How to Connect Keen IO to Google Sheets (4 Methods Compared)

The Problem With Getting Sheet Data In and Out of Keen IO

You have a Google Sheet full of data — event collection names you're tracking, property definitions you want to document, cached dataset configurations you need to audit. And over in Keen IO, you have an analytics project that's been accumulating event streams for months. Getting the two to talk requires more ceremony than it should.

Keen IO is built for custom event analytics — it ingests structured event data, runs queries at scale, and powers embedded dashboards. But the moment you want that data in a spreadsheet, you're writing API calls by hand or exporting a JSON blob that takes 20 minutes to flatten into usable rows.

Below are the four ways teams typically handle this. The first three have real limits.

Method 1: Manual Copy-Paste

The default approach: open the Keen IO dashboard or run an API query in your terminal, get the JSON output, and manually paste relevant fields into your sheet. For a single collection with ten properties, that's annoying. For forty collections with varying schemas, it means an afternoon of copying property names and guessing at types — and then redoing the whole thing next quarter when the schema changes.

The specific grind with Keen IO data is that the schemas aren't visible in one place. You're querying each collection separately, interpreting type inferences one at a time, and maintaining a registry by hand that's probably already stale before you finish building it. Nobody wants to be the person who owns that file.

Method 2: Zapier or Make

Both platforms have Keen IO connector options. You can wire up a trigger on a schedule, hit the Keen IO API, and write results back into a sheet. That's the architecture.

Before you go further: do you know what a Zapier filter is? A multi-step Zap? Field path notation for nested JSON? How to parse an array of objects into separate rows? If those feel unfamiliar, this path isn't for you — skip to Method 3 or 4.

If you're still here: yes, you can build it. The setup involves picking the right Keen IO endpoint, handling authentication tokens, mapping the response JSON to sheet columns, and dealing with how Keen IO returns nested property arrays that don't flatten automatically into rows.

The workflow runs. The problem is the structural ceiling.

A trigger-per-row automation isn't the same as a bulk schema pull. Fetching schemas for forty event collections means forty separate API calls, forty trigger fires, and a task history that becomes unreadable when collection 23 returns a 404 and the rest continue silently.

You probably just need a complete schema registry and you have no idea how to handle nested JSON arrays in a Zap. You shouldn't have to. So you hand this to whoever on your team writes automations — and now you're waiting in Slack to find out if they have bandwidth this sprint.

And once you need to filter by creation date, flag stale datasets, or join schema data against a second tab, you've left the automation's native capabilities entirely.

Method 3: The Previous Generation — Connector Add-Ons

Until recently, the best option for repeatable spreadsheet ↔ Keen IO workflows was a category of add-ons that let you configure column mappings and saved templates. You picked your endpoint, mapped the response fields, saved the config, and ran it on a schedule.

That was a real step up from copy-paste. The output was consistent, configs were reusable, and you didn't have to rethink the mapping every time.

But you were still responsible for the field mapping, the array-flattening logic, the filter conditions, the column naming conventions. The tool moved data through the pipe — but you still had to design the pipe. And the moment Keen IO added a new property to a collection or changed its inferred type, your config produced wrong output until someone went back in and fixed it.

This is the previous generation. It worked, but it asked a lot of the operator.

The Easy Way: Using SheetXAI in Google Sheets

There is a different approach entirely. SheetXAI is an AI agent that lives inside your Google Sheet. It reads the sheet, understands what you're looking at, and through its built-in Keen IO integration it can pull from or push to Keen IO for you. No field mapping templates, no API call authoring, no manual JSON flattening. You just ask.

Example 1: Pull a full schema registry for all event collections

Fetch the schema for every event collection in my Keen IO project and write one row per property to this sheet with columns for collection name, property name, and inferred type.

SheetXAI queries every collection's schema, flattens the nested property arrays, and writes one row per property — collection name in column A, property name in column B, inferred type in column C.

Example 2: Flag stale cached datasets before a migration

List all cached dataset definitions in my Keen IO project and write each one to this sheet with columns for dataset name, index field, query type, timeframe, and creation date. Flag any created more than 6 months ago by marking column E as "Stale—Review."

The pattern: the conditional logic — "created more than 6 months ago" — runs inside the same prompt as the data pull. You don't clean the data first and then annotate it. You ask for both at once.

Try It

Get the 7-day free trial of SheetXAI and open any Google Sheet where you're working with Keen IO event data, then ask it to pull your schema registry or audit your cached datasets. The Keen IO integration is included in every SheetXAI plan.