---
title: "Data sources Configurations"
slug: "data-sources-configurations-12-1"
status: "new"
updated: 2026-02-27T08:21:50Z
published: 2026-03-19T14:20:45Z
canonical: "docs.bazefield.com/data-sources-configurations-12-1"
---

> ## Documentation Index
> Fetch the complete documentation index at: https://docs.bazefield.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Data sources Configurations

**For more high level documentation see** [**Data Sources**](/product/docs/data-sources-1-1)

## **Overview**

A **Datasource** defines how data is accessed, structured, and shared across multiple applications within the platform — such as **Dashboards** and **Reports**. It acts as a reusable configuration that encapsulates one or more **data inputs** (e.g., time series, statistics, attributes) and exposes them to different visualization or analysis contexts.

Datasources enable users to:

- **Share consistent data definitions** across multiple tools.
- **Simplify configuration management** by centralizing data logic.
- **Support multiple data types**, such as time series points, availability statistics, and object attributes.

Each **Datasource** includes the following key parameters:

| Parameter | Description | Multiplicity |
| --- | --- | --- |
| **Numerical Attributes** | Numerical Metadata or object properties describing the asset (e.g., rated power, site name). | Many |
| **Asset Type** | Defines the type of physical or logical asset associated with the datasource (e.g., turbine, inverter). | 1 |
| **Points / Statistics** | Data sources providing numerical or categorical information, such as time series, real-time values, or aggregated statistics. | Many |
| **Calculations** | Derived metrics computed from raw data or attributes (e.g., Active Power / Rated Power). | Many |

## **Datasource Types**

Datasources can encapsulate different **data source types**, depending on the nature of the data and use case:

### 1. **Point**

Represents a time series or real-time measurement. Examples:

- **Time series:** 10-07-25 → 2100 kWh, 11-07-25 → 1400 kWh
- **Real time:** 10-07-25 09:17 → 321 kW
- **Aggregated period:** 10-07-25 to 14-07-25 → 5600 kWh

Typical use cases:

- Monitoring energy production, temperature, or wind speed over time.
- Computing period-based aggregations or derived KPIs.

For configuration documentation see [Point Datasource](/product/docs/point-1)

---

### 2. **Availability Statistics**

Represents operational availability data or loss-related metrics. Examples:

- **TBA (Technical Based Availability):** 92.5 %
- **Lost Production:** 23.2 MWh

Typical use cases:

- Measuring equipment uptime.
- Quantifying production losses.
- Supporting availability-based performance KPIs.

For configuration documentation see [Availability Statistics Data Source](/product/docs/availability-statistics-1)

---

### 3. **Asset Attributes**

Represents static or slowly changing asset information. Examples:

- **Rated Power:** 2000 kW
- **Site Name:** Wind Turbine 2

Typical use cases:

- Enriching dashboards and reports with contextual metadata.
- Providing reference values for calculations (e.g., normalization).

For configuration documentation see [Asset Attribute Datasource](/product/docs/asset-attribute-1)

## General Datasource Architecture

![](https://cdn.document360.io/a0786c0f-b171-4b24-8860-c32eb7ef2738/Images/Documentation/image(455).png?sv=2026-02-06&spr=https&st=2026-11-06T20%3A57%3A56Z&se=2026-11-06T21%3A57%3A56Z&sr=c&sp=r&sig=AQqGNlStCSfnzw3d2qvrrE%2BSFnEDbocVz1EschIZNHs%3D)

![](https://cdn.document360.io/a0786c0f-b171-4b24-8860-c32eb7ef2738/Images/Documentation/image(456).png?sv=2026-02-06&spr=https&st=2026-11-06T20%3A57%3A56Z&se=2026-11-06T21%3A57%3A56Z&sr=c&sp=r&sig=AQqGNlStCSfnzw3d2qvrrE%2BSFnEDbocVz1EschIZNHs%3D)

## **Key Benefits**

- **Centralized Data Configuration:** Define once, reuse everywhere.
- **Flexible Data Composition:** Combine attributes, points, and calculations.
- **Multi-Application Sharing:** Seamless integration with dashboards and reports.
- **Consistency and Traceability:** Ensure unified data logic across the platform.