Resources · Make integration

BigQuery Make integrationAutomate BigQuery with Make.

Want new rows and finished queries in BigQuery to kick off work elsewhere? The BigQuery Make integration gives you 20 modules, including 3 triggers. This guide is for data and ops people new to Make: connect the account, then build a first scenario.

Try the BigQuery integration on MakeSee the 20 modules

Verified Trustpilot reviews · AI, automation & growth agency

What you can automate

What can the BigQuery Make integration do for you?

The BigQuery Make integration lets Make read, write and manage your BigQuery data without code. You build a scenario, the automation itself, from modules: each module is one brick, such as a trigger that spots a new row or an action that runs a query. Your warehouse then talks to the rest of your tools.

Rows that show up where people work. Watch Rows notices each new row inserted in a table, and the next module can forward it to a chat tool, a CRM or a sheet. A sales team sees fresh orders without opening the console.

Data that lands in the warehouse on its own. Upload Data (Streaming) adds a row to a table each time another app produces a record, a form answer or a paid invoice for instance. For a marketing team, that ends the export and import routine. If the source lives in Airtable, the Airtable Make integration shows the other half of that flow.

Queries that report back. Run a Query executes SQL and returns the results when the query finishes within its timeout; Watch Query Jobs Completed reacts once a job ends, whether it succeeded or failed. An analyst can be told when a long job is done.

What the integration does not do: none of the 3 triggers is instant. Make checks BigQuery on the scenario's schedule, so a new row is picked up on the next check, not the second it arrives. When an endpoint has no module of its own, Make an API Call reaches it with the same connection.

BigQuery also has a node in n8n. To weigh the two tools on your own criteria, read n8n vs Make; to see what Make plans include, check Make pricing.

Connect

How do you connect BigQuery to Make?

  1. 01

    Add a BigQuery module

    Open your scenario, click the +, search for BigQuery and pick any of its modules. In the module panel, click Create a connection. The connection is your BigQuery account linked to Make once, then reused by every BigQuery module.

  2. 02

    Name the connection

    This step is optional, but a clear name helps when several accounts or projects sit side by side in the same Make team. Pick something your colleagues will recognize at a glance in the dropdown.

  3. 03

    Authorize Make and save

    Make sends you to the BigQuery authorization page, or asks for the key the app gives you. Grant access, come back to Make and click Save. The module can now list what it is able to read in your account.

First scenario

Your first scenario with BigQuery

GoalWhen another app produces a new record, Make adds it as a row in a BigQuery table.

  1. 01

    Create the scenario

    Go to the Scenarios page, create a new scenario and click the + to place the first module. Choose the app that produces your records, a form tool or a shop for example, and link its account.

  2. 02

    Add Upload Data (Streaming)

    Click the + on the right of that first module, search for BigQuery and pick Upload Data (Streaming). Select your BigQuery connection, or click Create a connection if this is your first module.

  3. 03

    Map the data

    Pick the table that should receive the rows; Make lists what it can read once the connection exists. Then map each value from the previous module into the matching column of the new row.

  4. 04

    Test with Run once

    Click Run once and trigger a real record in the source app. Read the bundles each module received, one bundle per record, and check that the row arrived in BigQuery.

  5. 05

    Schedule and switch on

    Open the clock on the first module and set the schedule: every 15 minutes by default, and the Free plan cannot go below that. Switch the scenario on and it keeps running without you.

Triggers

What can start a BigQuery scenario?

3 modules start a scenario when something happens in BigQuery. A scheduled trigger is checked by Make on the scenario's schedule; an instant one is called by BigQuery as soon as it happens.

BigQuery1Watch Query Jobs Completed

Watch Query Jobs Completed

Scheduled trigger

In Make“Triggers when a query job has completed processing successfully or unsuccessfully.”

This trigger tells you that a query job has finished, so the next modules can fetch results or warn someone. It fires on success and on failure alike, which makes it useful for monitoring as much as for reporting.

How it fires
scheduled. Make checks BigQuery on the scenario's schedule and picks up every job that ended since the last check.
When to use it
a heavy query started from another tool, and you want the outcome posted to the team channel once it ends.
Watch out
a failed job also starts the scenario, so decide in the next modules what should happen when the outcome is a failure.
BigQuery2Watch Rows

Watch Rows

Scheduled trigger

In Make“Triggers when a new row is inserted in a table.”

With Watch Rows, every new row inserted in a table becomes a bundle, one item that travels to the next module. You can then copy it, alert someone or enrich it somewhere else.

