Establishing Robust Data Foundations: Early-Stage Architecture for Scalable Data Warehousing and Analytics Systems
DOI:
https://doi.org/10.15662/IJEETR.2021.0303003Keywords:
Data Warehousing, Scalable Architecture, Analytics Systems, Data Governance, ETL Pipelines, Metadata Management, Dimensional ModelingAbstract
The era of data-driven decision making has put organizations in the need of data infrastructures that are robust and scalable and these can be used to meet the long-term analytic and business intelligence needs. This research paper explains why it is important to develop a sound architectural design during the early stages of data warehouse development. The article points out that storage capacity or processing capability will not define the effectiveness of more sophisticated analytics systems but the quality, integration, governance, and scalability of underlying data structures.
This paper presents a framework that is built based on seven main components that comprise data sources integration, data ingestion pipelines, data quality management, metadata governance, dimensional model, scalable storage architecture, and analytics enablement. Together, these features offer a methodical procedure of transforming raw and unstructured data into trusted and analysis ready information resources. The article cites that decisions made early on in architecture establish the degree of flexibility, performance, and cost efficiency in the future, particularly in organizations that are fast expanding and have use of large volumes and high velocity data.
The research further asserts that a good early architecture reduces redundancy, increases homogeneity and contributes towards a smooth transition into cloud-based warehousing, real-time analytics and machine learning applications. With the application of a framework-based approach, organizations will be capable of creating sustainable analytics ecosystems by aligning technical infrastructure to strategic business goals. The paper ends with the idea that building solid data bases during early stages is important in realizing scalability, reliability, and analytical maturity in the modern business environments.
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