Working with Insight IDs

Insight IDs are unique numeric identifiers assigned to each Insight available on the Carbon Arc Platform. They act as the primary reference for accessing structured views of data, including metrics, KPIs, and time series. Each ID corresponds to a specific analytical lens within a given dataset — for example:

626 — Card Spend in Facteus
379 — Clickstream Web Traffic in Datos
385 — POS Spend in PDI Technologies

Insight IDs are foundational to building queries and retrieving data through Carbon Arc’s Python SDK or UI. Below are a few important highlights about Insight IDs:

Insight IDs are globally unique.
Insight types include: metric, event, kpi, marketshare, and cohort.
Use get_insights() with filters like subject_ids, entity_id, or insight_types to explore the catalog.

Whether you’re conducting exploratory analysis, building dashboards, or integrating into workflows, Insight IDs provide a consistent way to reference and pull data from the Carbon Arc platform.

Download a list of Insight IDs

Use the button below to download a static CSV of Insight IDs and their mapped metrics for easier reference.

Download CSV

Migrating APIs

Users transitioning from the 1.0 Platform to Carbon Arc’s 2.0 Platform can use this guide to adapt and update their existing code. Users starting off in the 2.0 Platform should leverage Tutorials or the Platform UI.

Download CSV

Retrieving Insight Metadata

To discover available insight IDs, use the OntologyAPIClient to search by subject, topic, or representation:

from carbonarc import CarbonArcClient

client = CarbonArcClient(token="YOUR_TOKEN")

# Get all insights for a given subject ID
insights = client.ontology.get_insights_for_subject(subject_id=INSERT)

# Preview available insight IDs and labels
for insight in insights.get("data", []):
    print(insight["insight_id"], insight["insight_label"])

You can also fetch insight definitions directly:

# Get detailed information for a single insight
insight = client.ontology.get_insight_information(insight_id=385)
print(insight["insight_label"], insight["description"])

Estimating Cost and Checking Filters

Before purchasing a Framework, you can inspect available filters and price:

# Check price
price = client.explorer.check_framework_price(framework)
print("Price:", price)

# List applicable filters for this insight/entity combo
filters = client.explorer.collect_framework_filters(framework)

Purchasing and Retrieving Data

Once the framework is validated, you can buy and retrieve data:

# Buy framework
client.explorer.buy_frameworks(framework)

# Get the data
df = client.explorer.get_framework_data(
    framework_id="your_framework_id",
    data_type="dataframe"
)
print(df.head())

Walkthrough Video

Migrate previous 1.0 APIs to the 2.0 Platform:

Filter on API Name or Insight Name to find your matching object
Use Feed Name to help you locate the Metric(s) you want
Replace legacy table access with the new Insight ID via the Python SDK

Download a list of Insight IDs​

Migrating APIs​

Retrieving Insight Metadata​

Estimating Cost and Checking Filters​

Purchasing and Retrieving Data​

Walkthrough Video​