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Data Ingestion

Data ingestion in Cloudaware FinOps is responsible for bringing detailed cost and usage records from each cloud into Cloudaware, validating them, and making them ready for allocation, reporting, and optimization. This section explains how billing data flows into Cloudaware, how it connects to Billing Integrations, and how to keep the data healthy over time.

Use these guides together:

How Data Ingestion Fits Into Cloudaware FinOps

  • Cloud billing exports are configured using the guides in Billing Integrations.

  • Cloudaware parses large, detailed billing files (for example, AWS CUR/Data Exports, Azure EA/MCA, GCP Billing Export) and normalizes them into a unified cost model.

  • Cost and usage lines are enriched with CMDB data (accounts, projects, subscriptions, workloads, tags) so they can be allocated to business units, applications, and teams.

  • Historical billing data is preserved even if a cloud account or subscription is later removed from the cloud integration.

Setup Workflow

  1. Review prerequisites.

    • Confirm that the relevant cloud integrations (AWS, Azure, GCP, and others) are configured in Cloudaware with read‑only access.

    • Ensure you have the necessary permissions in each cloud portal to create or modify billing exports.

  2. Configure billing exports:

    • For AWS, configure Cost & Usage Reports or Data Exports (and legacy DBR if still required).

    • For Azure, configure MCA/EA or other supported exports.

    • For Google Cloud, enable Billing Export to BigQuery and/or file exports.

    • For Oracle Cloud, configure billing exports.

    • For Kubernetes, enable cost attribution using OpenCost.

  3. Verify ingestion.

    • After setting up billing integrations, confirm new data is arriving and that object and cost totals look reasonable.

    • Check FinOps dashboards or sample reports to ensure spend is visible across providers and accounts.

  4. Monitor health.

    • Establish a lightweight routine (for example, weekly) to review data freshness and coverage using the guidance in Data Health & Freshness.

    • Investigate any gaps or anomalies before relying on the data for budgeting, anomaly detection, or optimization planning.

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