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Big Data Architecture for Large Organizations

Fathima Nuzla Ismail, Abira Sengupta, Shanika Amarasoma

TL;DR

With data volumes and heterogeneity accelerating in large organizations, the paper addresses the need for a scalable, governance-focused big data architecture. It proposes a cloud-agnostic, multi-layer blueprint spanning ingestion, transformation, storage, analytics, ML, and security, and integrates GenAI and low-code ML to accelerate value. The design is validated through a case study and supported by detailed cloud-environment analyses (Google Cloud, AWS, Azure), highlighting practical deployment patterns and governance mechanisms. The work offers a structured, adaptable blueprint that enables enterprises to treat data as a strategic asset across cloud platforms, while staying aligned with privacy, regulatory, and operational requirements.

Abstract

The exponential growth of big data has transformed how large organisations leverage information to drive innovation, optimise processes, and maintain competitive advantages. However, managing and extracting insights from vast, heterogeneous data sources requires a scalable, secure, and well-integrated big data architecture. This paper proposes a comprehensive big data framework that aligns with organisational objectives while ensuring flexibility, scalability, and governance. The architecture encompasses multiple layers, including data ingestion, transformation, storage, analytics, machine learning, and security, incorporating emerging technologies such as Generative AI (GenAI) and low-code machine learning. Cloud-based implementations across Google Cloud, AWS, and Microsoft Azure are analysed, highlighting their tools and capabilities. Additionally, this study explores advancements in big data architecture, including AI-driven automation, data mesh, and Data Ocean paradigms. By establishing a structured, adaptable framework, this research provides a foundational blueprint for large organisations to harness big data as a strategic asset effectively.

Big Data Architecture for Large Organizations

TL;DR

With data volumes and heterogeneity accelerating in large organizations, the paper addresses the need for a scalable, governance-focused big data architecture. It proposes a cloud-agnostic, multi-layer blueprint spanning ingestion, transformation, storage, analytics, ML, and security, and integrates GenAI and low-code ML to accelerate value. The design is validated through a case study and supported by detailed cloud-environment analyses (Google Cloud, AWS, Azure), highlighting practical deployment patterns and governance mechanisms. The work offers a structured, adaptable blueprint that enables enterprises to treat data as a strategic asset across cloud platforms, while staying aligned with privacy, regulatory, and operational requirements.

Abstract

The exponential growth of big data has transformed how large organisations leverage information to drive innovation, optimise processes, and maintain competitive advantages. However, managing and extracting insights from vast, heterogeneous data sources requires a scalable, secure, and well-integrated big data architecture. This paper proposes a comprehensive big data framework that aligns with organisational objectives while ensuring flexibility, scalability, and governance. The architecture encompasses multiple layers, including data ingestion, transformation, storage, analytics, machine learning, and security, incorporating emerging technologies such as Generative AI (GenAI) and low-code machine learning. Cloud-based implementations across Google Cloud, AWS, and Microsoft Azure are analysed, highlighting their tools and capabilities. Additionally, this study explores advancements in big data architecture, including AI-driven automation, data mesh, and Data Ocean paradigms. By establishing a structured, adaptable framework, this research provides a foundational blueprint for large organisations to harness big data as a strategic asset effectively.
Paper Structure (16 sections, 3 figures)

This paper contains 16 sections, 3 figures.

Table of Contents

  1. Introduction
  2. Methodology
  3. Requirements Analysis
  4. Architectural Design
  5. Validation and Case Study
  6. Assessment Framework
  7. Continuous Monitoring and Feedback Mechanism
  8. Results
  9. Google Cloud environment
  10. Google Cloud Documentation
  11. Cloud Service Analysis
  12. Amazon Web Services (AWS) environment
  13. Microsoft Azure environment
  14. Recent Advancement in Big Data Architecture
  15. Challenges and Future Work
  16. ...and 1 more sections

Figures (3)

  • Figure 1: High-Level Functional Diagram
  • Figure 2: Detailed Functional Diagram
  • Figure 3: In Data Mesh Architectures the individual functions(Marketing/Finance/etc) behave as separate domains having the ownership of their data. Data is considered as products and is distributed among the domains/owners. The simple premise of data mesh is that business domains should be able to define, access, and control their own data products.