> For the complete documentation index, see [llms.txt](https://knowledge.aterio.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://knowledge.aterio.io/general-faqs/data-delivery-and-integration-faqs.md). # Data Delivery and Integration FAQs ### What data delivery options are available? Aterio can deliver data through: * AWS S3 * Google Cloud Storage * Snowflake secure shares * SFTP * APIs * Direct CSV or Parquet files The appropriate delivery method depends on the customer’s infrastructure, preferred file format, update frequency, and internal data-ingestion process. ### Does Aterio provide a customer download portal? Yes. Aterio provides a secure customer download portal where authorized users can access and download subscribed datasets. ### What file formats are supported? Aterio commonly supports CSV, Parquet, and JSON. CSV is suitable for manual analysis and broad compatibility. Parquet is recommended for larger datasets and database workflows because it is compressed and preserves data types. JSON is generally used for API responses and application integrations. Available formats may vary by product and delivery method. ### How frequently is the data updated? Update frequency depends on the product: * US Data Centers: generally updated daily * US Power Generation: generally updated daily or as material changes are identified * Industrial Developments and Early Signals: updates may be delivered hourly * Balancing Authority Energy Demand: updated daily * Population and Housing Forecasts: generally updated monthly Delivery schedules can also be configured based on customer requirements. ### How are AWS S3 deliveries structured? Cloud-storage deliveries can include full dataset snapshots, incremental updates, or both. Files are typically organized using consistent folders, table names, dates, and file-naming conventions. A delivery may contain separate folders or files for inventory, events, reference tables, and historical snapshots. The final structure is agreed during implementation to align with the customer’s ingestion workflow. ### How does Snowflake delivery work? Aterio can provide data through a Snowflake secure share. Customers can access the shared tables directly from their own Snowflake environment without requiring Aterio to send separate files. Customers may query the shared data directly or copy it into their internal databases, depending on their own Snowflake architecture and permissions. ### Does Aterio support API access? Yes. Aterio can provide API access for supported products and use cases. API responses are generally provided in JSON and may support filters, pagination, date parameters, project identifiers, or other query options. Authentication is handled through secure API credentials or tokens. ### Are full snapshots updates available? Yes. Depending on the delivery arrangement, Aterio can provide complete snapshots. A full snapshot contains the latest available version of every included record. Customers that need a simple replacement workflow may prefer full snapshots, while customers maintaining a data warehouse may prefer incremental updates. ### What is the difference between the Inventory and Events tables? The Inventory table contains the latest available record for each project, campus, or building. The Events table records material changes over time. Events may include: * Project announcements * Construction starts * Activation dates * Cancellations or withdrawals * Construction milestones The Events table allows customers to analyze project history and pipeline movement rather than only viewing the current project status. ### How far back does historical data go? Historical coverage varies by product and field. For the Data Centers product, Aterio maintains historical activation information for older operational facilities and detailed daily change history from the point when systematic historical tracking began. Customers should review the relevant product data dictionary because historical availability may differ between construction stages, activation dates, capacity values, and event types. ### How are schema changes communicated? Aterio communicates material schema changes before implementation whenever reasonably possible. Notifications may include the field name, data type, description, effective date, and whether the change is additive or may affect an existing integration. New fields are generally added in a way that minimizes disruption. Customers should build ingestion processes that tolerate additional columns where possible. ### Is a data dictionary available? Yes. Aterio provides data dictionaries for its datasets. The dictionary describes field names, definitions, formats, units, expected values, and relevant methodology. Product-specific documentation may also explain project stages, estimated fields, event types, power calculations, and date logic. ### How is the delivered data secured? Aterio uses the security controls available through each delivery channel. These may include cloud identity and access management, encrypted storage and transmission, Snowflake secure shares, API tokens, restricted SFTP access, one-time-password protection, role-based access, and customer-specific permissions. Access is limited to authorized users and can be removed or modified when requested. ### Can delivery be customized for a customer’s infrastructure? Yes. Aterio can work with customers to configure file format, delivery channel, folder structure, naming conventions, table selection, delivery frequency, and full-versus-incremental update logic. The objective is to align delivery with the customer’s existing database, cloud, analytics, or ingestion environment while maintaining a consistent underlying data model. ### How can the Data Center Inventory and Behind-the-Meter datasets be joined? The Data Center Inventory and Behind-the-Meter datasets can be joined using `ATERIO_POWER_GENERATION_PROJECT_UID`. Because the Behind-the-Meter dataset is structured at the phase level—project, plant, and phase—a direct join may create multiple rows for the same data center record when a project includes several phases. To avoid duplication, first aggregate the Behind-the-Meter data to one row per `ATERIO_POWER_GENERATION_PROJECT_UID`, either by pivoting phases into separate columns or consolidating them into project-level fields. When aggregating: * Sum `TOT_PHASE_NAMEPLATE_POWER_MW` across all phases to calculate the project’s total nameplate capacity. * Do not sum `TOT_CONTRACTED_POWER_CAPACITY_MW`, as this field is already reported at the project level. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. 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