very limited research discussing various aspects of data lakes, particularly in Internet articles or blogs.

Although data warehouses and data lakes share some overlapping characteristics and use cases, there are fundamental differences in the data management philosophies, design features, and ideal usage conditions for each of these technologies. In this context, we provide a detailed overview and differences between DW and DL data management schemes in this study. In addition, we reinforce the concepts and provide a detailed analysis of various design aspects, various tools and utilities, etc., along with the recent developments that have emerged.

The remainder of this paper is organized as follows. In Section 2, the terminology and basic definitions of big data analytics and data management schemes are reviewed. Moreover, related works in the field are also summarized in this section. In Section 3, the architectures of both the data warehouse and the data lake are presented. Next, in Section 4, the key design aspects of the DW and DL models along with their practical aspects are presented in detail. Section 5 summarizes the various popular tools and services available for enterprise data management. In Section 6 and Section 7, respectively, open challenges and promising directions are explained. In particular, the advantages and disadvantages of different methods are critically discussed and observations are presented. Finally, Section 8 concludes this study.

Definitions and fundamentals of various data management schemes are provided in this section. In addition, related papers and review papers on the topic are also summarized.

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With significant advances in technology, there has been unprecedented use of computer networks, multimedia, the Internet of Things, social networks, and cloud computing [17]. As a result, a huge amount of data, known as “big data”, has been generated. It is necessary to collect, manage and analyze this data effectively through big data processing. The process of big data processing is about data mining (that is, extracting knowledge from large amounts of data), leveraging data management, machine learning, high-performance computing, statistics, pattern recognition, etc. The important characteristics of big data (known as the seven Vs of big data) (https://impact.com/marketing-intelligence/7-vs-big-data/, accessed 25 September 2022) are as follows:

Usually there are mainly three

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