How to Connect Bitquery to Google Sheets (4 Methods Compared)

The Problem With Getting Sheet Data In and Out of Bitquery

You have a Google Sheet full of token contract addresses, wallet IDs, or liquidity pool pairs. You need Bitquery's indexed blockchain data — DEX trades, block stats, transfer history, aggregated metrics — pulled into your sheet for analysis, or you need your sheet-defined parameters fed into a Bitquery query so you don't have to re-type them every run.

Bitquery is good at what it does: deep, cross-chain indexed blockchain data served through a GraphQL API. But there's a gap between "I have a list of 20 token addresses" and "I have the last 7 days of trade data for all 20 of them in a spreadsheet." The default path is writing GraphQL queries by hand, executing them via curl or Postman, parsing JSON, and pasting the results column by column. Then doing it again next week.

Below are the four ways teams work around this. Only the last one requires no technical overhead.

Method 1: Manual Copy-Paste

The baseline. Open the Bitquery IDE, write a query for one address, run it, copy the table output, paste it into your sheet, reformat the columns, repeat for the next address.

With 20 token addresses, that's 20 separate query runs. With 5 columns of output each, you're doing 100 individual cell ranges. And when the trading week closes and you need fresh data, you start over from row 1.

The specific grind with Bitquery is that the data is precise and deep — which is why you're using it — but it arrives as nested JSON that doesn't paste cleanly into a flat spreadsheet. You end up re-shaping every result by hand. After the third weekly refresh, the process becomes less "data analysis" and more "data relocation."

Method 2: Zapier or Make

Both platforms have Bitquery-compatible connector options — or you can use a generic HTTP/GraphQL step to call the API directly. You wire up a schedule trigger, pass in your query, parse the response, and write the result back to a specified range.

Before you go further: do you know what a GraphQL body looks like in a webhook step? Have you configured dynamic variables in a Make module? Have you mapped nested JSON fields to sheet columns using dot notation? If those questions feel unfamiliar, this path isn't for you — jump ahead to Method 3 or 4.

Still here? Good. The workflow is achievable. You set up a trigger — time-based, usually. You POST to Bitquery's API endpoint with your query in the request body. You parse the data.EVM.DEXTrades array (or whichever schema path applies). You map each field to a column using Make's iterator or Zapier's formatter. It works.

But a row-at-a-time automation is not the same as a batch pull.

Sending 20 token addresses through a Make scenario means 20 separate API calls, 20 separate iterator runs, and a history log that becomes unreadable when address 14 returns no results and the mapper silently skips it.

You probably just need the DEX trade data. You probably have no idea how to write a dynamic GraphQL query variable inside a Make HTTP module — and there's no reason you should. So you hand it off to whoever on your team is comfortable with automations, and now you're waiting on a Slack reply while they figure out why the pagination isn't working.

Even when it's built, the scenario only handles what it was wired for. The moment you need to add a filter — say, minimum trade size over $50,000 — someone has to go back in and update the query body.

Method 3: The Previous Generation — Connector Add-Ons

Until recently, the sharpest available option for repeating a blockchain data pull into a spreadsheet was a class of add-ons that let you save query configurations — endpoint, field mapping, output range — and re-run them on a schedule.

That was a real step forward from manual execution. The config was saved. The output columns were consistent. You didn't have to reformat every week.

But you were still writing the query. You were still configuring the field map. You were still managing the output range. You were still deciding which fields to include and how to name them. The tool moved the data through, but the analytical thinking stayed on your side. And if the Bitquery schema changed — or you wanted to add a new metric — the config needed to be rebuilt from scratch.

This is the previous generation. It worked for the disciplined, but it asked a lot of them.

The Easy Way: Using SheetXAI in Google Sheets

There is a different path. SheetXAI is an AI agent that lives inside your Google Sheet. It reads the sheet — your token list, your wallet addresses, your pool pairs — and through its built-in Bitquery integration, it queries the API and writes the results back into your sheet for you. No query construction, no field mapping, no JSON parsing. You describe what you want.

Example 1: Pull DEX trade data for a token watchlist

For each token contract address in column A, query Bitquery for the last 7 days of Ethereum DEX trades and write total volume in USD, average price, and trade count into columns B, C, and D

Bitquery runs the query for each contract address, aggregates across the 7-day window, and writes three values per row — total USD volume in B, average price in C, trade count in D. Addresses that returned no trades get a zero, not a blank.

Example 2: Pull large token transfers from the archive

Using the Bitquery archive database, pull all Ethereum token transfers over $1,000,000 in the last 24 hours and populate this sheet with sender, receiver, token symbol, amount, and transaction hash

Each qualifying transfer lands as its own row, with the five fields mapped to the columns you'd expect. No separate export step, no JSON to untangle.

Try It

Get the 7-day free trial of SheetXAI and open any Google Sheet with a list of token addresses or wallet IDs, then ask it to pull the Bitquery data you need. The Bitquery integration is included in every SheetXAI plan.