International Journal on Science and Technology

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Demystifying Data Integration: Building Unified Analytics Pipelines for Seamless Decision-Making

Author(s) Shafeeq Ur Rahaman
Country India
Abstract The modern enterprise requires an integrated analytics pipeline to facilitate decision-making without barriers, given the ever-increasing complexity and volume of data. Advanced techniques regarding the integration of structured, semi-structured, and unstructured data sources in extended and efficient analytics pipelines are discussed in this paper. Subsequently, the contribution of integrated data to real-time insight development, operational efficiency, and data-driven decision-making across all organizational functions is discussed. The study has explored in detail the technologies and methodologies for building robust data pipelines using ETL, data lakes, and stream processing. Additionally, it has pointed out that the basic building blocks of effectiveness and reliability of such systems are data governance, security, and scalability. Real-time analytics is also identified as one of the critical enablers needed by enterprises to be able to adapt to fast-moving changes in market conditions, optimize processes, and drive innovation. This paper acts as a road map in depth for addressing data integration challenges and deploying solutions to derive real-time insights continuously for organizations that strive to be competitive in the ever-evolving environment where data assumes prime position
Keywords data integration, unified analytics, data pipelines, real-time insights, data governance, ETL, stream processing, data lakes, decision making, scalability, enterprise analytics, operational efficiency, data security, data-driven, real-time analytics, data sources
Field Computer > Data / Information
Published In Volume 14, Issue 1, January-March 2023
Published On 2023-01-04
Cite This Demystifying Data Integration: Building Unified Analytics Pipelines for Seamless Decision-Making - Shafeeq Ur Rahaman - IJSAT Volume 14, Issue 1, January-March 2023. DOI 10.5281/zenodo.14352971
DOI https://doi.org/10.5281/zenodo.14352971
Short DOI https://doi.org/g8t7rz
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