Accelerating Analytics With a Hybrid Cloud Foundation

To support its digital strategy, a leading financial services organization needed faster, more cost-effective access to analytical data. Myridius designed and operationalized a hybrid environment integrating on-premise systems with AWS, including a piloted cloud data lake, enabling near-unlimited scalability, pay-for-use cost efficiency, and stronger readiness for timely, data-driven decisions.

Key Outcomes

• Near-unlimited scalability through on-demand cloud resources.
• Improved cost effectiveness with a pay-for-use model.
• Stronger analytical readiness for timely decision-making.

Overview

To support its digital strategy, a leading financial services organization needed faster access to analytical data within a seamless, cost-effective cloud environment. The status quo limited agility and scalability, and the desired state was a hybrid model integrating on-premise systems with cloud services to support efficient, timely, data-driven decisions. Myridius designed and operationalized a hybrid environment integrating on-premise infrastructure with AWS, evaluated AWS components, piloted a cloud data lake to centralize storage, and operationalized AWS for data workloads on a secure, production-ready foundation. As a result, the organization enabled near-unlimited scalability through on-demand resources, improved cost effectiveness with a pay-for-use model, and strengthened analytical capabilities with streamlined workflows, real-time integration, and greater readiness for timely decision-making.

Client Context

The client is a leading financial services organization pursuing a digital strategy that depends on fast, reliable access to analytical data.

A hybrid cloud foundation mattered here because the existing model limited agility and scalability, slowing the data-driven decisions the strategy required. What was at stake was the organization's ability to scale analytics efficiently and cost-effectively while integrating established on-premise systems with modern cloud services.

The Challenge

The organization needed faster access to analytical data within a seamless, cost-effective cloud environment. The status quo limited agility and scalability, and the desired state was a hybrid model that could integrate on-premise systems with cloud services and support efficient, timely, data-driven decision-making.

Consider an analytics team waiting on data. Access was too slow to support a seamless, scalable strategy, on-premise systems were not integrated with cloud services, and there was no centralized, elastic foundation for advanced analytics. The result was constrained agility and limited readiness for timely decisions.

Transformation Goals

The engagement focused on north stars that connected faster analytical access to a scalable hybrid environment and a secure, production-ready foundation.

• Accelerate Data Access: Speed up access to analytical data in support of the organization's digital strategy.

• Seamless Hybrid Scalability: Build a seamless, scalable hybrid environment integrating on-premise systems with AWS.

• Secure Production Foundation: Establish a secure, production-ready cloud foundation for cost-effective data workloads and advanced analytics.

The Solution

The engagement designed and operationalized a hybrid environment integrating on-premise infrastructure with AWS to strengthen scalability, streamline data access, and improve analytical readiness. Myridius orchestrated the hybrid architecture, embedded a centralized data lake and analytics components, and reimagined data operations as an elastic, production-ready capability. The progression moved from deploying the hybrid architecture, to embedding a piloted data lake and AWS components, to reimagining data workloads on a secure, scalable foundation.

• Orchestrated the foundation: Architected a hybrid environment to enable seamless data flow and operational efficiency across on-premise and AWS environments.

• Embedded intelligence into the journey: Evaluated AWS components and piloted a cloud data lake to centralize storage and support advanced analytics.

• Reimagined the operating model: Operationalized AWS for data workloads and built a secure, production-ready foundation aligned to evolving business demand.

Governance and Trust

Because this engagement handled analytical data for a financial services organization, security and a production-ready posture were central. The AWS foundation was built as secure and production-ready, aligned to evolving business demand, ensuring that scalability did not come at the expense of control.

A centralized cloud data lake with Delta Lake supported data integrity and consistency across analytical workloads, while real-time integration through StreamSets and managed processing on Databricks gave the organization streamlined, governed data workflows rather than ad hoc movement of data between systems.

Results

The engagement transformed slow, constrained analytical access into an elastic, cost-effective hybrid foundation. The result was scalability, cost efficiency, and stronger decision readiness.

The result:

• Enabled near-unlimited scalability through on-demand cloud resources aligned to changing business needs.

• Improved cost effectiveness with a pay-for-use consumption model for cloud services.

• Strengthened analytical capabilities with streamlined workflows, real-time integration, and greater decision readiness.

Technology Stack

Infrastructure and Cloud

AWS
Provides the elastic, production-ready foundation

Data Platform

Databricks
Processes and manages analytical data workloads

Analytical Platform

Amazon SageMaker
Supports advanced analytics and machine learning

Data Integrity

Delta Lake
Ensures consistency and reliability in the data lake

Data Integration

StreamSets
Enables real-time data integration across environments

For a financial services organization, slow analytical access is a brake on a digital strategy. This case shows how a hybrid cloud foundation turns data into a timely, cost-effective decision advantage. This was not a lift-and-shift. It was a hybrid, production-ready data foundation built for scalable, cost-effective analytics.