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Question: 1 A DWE control flow is executed as: A. A ZIP file B. A binary executable C. An XML file D. A WebSphere process Answer: D Question: 2 Which of the following this is not platform compatible server for DB2 Alphabet? A. Weblogic Application Server BEA B. IBM WebSphere Application Server C. Microsoft .Net Framework D. Jboss Application Server Answer: C Question: 3 How can DB2 Alphablox be deployed on a DWE solution any? A. On the same server as DWE B. As a separate instance C. a or b D. None of the above Answer: C Question: 4 OLAP objects include: A. Block Model B Dimensions C. a and b D. MQT’s Answer: C Question: 5 Which statement is true about DB2 Warehouse Edition? A. Simplify enterprise data warehouse development, deployment, and maintenance with a comprehensive, integrated solution B. Streamline business intelligence processes and enhance flexibility with components powerful, analytical solution C. Enhance data warehouse performance and improve productivity with DB2’s powerful optimization features 9 D. All of the above Answer: D Question: 6 Materialized Query What kind of data do Tables (MQTs) contain? Page 1/9

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

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(This article is under the Special Issue Assessment of 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 data plays in modern business intelligence systems to make meaningful decisions and stay competitive in the field. Efficient and optimal data analysis gives a competitive edge to its performance and services. Large organizations create, collect, and process huge amounts of data, which falls under the category of big data. Managing and analyzing large volumes and many types of big data is a cumbersome process. At the same time, the proper use of an organization’s vast collection of information can produce meaningful insights into business tactics. In this regard, two of the popular data management systems in the big data analytics sector (i.e. data warehouses and data lakes) serve as platforms for big data accumulation due to created and used by the organization. Although they appear to be similar, both are different in their characteristics 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, characteristics and associated work for the respective data management frameworks. Furthermore, we explain 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 warehouse; 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 entails examining big data and uncovering 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 is about 79 zettabytes and this number is expected to double by 2025 [2]. This unprecedented amount of data is the result of the data explosion that has occurred over the past decade, during which data interactions have increased by 5000% [3].

The Evolution Of Storage Devices

Big data takes the volume, variety, and speed of data to process and provides authenticity (insights) and value to the data. These are called the 5 Vs of big data [4]. An unprecedented amount of diverse data is collected, stored and processed with high data quality for different application domains. These include business transactions, real-time streaming, social media, video analytics, and text mining, which generate large amounts of semi- or unstructured data stored in databases. different information silos [5]. It is necessary to efficiently integrate and analyze this much data across silos to reveal complete database insights. This is an open research topic of interest.

Big data and its related emerging technologies have been changing the way e-commerce and e-services work, and opening new frontiers in business analytics and research. related [6]. Big data analytics systems play a big role in the field of modern business management, from product distribution to sales and marketing, as well as analyzing hidden trends, similarities, and problems. other topic.

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