Modernizing Data Replication in the Cloud

Customer Context

A large automotive organization was constrained by its legacy on-premises data replication systems. These outdated systems incurred significant operational and licensing costs while impairing overall efficiency. Furthermore, the restrictive nature of the infrastructure limited data access for analytics teams and prevented the company from adopting modern, flexible data solutions, creating a major bottleneck for data-driven innovation.

The Challenge

Legacy replication systems have long been a bottleneck for a large automotive organization seeking agility in data management. The customer faced two major hurdles.

Maintaining and licensing the existing on-premises replication systems required significant resources, leading to increased operational expenses and impaired efficiency.
The restrictive nature of these legacy systems limited access to data for analytics use cases, as they were locked into specific technologies, thereby preventing the company from adopting modern, flexible data solutions.

The 3 Key Initiatives

1. Cloud Migration of Legacy Replication Systems

By leveraging AWS technologies such as S3, DMS, Glue, EMR, Iceberg, Athena, and Lakeformation, the customer successfully transitioned its replication infrastructure from on-premises to a scalable and cost-effective cloud environment.

2. Enabling Multi-Format Data Consumption

With multiple output ports, the system now supports batch and near-real-time analytics use cases, ensuring that various teams across the organization can access the data they need without technological limitations.

3. Integrating Snowflake and Dremio

These technologies were incorporated to enhance data accessibility and analytical performance, allowing for efficient querying and transformation of large datasets.

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ACCELERATED DECISION-MAKING

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COST SAVING

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DATA QUALITY IMPROVEMENT

Accelerated Decision-Making

Data-driven organizations are three times more likely to report significant improvements in decision-making speed, helping them to respond faster to market changes

(Source: HARVARD BUSINESS SCHOOL)

Cost Saving

Data Platforms can allow companies to realize cost savings of up to 15% through minimized redundancies, optimized resource utilization and streamlined processes.

(Source: McKinsey&Company)

Data Quality Improvement

Companies focusing on structured data management can improve data accuracy and consistency by 10-20% through centralized data platforms

(Source: McKinsey&Company)

Cost Saving

Our approach resulted in lower storage, data integration costs and data transaction costs. This reduction in expenses has enhanced the organization's financial efficiency and resource allocation.

Efficiency

We achieve streamlined Data Management processes and improved Governance by implementing structured guidelines and technical solutions. This led to smoother operations and better utilization of resources across the organization.

Stakeholders Confidence

Demonstrable improvements in Data Management increased stakeholder trust. This support was crucial for securing ongoing investments and resources for future Data Management initiatives.

Real-World Impact and Benefits

The project resulted in some key benefits, cascading from a strong data platform foundation

Operational Area	Before Modernization	After Modernization
Data Infrastructure	Constrained by legacy on-premises replication systems that created bottlenecks.	A modern, scalable cloud-based replication solution on AWS (CloudLake).
Cost & Efficiency	High operational expenses and licensing overhead impaired overall efficiency.	Significantly reduced maintenance and licensing costs, redirecting resources to strategic initiatives.
Data Accessibility	Restrictive, closed ecosystem limited data access for analytics and locked teams into specific technologies.	An open, adaptable environment with multi-format data consumption for various analytics use cases.
Time to Market	Delays in deploying new data-driven applications due to system limitations and integration challenges.	Drastically improved time to market for new services, powered by a flexible and accessible architecture.
Analytics & Agility	Analytics teams were hindered by restrictive data pipelines and lack of real-time availability.	Empowered analytics teams with real-time insights, allowing the company to respond quickly to market changes.

Conclusions

The transition from legacy replication systems eliminated the constraints of outdated on-premises infrastructure, significantly reduced maintenance costs and licensing overhead, redirecting valuable resources toward strategic initiatives rather than system upkeep.

The once-restrictive data pipelines have been replaced with an architecture that prioritizes accessibility, flexibility, and real-time availability, empowering analytics teams with the insights they need, when they need them. This transformation has drastically improved time to market, as new data-driven applications and services can now be deployed without the delays caused by system limitations or integration challenges.

Instead of being locked into a closed ecosystem, the organization now operates in an open, adaptable data environment. Data can be leveraged more efficiently across business functions, allowing the company to respond quickly to market changes, customer demands, and competitive pressures.