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Sql Web Server Business Intelligence Advancement Workshop 2005 Download And Install
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Received: September 28, 2022 / Revised: October 28, 2022 / Accepted: November 2, 2022 / Published: November 7, 2022
(This article belongs to a separate overview paper on Big Data, Cloud Data Analytics and Learning Systems)
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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 stay competitive in the field. Efficient and optimal data analysis provides a competitive advantage for its performance and services. Large organizations generate, collect and process huge amounts of data, which fall into 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 use of an organization’s vast collection of information can create meaningful insight into business tactics. In this regard, two popular data management systems in the field of big data analytics (i.e. data warehouse and data lake) act as platforms for the accumulation of big data generated and used by organizations. Although seemingly similar, both differ in their features and applications. This article presents 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. Furthermore, we explain the architecture 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 storage; easy data; 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. This entails examining big data and discovering hidden patterns, correlations, etc. available in the data [1]. Big data analytics extracts and analyzes random data sets, forming them into meaningful information. According to statistics, the total amount of data generated in the world in 2021 was approximately 79 zettabytes, and it is expected to double by 2025 [2]. This unprecedented amount of data is a result of the data explosion that has occurred over the last decade, in which data interaction has increased by 5000% [3].
Big data deals with the volume, variety and speed of data to process and provides truth (insight) and value to the data. These are known as 5 V of big data [4]. An unprecedented amount of diverse data is collected, 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, creating a vast amount of semi- or unstructured data that is stored in various information silos [5]. Effective integration and analysis of these multiple silos of data is required to reveal complete insights into the database. This is an open research topic of interest.
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Big data and related new technologies are changing the way e-commerce and e-services work and opening new frontiers in business analytics and related research [6]. Big data analysis systems play a major role in the modern domain of business management, from product distribution to sales and marketing, as well as the analysis of hidden trends, similarities and other insights, and enable 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 a competitive advantage over others. Therefore, 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 big data analytics and business intelligence software application markets look set to grow by USD 68 billion and USD 17.6 billion by 2024-2025 [8].
Big data warehouses exist in many forms, according to the demands of corporations [9]. An effective data warehouse needs to aggregate, curate, evaluate, and deploy massive amounts of data resources to improve analytics 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 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 processed for a specific purpose, while a data lake (DL) is a huge collection of data for which the purpose is not 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 business data and power business intelligence and analytics applications [14, 15, 16].
Data lakes (DL) have emerged as large 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 warehousing, it does not inherently include the same analytical features typically 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 the 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, especially in online articles or blogs.
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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 use cases for each of these technologies. In this context, we provide a detailed overview and differences between DW and DL data management schemes in this survey paper. Furthermore, we consolidate the concepts and provide a detailed analysis of various design aspects, various tools and utilities, etc., along with the recent developments that have occurred.
The remainder of this paper is organized as follows. Section 2 analyzes the terminology and basic definitions of big data analytics and data management schemes. Furthermore, related works in the field are summarized in this section. In Section 3, architectures and data warehouses and data lakes are presented. Then, 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 open challenges and promising directions are explained. In particular, the advantages and disadvantages of different methods are critically discussed, and observations are made. Finally, Section 8 concludes this survey paper.
Definitions and fundamental concepts of various data management schemes are given in this section. Furthermore, related papers and review papers on this topic are summarized.
With significant advances in technology, there has been an unprecedented use of computer networks, multimedia, Internet of Things, social media, 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 efficiently through big data processing. The process of big data processing is focused on data collection (i.e. extracting knowledge from large amounts of data), using 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:
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Typically, three types of big data processing are mainly possible: batch processing, stream processing, and hybrid processing [18]. In batch processing, data
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