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Business Intelligence Designer Workshop Download And Install

The feature paper represents the most advanced research with great potential for high impact in the field. A Feature Paper should be a large original Article that involves several techniques or approaches, provides insights for future research directions and describes possible research applications.

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Feature papers are submitted upon individual invitation or recommendation by the scientific editor and must receive positive feedback from reviewers.

Editors’ Choice articles are based on recommendations by scientific editors of journals from around the world. The editors select a small number of recently published articles in journals that they believe will be of particular interest to readers, or important in their respective research areas. The aim is to provide an overview of some of the most interesting works published in various research areas of the journal.

By Athira Nambiar Athira Nambiar Scilit Google Scholar * and Divyansh Mundra Divyansh Mundra Scilit Google Scholar

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

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(This article belongs to the Special Issues Research Paper in Big Data, Cloud-Based Data Analysis and Learning Systems)

Data is the lifeblood of any organization. In today’s world, organizations recognize the critical 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 service. Major organizations generate, collect and process large amounts of data, including in the big data category. Managing and analyzing the volume and diversity of big data is a cumbersome process. At the same time, the proper use of a large collection of organizational information can generate meaningful insights into business tactics. In this regard, two of the popular data management systems in the field of big data analysis (ie, data warehouses and data lakes) act as platforms for collecting big data generated and used by organizations. Although they look similar, the two differ in terms of features and applications. This article presents a detailed overview of the role of data warehouses and data lakes in modern enterprise data management. We detail the definitions, features and related work for each data management framework. Furthermore, we describe the architectural and design considerations of the current state of the art. Finally, we provide a perspective on challenges and promising research directions for the future.

Big data; data warehousing; data lake; enterprise data management; OLAP; ETL tools; metadata; cloud computing; Internet of Things

Big data analysis is one of the buzzwords in today’s digital world. It requires examining big data and uncovering hidden patterns, correlations, etc. found in the data [1]. Big data analytics extracts and analyzes random data sets, shaping them into meaningful information. According to statistics, the total amount of data generated in the world in 2021 is about 79 zettabytes, and this is expected to double by 2025 [2]. The unprecedented amount of data is a result of the data explosion that occurred over the past 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 provide truth (insight) and value to the data. This is 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 domains. These include business transactions, real-time streaming, social media, video analysis, and text mining, creating large amounts of partial or unstructured data to be stored in different information silos [5]. Efficient integration and analysis of these various data across silos is required to reveal complete insights into the database. This is an open research topic of interest.

Big data and related emerging technologies have changed the way e-commerce and e-services operate and have opened new frontiers in business analysis and related research [6]. Big data analytics systems play a big role in the domain of modern enterprise management, from product distribution to sales and marketing, and analyze hidden trends, similarities and other insights and allow companies to analyze and optimize their data to find new opportunities [7 ]. As organizations with better and accurate data can make informed business decisions by looking at market trends and customer preferences, they can gain a competitive advantage over others. Therefore, organizations are investing heavily in artificial intelligence (AI) and big data technologies to work towards digital transformation and data-based decision making, which ultimately leads to advanced business intelligence [6]. According to the report, the market for big data analytics applications and business intelligence software worldwide seems to increase by USD 68 billion and 17.6 billion by 2024–2025, respectively [8].

Big data repositories exist in various forms, according to the needs of companies [9]. An effective data repository needs to consolidate, curate, evaluate and use a large number of data sources to improve analytical and query performance. Based on the nature and scenario of the application, there are different types of data repositories apart from the traditional relational database. Two of the popular data repositories among them are enterprise data warehouses and data lakes [10, 11, 12].

A data warehouse (DW) is a data repository that stores structured, filtered and processed data that has been treated for a specific purpose, while a data lake (DL) is a vast pool of data whose purpose is undefined [9]. ]. In detail, a data warehouse stores large amounts of data collected by different sources, usually using a predefined schema. Typically, a DW is a purpose-built relational database that runs on dedicated hardware either on-premise or in the cloud [13]. DW has been widely used to store enterprise data and drive business intelligence and analytics applications [14, 15, 16].

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Data lakes (DL) have emerged as big data repositories that store raw data and provide a rich functional list with the help of metadata descriptions [10]. Although DL is also a form of enterprise data storage, it does not include the same analytical features typically associated with data warehouses. Instead, they are repositories that store raw data in its original format and provide a common access interface. From the lake, data may flow downstream to the DW to be processed, packaged and ready for use. As a relatively new concept, there is very limited research discussing various aspects of data lakes, mainly in Internet articles or blogs.

Although data warehouses and data lakes share some overlapping features and use cases, there are fundamental differences in data management philosophies, design features, and ideal use cases for each of these technologies. In this context, we provide a detailed overview and differences between both DW and DL data management schemes in this survey paper. Furthermore, we consolidate concepts and provide detailed analysis of different design aspects, various tools and utilities, etc., along with the latest developments that have come into being.

The remainder of this paper is organized as follows. In Part 2, the terminology and basic definitions of big data analysis and data management schemes are analyzed. Furthermore, related works in the field are also summarized in this section. In Section 3, the architecture of both the data warehouse and the data lake is presented. Next, in Section 4, the main design aspects of the DW and DL models along with their practical aspects are presented at length. Section 5 summarizes the various popular tools and services available for enterprise data management. In Section 6 and Section 7, open challenges and promising directions are explained, respectively. In particular, the pros and cons of various methods are critically discussed, and observations are presented. Finally, Section 8 concludes this survey paper.

Basic definitions and notions of various data management schemes are provided in this section. Furthermore, related works and review papers on this topic are also summarized.

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With significant advances in technology, unprecedented use of computer networks, multimedia, Internet of Things, social media, and cloud computing has occurred [17]. As a result, a large amount of data, known as “big data”, has been produced. It is necessary to efficiently collect, manage and analyze this data through big data processing. Big data processing processes are aimed at 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) (, accessed on September 25, 2022) are as follows:

Usually, there are mainly three

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