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Enterprise PaaS: How it Works

Solution Overview

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Enterprise PaaS Architecture

Continuous Transformation and Process Optimization

47Lining Enterprise PaaS gives industrial enterprises a platform to drive continuous transformation and process optimization. The architecture below demonstrates how this solution works.

Diagram DataOps Web Services Architecture

Once 47Lining Enterprise PaaS is deployed to your AWS account, data is ingested from external systems, like IoT sensors, on-premises storage environments, and third-party providers. This data is then automatically curated to establish context and ensure it is meaningful and valuable for search, subsetting, and analysis.

Users can provision machine learning and analytics environments on demand from the workbench and experiment with their preferred analytics toolsets. During this process, asset schemas and models help users easily understand the assets being modeled. Once experimentation is complete, analytics and machine learning algorithms can be deployed directly into production from the workbench.

47Lining offers a managed portfolio to track assets throughout this process. Additionally, the catalog captures all data providers, datasets, transformations, delivery targets and lineage relationships. These capabilities combine to simplify governance of your optimization portfolio, making it easy to push algorithms into production.

Experiment Frequently, Innovate Often

47Lining Helps Eliminate Silos in Process-Oriented Data

Diagram lays out components of the 47Lining Enterprise PaaS solution's architecture, including management console and deployed portfolio

One of the primary drivers for adoption of Enterprise PaaS is to separate industrial data from process optimization applications. Doing so eliminates silos and gives teams greater ownership over data in order to experiment and innovate with less friction.

Enterprise PaaS fulfills this initiative by offering an API-accessible data platform that turns data resources into first-class enterprise assets:

  • Deliver data resources to, or directly consume them by any application or workflow
  • Ensure data resources have strong, consistent base treatment and behaviors with domain-specific extensions that empower users to add new micro- or nano-apps and workflows

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