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Serverless Data Warehousing & Predictive Analytics

Cloud strategies, because of their unparalleled scale and flexibility in deployment, are rapidly getting entrenched in the digital journeys of organizations globally. As monolithic legacy architectures fail to provide data monetization opportunities & agility to processes, there is a growing need to have a hybrid architecture leveraging the best of both – legacy systems and cloud computing – for amplified outcomes.

Serverless computing is offered by cloud providers, where the provider dynamically manages the allocation of infrastructure, and pricing is based on the actual amount of resources consumed, rather than the pre-purchased capacity. As enterprises address the volume & velocity of big data, it is important for them to have flexible infrastructure capacity to address any anticipated / unanticipated spike in the transaction volume. This will enable enterprises to focus on business decision-making, rather than sizing the underlying IT infrastructure.


LTI’s Serverless Data Warehousing & Predictive Analytics framework helps organizations strategize & implement serverless (infinite scale & collaborative) DWH & AI solutions. By applying the insights gathered from huge quantities of data on cloud, our clients are able to accelerate business decision-making. The framework includes three major key essentials:

  • Consulting advisory services, providing a blueprint of Data Warehousing & Predictive Analytics framework strategy, and assessment roadmap.
  • Implementation services, detailing out the steps to address integration points, build serverless data management process, frame a cloud-native data model, and foster a collaborative data science workbench.
  • Cognitive Ops, supporting agile delivery & CI/CD, to help enterprises with productivity improvement and persona-based interventions, for improved business outcomes.


  • Unlimited scale in data processing & analytics
  • Reduction in ops, with the Cloud Native data model
  • Saving on infrastructure expenses

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