Multicloud View

Multicloud Functionality in Lighthouse Lighthouse was designed for multicloud environments from the ground up. This means the platform allows you to centralize, compare, and correlate data from different cloud providers (such as AWS, Azure, and GCP) within a single analytical structure—without requiring parallel integrations or external tools. This functionality is automatically activated when multiple clouds are associated with the same company. The data becomes global and is harmonized through the generated datasets, making it usable by any workspace, regardless of the data source (cloud provider). This enables users to create unified views, such as:

• Consolidated costs by project, even if part of it runs on AWS and part on Azure.

• Comparative analyses of performance or efficiency across different clouds.

• Executive indicators that aggregate multicloud KPIs into single dashboards.

As the client’s FinOps maturity evolves, the use of multicloud environments becomes common—and Lighthouse supports this growth with native structure, performance, and scalability.

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Creating Datasets in Lighthouse Creating datasets in Lighthouse allows any validated workspace to be transformed into a structured, lightweight, and high-performance source for subsequent analyses. The process begins after data validation: simply select the desired workspace, click on More Actions, and choose Generate Dataset. The platform then creates an optimized table based on the columns used in that workspace’s views. This means the dataset will contain only the fields visible in the workspace—a way to ensure performance without losing context.

Editing Once created, the dataset can be edited with:

• Additional data from different providers;

• Complementary datasets (such as those from CCA or Anomalies);

• External CSV files (e.g., budget data, financial targets, mappings).

This flexibility is essential to address more complex cases, such as:

• Intelligent rightsizing in CCA, which depends on cross-referencing billing and technical metrics;

• Anomaly analyses, which combine multiple sources to understand behavioral deviations;

• Budget vs. Actual scenarios, where CSV files containing financial data are integrated with billing.