Can I query Cloud Firestore in plain English instead of writing SQL?

Yes, once Cloud Firestore data is in BigQuery. Dataki has no Cloud Firestore connector of its own; it reads the dataset the data lands in (Google's Firestore to BigQuery extension), turns a plain-English question into SQL against it, runs it and returns a chart. The generated query is shown with every result, so you can verify or edit it.

How do I build a dashboard from Cloud Firestore data without an ETL pipeline?

Set up the route in the Cloud Firestore setup guide once. The copy into BigQuery then runs on its own, so there is no pipeline for you to maintain. Connect that dataset to Dataki, ask your question and keep the answer as a dashboard widget; Dataki queries the dataset at read time, so the dashboard is as fresh as the export.

Can Claude, ChatGPT or Cursor query my Cloud Firestore data?

Yes, through Dataki's hosted MCP server at https://dataki.ai/api/mcp. Connect Cloud Firestore to Dataki, add that URL as a connector in Claude or ChatGPT (or in Cursor, VS Code or Claude Code with a Dataki key), and the assistant can read the schema and run read-only queries. It gets the rows back; the credentials stay encrypted in Dataki and are never handed to the model.

Dataki for Cloud Firestore

Ask Cloud Firestore in plain English

Query Firestore collections in plain English. Google's Firestore to BigQuery extension keeps a live copy of each collection, and Dataki answers questions about your documents from it.

Through BigQuery Google's Firestore to BigQuery extension. The guide takes about 15 minutes.

Follow the setup guide Open Dataki

app.dataki.ai / chat BigQuery · firestore_export

How many orders are in each status right now?

Reading the schema, writing the query…

SELECT JSON_VALUE(data, '$.status') AS status, COUNT(*) AS orders
FROM   firestore_export.orders_raw_latest
GROUP BY status
ORDER BY orders DESC

2,318 orders are shipped and on their way; 486 are still waiting for payment.

01Questions

What people ask Cloud Firestore

Each one becomes a query against the Cloud Firestore data, run read-only, with the SQL shown beside the chart so anyone who reads SQL can check it. Anything worth keeping goes on a dashboard that refreshes on its own.

How many orders are in each status right now, and how did that change this month?
Which users created the most documents last week?
How many documents were deleted in each collection yesterday?
What is the average time from order created to delivered?

02From your assistant

Ask Cloud Firestore from Claude, ChatGPT or Cursor

Dataki runs a hosted MCP server. Add it to your assistant once and it can read the Cloud Firestore data in BigQuery and run read-only queries through Dataki. The assistant gets the rows back; the credentials stay encrypted in Dataki and never reach the model.

Setup for your assistant:

Claude · dataki (MCP) BigQuery · firestore_export

From the Firestore export, how many orders moved to delivered each day this week?

list_sources · finds BigQuery · firestore_export
get_data_context · reads the tables and what the columns mean
query · runs the SQL read-only, returns the rows
render_ui · draws the chart from those rows, in the chat

server URL streamable http

https://dataki.ai/api/mcp

03Setting it up

Getting Cloud Firestore data to Dataki

Google's Firestore to BigQuery extension. The setup guide covers it step by step.

Setup: About 15 minutes per collection, plus a one-off import of the documents that already exist.
Cost: Needs the Blaze plan. The extension's function calls (2 million a month free) and BigQuery streaming inserts ($0.01 per 200 MiB) are billed to you.
Freshness: Seconds. Each write reaches BigQuery as it happens.
Scope: One collection per installation. Install it again for each collection you want to ask about.

Read the Cloud Firestore guide Open Dataki

The answer is still there tomorrow.

Ask in plain language. Dataki writes the SQL, runs it against your warehouse and draws the chart. Anything worth keeping becomes a dashboard with a link — not a message that scrolls away.

app.dataki.ai / chat

Which channels actually brought in revenue last quarter?

Queried bigquery · payments · 1.2 GB scanned

SELECT channel,
       SUM(amount) AS revenue
FROM   payments
WHERE  created_at >= DATE_TRUNC(CURRENT_DATE(), QUARTER)
GROUP BY channel
ORDER BY revenue DESC

Revenue by channel

Q3 · 5 rows

Organic search: $412,900
Partner referral: $268,400
Email: $151,200
Paid social: $96,700
Direct: $61,300

One click later, that chart is a dashboard of its own:

app.dataki.ai/d/revenue-by-channel

Live, not a screenshot. It re-runs the query on a schedule, so the number your team reads on Friday is Friday's number.

Share the link with anyone. Viewers are free on every plan.
Edit the SQL by hand whenever the model gets the logic almost right.
Drop the same dashboard into your own product with one line of HTML.

Start free See how it works

Using Dataki with Cloud Firestore

Can I query Cloud Firestore in plain English instead of writing SQL?: Yes, once Cloud Firestore data is in BigQuery. Dataki has no Cloud Firestore connector of its own; it reads the dataset the data lands in (Google's Firestore to BigQuery extension), turns a plain-English question into SQL against it, runs it and returns a chart. The generated query is shown with every result, so you can verify or edit it.
How do I build a dashboard from Cloud Firestore data without an ETL pipeline?: Set up the route in the Cloud Firestore setup guide once. The copy into BigQuery then runs on its own, so there is no pipeline for you to maintain. Connect that dataset to Dataki, ask your question and keep the answer as a dashboard widget; Dataki queries the dataset at read time, so the dashboard is as fresh as the export.
Can Claude, ChatGPT or Cursor query my Cloud Firestore data?: Yes, through Dataki's hosted MCP server at https://dataki.ai/api/mcp. Connect Cloud Firestore to Dataki, add that URL as a connector in Claude or ChatGPT (or in Cursor, VS Code or Claude Code with a Dataki key), and the assistant can read the schema and run read-only queries. It gets the rows back; the credentials stay encrypted in Dataki and are never handed to the model.

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