From Data Warehouses to Streaming Warehouses: A Survey on the Challenges for Real-Time Data Warehousing and Available Solutions
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paper_title: ETL Evolution for Real-Time Data Warehousing

paper_content:

Informed decision-making is required for competitive success in the new global marketplace, which is fraught with uncertainty and rapid technology changes. Decision makers must adjust operational processes, corporate strategies, and business models at lightning speed and must be able to leverage business intelligence instantly and take immediate action (Oxford Economics, 2011). Sound decisions are based on data that is analyzed according to well-defined criteria. Such data typically resides in a Database Warehouse for purposes of performing statistical and analytical processing efficiently. Data warehouses (DWH) are typically designed for efficient processing of read only analysis queries over large data, allowing only offline updates at night. The current trends of business globalization and online business activities available 24/7 means DWH must support the increasing demands for the latest versions of the data. Real-Time (or Active) Data Warehousing aims to meet the increasing demands of Business Intelligence for the latest versions of the data (Athanassoulis, et al., 2011). Achieving Real-Time Data Warehousing is highly dependent on the choice of a process in data warehousing technology known as Extract, Transform, and Load (ETL). This process involves: 1) Extracting data from outside sources; 2) Transforming it to fit operational needs; and 3) Loading it into the end target (database or data warehouse). Not all ETL’s are equal when it comes to quality and performance. As such, optimizing the ETL processes for real time decision making is becoming ever increasingly crucial to today's decision-making process. An effective ETL leads to effective business decisions and yields extraordinary decision-making outcomes. This study overviews the theory behind ETL and raises a research vision for its evolution, with the aim of improving the difficult but necessary data management work required for the development of advanced analytics and business intelligence.
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paper_title: An overview of business intelligence technology

paper_content:

BI technologies are essential to running today's businesses and this technology is going through sea changes.
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paper_title: An overview of business intelligence technology

paper_content:

BI technologies are essential to running today's businesses and this technology is going through sea changes.
---
paper_title: An overview of business intelligence technology

paper_content:

BI technologies are essential to running today's businesses and this technology is going through sea changes.
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Title: From Data Warehouses to Streaming Warehouses: A Survey on the Challenges for Real-Time Data Warehousing and Available Solutions

Section 1: INTRODUCTION
Description 1: Introduce the history and evolution of decision support systems and data warehousing, and present the growing need for real-time data processing.

Section 2: REAL-TIME DATA WAREHOUSING CHALLENGES
Description 2: Discuss the challenges faced by traditional data warehousing systems in achieving real-time data processing and analysis.

Section 3: Enabling Real-Time ETL
Description 3: Cover various techniques and approaches to modify existing ETL systems to support real-time or near real-time data loading.

Section 4: Enabling Real-Time Business Intelligence
Description 4: Explain methods and tools, such as Data Mining and OLAP, that enable the analysis of real-time data for immediate business intelligence.

Section 5: CONCLUSION
Description 5: Summarize the importance of real-time data warehousing and the trade-offs involved, emphasizing the need for further research and development in this field.