Preparing API data for analytics means converting paginated, nested API responses into a flat, normalized dataset ready for dashboards.
Most teams write scripts for this. They work at first, then break when an endpoint adds a field, changes a type, or returns a slightly different nested object. This guide shows a pipeline approach: combine the responses, transform the JSON, preview the output, and reuse the workflow when the API changes.
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The problem with raw API data
Raw API responses are rarely analytics-ready. Before you can load them into dashboards, BI tools, spreadsheets, or warehouses, you usually need to fix a few structural problems:
- Pagination splits records across multiple responses
- Nested JSON does not map cleanly to table columns
- Numbers, booleans, and dates may arrive as strings
- Null, empty, or missing fields create inconsistent rows
- Retries or overlapping pages can create duplicate records
The goal is not just to clean one file. The goal is to make API data analytics-ready in a repeatable way, so the same workflow can run again when new responses arrive.
Example: Raw API response
Here’s a typical paginated API response with nested user.email, numeric totals stored as strings, yes/no booleans, a null status, and a duplicate customer across pages.
[
{
"page": 1,
"next": "/customers?page=2",
"data": [
{ "id": "cus_001", "user": { "email": "maya@example.com" }, "total": "29.99", "subscribed": "yes", "status": "active" },
{ "id": "cus_002", "user": { "email": "liam@example.com" }, "total": "49.50", "subscribed": "no", "status": null }
]
},
{
"page": 2,
"next": "/customers?page=3",
"data": [
{ "id": "cus_003", "user": { "email": "ava@example.com" }, "total": "15.00", "subscribed": "yes", "status": "active" },
{ "id": "cus_001", "user": { "email": "maya@example.com" }, "total": "29.99", "subscribed": "yes", "status": "active" }
]
},
{
"page": 3,
"next": null,
"data": [
{ "id": "cus_004", "user": { "email": "noah@example.com" }, "total": "78.25", "subscribed": "no", "status": "active" }
]
}
]How to normalize API JSON for analytics
The pipeline version of this workflow combines smaller JSON operations into one reusable process:
This transforms raw API responses into analytics-ready tables for dashboards, BI tools, and data warehouses.
- Combine paginated responses: collect every page into one array
- Flatten nested fields: convert nested objects into top-level keys with
structure.flatten-nest - Normalize values: coerce totals, booleans, dates, and empty values with
convert.format-valuesandconvert.map-values - Clean records: remove nulls and duplicates with
cleanup.clean-json
Step 1 is "get all data" (the pagination article). Steps 2–4 are "make data usable" (this article). The combination delivers what no single utility does alone: an analytics-ready dataset.
How to flatten nested API responses
Flattening JSON is required to convert nested API structures into tabular data for analytics.
For this example, the workflow does:
1. Combine all page results into one array
2. Flatten nested `user.email` into `user_email`
3. Coerce string totals into numbers
4. Map `"yes"`/`"no"` subscribed values into `true`/`false`
5. Remove null fields and duplicate recordsSee how this transformation works step by step in the interactive pipeline below.
