AWS, Cloud Computing

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The Power of Data Warehousing and Business Intelligence on VMware Cloud with AWS


In today’s data-driven world, organizations thrive on insights derived from data warehousing and business intelligence (BI) solutions. The synergy between VMware Cloud on AWS and AWS services presents an unparalleled opportunity to build a robust, scalable, cost-effective data ecosystem. In this blog post, we delve into a comprehensive reference architecture that harnesses the potential of VMware Cloud on AWS and AWS services to empower businesses with data-driven decision-making capabilities.

The Architecture in Action

At the core of this architecture lies the seamless integration between VMware Cloud and AWS services, enabling a fluid data flow from on-premises sources to insightful visualizations. Let’s walk through the key components and steps:


  • VMware Cloud Account: Establishing a Software Defined Data Center (SDDC) on AWS forms the foundation of this architecture. The SDDC furnishes a secure, isolated environment for VMware workloads, ensuring reliability and performance.
  • Customer-owned AWS Account: Leveraging a customer-owned AWS account, organizations provision essential AWS services vital for data warehousing and BI. These services encompass Amazon S3 for storage, Amazon Redshift for data warehousing, Amazon QuickSight for BI, and AWS Direct Connect for seamless connectivity.
  • Data Collection and Storage: The journey commences with data collection from on-premises sources, which is then stored in Amazon S3. This pivotal step ensures data accessibility and durability, allowing further processing and analysis.
  • Data Migration with AWS DMS: AWS Data Migration Service (DMS) comes into play to seamlessly migrate data from Amazon S3 to Amazon Redshift. This streamlined process ensures data integrity and minimizes downtime, facilitating a smooth transition to the data warehousing environment.
  • Data Warehousing and Analysis: Amazon Redshift is the cornerstone for storing and analyzing data. Its scalable and high-performance architecture empowers organizations to derive actionable insights swiftly, unlocking the true potential of their data assets.
  • BI Visualization with Amazon QuickSight: Amazon QuickSight takes center stage in transforming data into compelling visualizations. Its intuitive interface empowers users to explore data dynamically, facilitating informed decision-making across the organization.
  • User Accessibility: Users gain access to insightful visualizations through a web browser or mobile device, enabling seamless collaboration and knowledge dissemination across teams.

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This reference architecture offers a plethora of benefits, making it a game-changer for organizations venturing into data warehousing and BI on VMware Cloud on AWS:

  • Scalability: The architecture’s inherent scalability allows organizations to adapt to evolving business needs effortlessly, ensuring sustained growth and performance.
  • Security: With data stored in highly secure AWS services like Amazon S3, Amazon Redshift, and Amazon QuickSight, organizations can rest assured about data confidentiality and integrity, adhering to the highest security standards.
  • Cost-effectiveness: By leveraging only the necessary AWS services on a pay-as-you-go basis, organizations optimize costs while maximizing the value derived from their data assets, ensuring a favorable return on investment.

Expanding on Cost Efficiency

Cost efficiency is critical to any architecture, and integrating VMware Cloud on AWS with AWS services offers significant cost-saving opportunities. Here’s how:

  • Pay-as-You-Go Model: With AWS services, organizations only pay for what they use, eliminating the need for upfront investments in hardware and infrastructure. This flexible pricing model ensures cost efficiency by aligning expenses with actual usage.
  • Resource Optimization: The scalability of AWS services allows organizations to optimize resource utilization based on demand. Whether scaling compute resources in Amazon Redshift or adjusting storage capacity in Amazon S3, businesses can optimize costs by provisioning resources as needed.
  • Managed Services: AWS offers a suite of managed services, reducing the operational overhead of maintaining and managing infrastructure. By leveraging managed services like Amazon Redshift and Amazon QuickSight, organizations can lower operational costs while focusing on core business activities.

Expanding on Scalability

Scalability is essential for accommodating growing data volumes and evolving business needs. The architecture’s scalability manifests in several ways:

  • Elastic Compute and Storage: AWS services like Amazon Redshift and Amazon S3 offer elastic compute and storage capabilities, allowing organizations to scale resources up or down based on demand. This elasticity ensures that the architecture can seamlessly handle fluctuations in data volumes and workload requirements.
  • Auto-scaling: With auto-scaling features in AWS services, organizations can automate resource provisioning and scaling based on predefined policies. This dynamic scaling ensures optimal performance and resource utilization, even during peak periods.
  • Global Reach: AWS’s global infrastructure enables organizations to deploy resources in multiple regions, ensuring low latency and high availability. This global reach enhances scalability by allowing organizations to expand their footprint and reach new markets without compromising performance.

Expanding on Robustness

A robust architecture is essential for ensuring the reliability, availability, and performance of data warehousing and BI solutions. The architecture’s robustness is evident in the following aspects:

  • High Availability: AWS services are designed for high availability, with built-in redundancy and failover mechanisms. This ensures that data warehousing and BI applications remain accessible and operational, even during hardware failures or infrastructure disruptions.
  • Data Durability: AWS services like Amazon S3 offer high durability for stored data, with multiple copies stored across different availability zones. This redundancy ensures data integrity and resilience against data loss or corruption.
  • Disaster Recovery: AWS provides disaster recovery solutions that enable organizations to replicate data and applications across multiple regions for business continuity. This ensures that data warehousing and BI operations can quickly recover from disasters or outages, minimizing downtime and ensuring uninterrupted operations.


The integration of VMware Cloud on AWS with AWS services offers a cost-efficient, scalable, and robust architecture for data warehousing and business intelligence.

By leveraging the inherent capabilities of AWS services, organizations can unlock the full potential of their data assets while minimizing costs and maximizing performance. Embrace the power of data warehousing and business intelligence on VMware Cloud with AWS and embark on a journey of limitless possibilities.

Drop a query if you have any questions regarding VMware Cloud and we will get back to you quickly.

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1. Can this architecture accommodate large-scale data volumes?

ANS: – Absolutely! With the scalability of Amazon Redshift and Amazon S3, this architecture seamlessly handles large-scale data volumes, ensuring uninterrupted performance and reliability.

2. Is data migration from on-premises sources to AWS complex?

ANS: – Not at all! AWS Data Migration Service (DMS) simplifies the data migration process, ensuring a smooth transition with minimal downtime and data loss.

3. How customizable is this architecture to meet specific business requirements?

ANS: – This architecture serves as a flexible blueprint that can be tailored to meet the unique needs of each organization. Whether fine-tuning data storage options or customizing visualization dashboards, the architecture offers ample room for customization.

WRITTEN BY Bineet Singh Kushwah

Bineet Singh Kushwah works as Associate Architect at CloudThat. His work revolves around data engineering, analytics, and machine learning projects. He is passionate about providing analytical solutions for business problems and deriving insights to enhance productivity. In a quest to learn and work with recent technologies, he spends the most time on upcoming data science trends and services in cloud platforms and keeps up with the advancements.



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