Where Is Actually Business Intelligence Advancement Workshop In Sql Web Server 2014

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Where Is Actually Business Intelligence Advancement Workshop In Sql Web Server 2014

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

(This article belongs to Special Edition Review Papers in Big Data, Cloud-based Data Analysis, and Learning Systems)

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Data is the lifeblood of any organization. In today’s world, organizations understand the vital role of data in modern business intelligence systems to make meaningful decisions and stay competitive in the field. Effective and optimal data analytics provide a competitive advantage to its performance and services. Large organizations create, collect and process huge amounts of data, which fall under the category of big data. Managing and analyzing the huge volume and diversity of a huge data set is a cumbersome process. At the same time, appropriate use of a wide range of enterprise information can generate useful insights into business practices. In this regard, two of the popular data management systems in the field of big data analytics (i.e. data warehouse and data lake) act as platforms for collecting big data that is generated and used by organizations. Although they are similar outwardly, they 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 definitions, characteristics and related workings of relevant data management frameworks. Furthermore, we explain the architecture and design considerations of the current state of the art. Finally, we provide a perspective on the 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. It entails examining big data and uncovering hidden patterns, correlations, etc. that are available in the data [1]. Big data analytics extracts and analyzes random data sets, and shapes them into useful information. According to statistics, the total volume of data generated in the world in 2021 was about 79 zettabytes, and this volume is expected to double by 2025 [2]. This unprecedented amount of data was the result of the data explosion over the past decade, with data interactions increasing by 5000% [3].

Big data deals with the volume, variety and speed of data processing and provides accuracy (insight) and value to the data. These are known as 5 vs Big Data [4]. An unprecedented amount of diverse data is captured, stored and processed with high data quality for various fields of application. These include business transactions, live broadcasts, social media, video analytics, text mining, and the creation of a huge amount of semi- or unstructured data to be stored in different information repositories [5]. Efficient integration and analysis of this multiple data across repositories is required to provide complete insight into the database. This is an interesting open research topic.

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Big data and related emerging technologies are changing the way e-commerce and e-services work and opening new horizons in business analytics and related research [6]. Big data analysis systems play a huge role in the field of modern enterprise management, from product distribution to sales and marketing, as well as analyzing hidden trends, similarities and other insights and allowing companies to analyze and improve their data to find new opportunities [7]. Because organizations with better and more accurate data can make informed business decisions by considering market trends and customer preferences, they can gain competitive advantages over others. Hence, organizations are investing heavily in artificial intelligence (AI) and big data technologies to strive towards digital transformation and data-driven decision making, ultimately leading to advanced business intelligence [6]. According to reports, it seems that the global big data analytics and business intelligence software applications markets will increase by $68 billion and $17.6 billion by 2024-2025 respectively [8].

Big data warehouses exist in many forms, depending on the requirements of companies [9]. An effective data warehouse needs to standardize, organize, evaluate and deploy a massive amount of data resources to improve query and analytics performance. Depending on the nature and application scenario, there are many different types of data warehouses apart from traditional relational databases. Two common 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 large collection of data for which the purpose has not been specified. ]. In detail, data warehouses store large amounts of data collected by various sources, usually using predefined schemas. Typically, a DW is a purpose-built relational database that runs on specialized hardware either on premises or in the cloud [13]. DWs have been widely used to store enterprise data and support business intelligence and analytics applications [14, 15, 16].

Data lakes (DLs) have emerged as massive data warehouses that store raw data and provide a rich list of functions with the help of metadata descriptions [10]. Although a DL is also a form of enterprise data warehousing, it does not inherently include the same analytics features commonly associated with data warehousing. Instead, they are repositories that store raw data in their original formats and provide a common access interface. From the lake, the data may 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, especially in online articles or blogs.

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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 conditions 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. Further, we standardize the concepts and provide a detailed analysis of various design aspects, different tools and facilities etc., along with the recent developments that have come into being.

The remainder of this paper is organized as follows. In Section 2, basic terms and definitions for big data analytics and data management plans are analyzed. Moreover, relevant works in this field are also summarized in this section. In Section 3, the architectures of both a data warehouse and a data lake are presented. Then, in Section 4, the main design aspects of DW and DL models along with their practical aspects are presented in detail. Section 5 summarizes the various common 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 different methods are critically discussed, and feedback is given. Finally, Section 8 concludes this survey paper.

Definitions and basic concepts of different data management schemes are provided in this section. Moreover, relevant works and review papers on this topic are also summarized.

With the great 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 is generated, known as “big data”. This data needs to be efficiently collected, managed and analyzed through big data processing. Big data processing aims at data mining (that is, extracting knowledge from large amounts of data), making use of data management, machine learning, high-performance computing, statistics, pattern recognition, and so on. Important characteristics of big data (defined as seven vs big data) (https://impact.com/marketing-intelligence/7-vs-big-data/, accessed September 25, 2022) are as follows:

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Usually, there are three basic types of big data processing possible: batch processing, streaming processing, and mixed processing [18]. in batch data processing

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