Infosys TechCompass

Architecture and design

Trend 1: Serverless-first development enables autonomous team velocity

Serverless computing represents a fundamental shift in how enterprises approach application development and operational responsibility. Moving beyond infrastructure management, teams can now focus exclusively on business logic while cloud providers handle provisioning, scaling, and maintenance. The architectural paradigm abstracts away the complexity of server configuration and supports organizations to iterate faster, with 64% of enterprises reporting development cycle reductions between 35% and 40%, following serverless adoption. Deployment frequency increases dramatically by 71% on average, allowing teams to deliver features and fixes to production multiple times per day without manual infrastructure provisioning or scaling concerns.

A manufacturing enterprise that builds agricultural devices modernized its operational data pipeline using a serverless architecture, with support from Infosys. The organization implemented real-time machine telemetry processing using AWS Lambda to extract, transform, and load data from controller area network (CAN bus) interfaces into Amazon Kinesis data streams. This enabled real-time analysis, filtering, and transformation of device telemetry, with insights stored in data lakes for analytics. By eliminating infrastructure management, the serverless approach improved operational efficiency. Industry studies show that serverless data pipelines can increase deployment frequency by more than three times and reduce infrastructure costs by about 35%.

Architecture and design

Trend 2: Data mesh architecture unleashes federated data governance and organizational autonomy

Data mesh represents a paradigm shift from centralized data engineering and governance models toward domain-driven, decentralized data ownership where business domains take responsibility for their data as products. This architectural transformation addresses the critical bottleneck of centralized data teams by empowering distributed domain teams to autonomously curate, enrich, and publish data products while maintaining compliance with enterprise governance standards. Organizations implementing data mesh achieve a 30% reduction in data latency by eliminating handoffs between central data teams and domain consumers, accelerating time to insights and enabling data-driven decision-making at the speed of business.

A global financial institution, in collaboration with Infosys, implemented a data mesh architecture to address master data management (MDM) latency challenges on legacy relational systems. The organization migrated its transactional MDM application to a high-performance distributed database, significantly reducing the latency of master data availability across the enterprise. Infosys implemented a cognitive data mapper solution leveraging machine learning (ML)-based techniques to automatically identify source-to-target attribute mapping, delivering 82% automation in value-based mapping and 60% automation in name-based mapping. This modernization enabled the bank to accelerate its enterprise data migration journey while establishing federated governance where autonomous domain teams could manage their data products independently within centralized policy frameworks, fundamentally transforming the organization’s data culture from a bottleneck-driven model to an autonomous, self-service ecosystem.