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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: 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 Issue Review Papers in Big Data, Cloud-Based Data Analytics and Learning Systems)
Data is the lifeblood of any organization. In today’s world, organizations recognize the important role of data in modern business intelligence systems to make meaningful decisions and to stay competitive in the field. Efficient and optimal data analysis provides a competitive advantage to its performance and services. Large organizations generate, collect and process large amounts of data, which falls under the category of big data. Managing and analyzing the sheer volume and variety of big data is a cumbersome process. At the same time, proper utilization of the vast collection of an organization’s information can generate meaningful insights into business tactics. In this regard, two of the popular data management systems in the field of big data analytics (i.e., data warehouse and data lake) serve as platforms to collect the big data generated and used by organizations. Although they are seemingly similar, both differ in terms of their properties 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, features and related works for the respective data management frameworks. Furthermore, we explain the architecture and design considerations of the current state of the art. Finally, we offer a perspective on the challenges and promising research directions for the future.
Big data; data warehouses; data lake; enterprise data management; OLAP; ETL tools; metadata; cloud computing; Internet of Things
Big data analytics is one of the buzzwords in today’s digital world. It involves examining big data and discovering the hidden patterns, correlations, etc. which is available in the data [1]. Big data analytics extracts and analyzes random data sets, and forms them into meaningful information. According to statistics, the total amount of data generated in the world in 2021 was about 79 zettabytes, and it is expected to double by 2025 [2]. This unprecedented amount of data was the result of a data explosion that occurred during the last decade, in which data interactions increased by 5000% [3].
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Big data deals with the volume, variety and speed of data to process and provide truth (insight) and value to 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 analytics, and text mining, which create a large amount of semi- or unstructured data that needs to be stored in different information silos [5]. The efficient integration and analysis of this multiple data across silos is necessary to reveal complete insight into the database. This is an open research topic of interest.
Big data and the related emerging technologies have changed the way e-commerce and e-services operate and have opened new frontiers in business analytics and related research [6]. Big data analysis systems play a major role in the modern enterprise management domain, from product distribution to sales and marketing, as well as analyzing hidden trends, similarities and other insights and allowing companies to analyze and optimize their data to find new opportunities [7 ]. As 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. Consequently, organizations are investing heavily in artificial intelligence (AI) and big data technologies to pursue digital transformation and data-driven decision-making, ultimately leading to advanced business intelligence [6]. According to reports, the global markets for big data analytics and business intelligence software applications are expected to increase by USD 68 billion and 17.6 billion, respectively, by 2024–2025 [8].
Big data repositories exist in many forms, according to the requirements of corporations [9]. An effective data warehouse must unify, regulate, evaluate and deploy a large amount of data resources to improve the analysis and query performance. Based on the nature and the application scenario, there are many different types of data repositories other than traditional relational databases. 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 treated for a specific purpose, while a data lake (DL) is a large pool of data for which the purpose is not defined [9 ] . In detail, data warehouses store large amounts of data collected from different sources, typically 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 for storing enterprise data and powering business intelligence and analytics applications [ 14 , 15 , 16 ].
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Data lakes (DLs) originated as large data repositories that store raw data and provide a rich list of functions using metadata descriptions [10]. Although the DL is also a form of enterprise data storage, it does not inherently contain the same analytical features commonly associated with data warehouses. Instead, they are repositories that store raw data in their native 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 features and use cases, there are fundamental differences in the data management philosophies, design characteristics, and ideal use cases for each of these technologies. In this context, we provide a detailed overview and differences between both the DW and DL data management schemes in this survey paper. Furthermore, we consolidate the concepts and give a detailed analysis of various design aspects, various tools and utilities, etc., along with recent developments that have come about.
The remainder of this paper is organized as follows. In Section 2, the terminology and basic definitions of big data analysis and the data management schemes are analyzed. Furthermore, the related works in the field are also summarized in this section. In Section 3, the architectures of both the data warehouse and data lake are presented. Next, in Section 4, the key design aspects of the DW and DL models are presented in detail along with their practical aspects. Section 5 summarizes the various popular tools and services available for enterprise data management. In Section 6 and Section 7 the open challenges and promising directions are explained respectively. In particular, the advantages and disadvantages of various methods are critically discussed, and the observations are presented. Finally, Section 8 concludes this survey paper.
The definitions and fundamental concepts 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, the 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 generated. It is required to efficiently collect, manage and analyze this data via big data processing. The process of big data processing is aimed at data mining (i.e. extracting knowledge from large amounts of data), utilizing data management, machine learning, high-performance computing, statistics, pattern recognition, etc. The important characteristics of big data (known). since the seven Vs of big data) (https://impact.com/marketing-intelligence/7-vs-big-data/, accessed 25 September 2022) are as follows:
Typically there are mainly three
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