From Traditional Data Warehouse To Real Time Data Warehouse

Bouaziz, Senda; Nabli, Ahlem; Gargouri, Faïez

doi:10.1007/978-3-319-53480-0_46

Cited by 16 publications

(6 citation statements)

References 3 publications

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“…Typically, it consists of historical data taken from transactional data, but it can also include data from different sources. In addition, it is crucial to discern the difference between the traditional data warehouse and the real-time data warehouse [32]. To elaborate, the traditional data warehouse can provide data only on a monthly, weekly, or daily basis, but is not updated instantly, unlike the real-time data warehouse, which could be preferable since it is more accurate and can enable both tactical and strategic decisions.…”

Section: Data Warehousementioning

confidence: 99%

Data Management for Environmentally Sustainable and Profitable Business: Evidence from the Food Retail Industry

Dibsi,

Cho

2023

Sustainability

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Climatic changes are now a global reality with widespread repercussions for individuals and businesses worldwide. The responsibility to adapt behaviors and protect the environment is shared by all of humanity. Business leaders can play a pivotal role in expediting this transformation by shifting their focus from pure profit-seeking to becoming value-driven entities that actively contribute to societal betterment while championing environmental conservation and sustainable profitability. This transition, however, poses certain challenges, notably the higher costs associated with ecofriendly materials, which can strain a company’s financial equilibrium. Furthermore, a significant number of organizations overlook the untapped potential of their data, a valuable asset that could significantly bolster their competitive position. To address these challenges, this study aims to provide actionable guidance for business leaders by using a notable data framework to reassess their capabilities and dynamically adapt to external environments. Our method relies on data mining algorithms and business intelligence tools to report data. The results pinpoint which products are greatly in demand, so leaders can be assured their investment in redesigning products yields profit, improves product freshness, and eliminates waste. By bridging the gap between profitability and environmental responsibility, this study endeavors to empower businesses to thrive in a sustainable, value-driven future.

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Section: Data Warehousementioning

confidence: 99%

Data Management for Environmentally Sustainable and Profitable Business: Evidence from the Food Retail Industry

Dibsi,

Cho

2023

Sustainability

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“…An organization will experience difficulty in decision making when it encounters conflicting reports due to the lack of consistency of data from the various data sources used. Therefore we need an integrated data processing model that can process heterogeneous transactional data called data warehouse (DWH) [1][2] [3][4][5] [6] [7][8] [9][10] [11].…”

Section: Introductionmentioning

confidence: 99%

“…The current trend, an organization needs the latest information in decision making. The realtime data warehouse has different characteristics from the classic data warehouse [1][2] [5] [7] [11]. To achieve real-time data warehouse is very dependent on the process known as ETL (Extract, Transform, Loading) [1][2] [5] [7][10] [11] [12] [13] [14] [15][16] [17].…”

Section: Introductionmentioning

confidence: 99%

“…The realtime data warehouse has different characteristics from the classic data warehouse [1][2] [5] [7] [11]. To achieve real-time data warehouse is very dependent on the process known as ETL (Extract, Transform, Loading) [1][2] [5] [7][10] [11] [12] [13] [14] [15][16] [17]. There are several ETL approaches to realizing real-time data warehouse between processing data that only undergoes updating or known as the Change Data Capture (CDC) concept [1][2] [5][10] [11] [16] [18].…”

Section: Introductionmentioning

confidence: 99%

“…There is also research on making RTDW using the CDC Event-Driven Programming method [11]. Then there is research on the architecture of RTDW and how the process of making RTDW from the Traditional model [7]. The process of making RTDW is very much determined by the ETL process.…”

Section: Introductionmentioning

confidence: 99%

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Implementation of Data Backup and Synchronization Based on Identity Column Real Time Data Warehouse

Adnyana¹,

Sulastra²

2020

LKJITI

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Failure in the process of loading data from the Online Transactional Processing(OLTP) system to the Normalized Data Store (NDS) database can occur. This caused by a disruption in the network so that the OLTP system is unable to save data to the OLTP and NDS databases. Backup and synchronization data scenarios are needed to maintain data consistency and data availability. In this research, the process of data backup and synchronization is done by providing an identity column for the table in the OLTP database. An identity column is used to give status data, value '0' if the inserting process fails, and value '1' if successful. Data backup is done by storing temporary data into a CSV file format, then the CSV file is read, and an insert process is carried out into the OLTP database. After the insertion process into the OLTP database is successful, it continues with the synchronization process between the OLTP database and the NDS. Data synchronization between OLTP and NDS databases is done by checking the value of the Identity Column in each table in the OLTP database.

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