How it fires
scheduled. Make checks the table on the scenario's schedule and collects the rows added since the previous run.
When to use it
an orders table fed by your shop, where each new line should reach the sales team without anyone running a query by hand.
Watch out
each row costs an operation in every module it passes through, so a busy table can use up your plan quickly.
BigQuery3Watch Tables

Watch Tables

Scheduled trigger

In Make“Triggers when a new table is created in a dataset.”

Know the moment a dataset gains a new table: Watch Tables starts the scenario for each table created there. That helps teams keep an eye on a dataset that several people or tools write into.

How it fires
scheduled. Make looks at the dataset on the scenario's schedule and returns the tables that appeared since the last check.
When to use it
a shared dataset where a new table should be announced to the data team, or logged in a tracking sheet.
Watch out
it only reacts to new tables, not to changes inside existing ones; for new data, use Watch Rows instead.
Modules

What else can Make do in BigQuery?

BigQuery gives you 17 modules. For each one: what it does for you, when to reach for it, and what to watch out for.

BigQuery1

Create a Dataset

Action

In Make“Creates a new dataset.”

You get a fresh dataset, the container that holds your tables, created from the scenario instead of the console. Handy when each new client or project deserves its own space.

When to use it
a new client is signed in your CRM and needs a separate dataset before any data arrives.
Watch out
place it right after the event that justifies it, so a test with Run once does not leave stray datasets behind.
BigQuery2

Create a Table

Action

In Make“Creates a new, empty table in the dataset.”

Adds a new, empty table inside a dataset you choose, ready to receive rows. It pairs well with Create a Dataset when a whole structure has to exist before loading starts.

When to use it
a new campaign or product line starts and you want a dedicated table for its data.
Watch out
the table is empty; rows come later through Upload Data (Streaming) or a query.
BigQuery3

Delete a Dataset

Action

In Make“Deletes the dataset specified by ID.”

Removes a dataset you point to by its ID, which keeps your project tidy once a project or trial is over. Nobody has to remember to clean up in the console.

When to use it
a client contract ends in your CRM and their dedicated dataset should go too.
Watch out
a deletion is not undone by Make; test on a throwaway dataset first and add a filter that checks the ID.
BigQuery4

Delete a Table

Action

In Make“Deletes the table specified by ID.”

Drops one table, picked by its ID, while the rest of the dataset stays in place. That is the tool for removing staging or temporary tables your scenarios created.

When to use it
once a temporary table has been copied elsewhere and no longer serves any report.
Watch out
Make cannot bring the table back; map the ID from a trusted module rather than typing it.
BigQuery5

Get a Dataset

Action

In Make“Returns the dataset specified by dataset ID.”

Returns the details of one dataset from its ID, so later modules can use its information. Think of it as a read-only look, nothing is changed.

When to use it
before an update, to check what a dataset currently holds, or to write its details into a documentation sheet.
Watch out
you need the exact ID; if you only know a name, run List Datasets first.
BigQuery6

Get a Table

Action

In Make“Gets the specified table resource by table ID.”

Brings back the resource of a single table, found by its ID, for the next modules to read. Like Get a Dataset, it only looks and never changes the table.

When to use it
after Watch Tables, to pull the details of the new table before announcing it to the team.
Watch out
it describes the table, not its content; for the rows themselves, use List Table Data.
BigQuery7

Get Query Results by Job ID

Action

In Make“Retrieves results of a specified query.”

Fetches the results of a query that already ran, identified by its job ID. You get the output without running the SQL a second time.

When to use it
a query was started earlier, and a later step, or another scenario, needs its results to fill a report.
Watch out
the module needs a job ID; List Jobs helps you find one when no previous module provides it.
BigQuery8

Make an API Call

Action

In Make“Performs an arbitrary authorized API call.”

Reaches any endpoint of the BigQuery API from your scenario, with the connection you already set up. It covers whatever the listed modules leave out, without a second login.

When to use it
you need an operation the list of modules does not offer, and you know which API endpoint handles it.
Watch out
this module expects you to know the endpoint from the BigQuery API documentation before you fill it.
BigQuery9

Run a Query

Action

In Make“Runs a BigQuery SQL query synchronously and returns query results if the query completes within a specified timeout.”

Runs a SQL query in BigQuery and hands the results to the next module, as long as the query completes within its timeout. It is the most direct way to turn a question into data for a scenario.

When to use it
someone submits a form asking for a figure. Make answers with the result of a prepared query.
Watch out
a long query may not finish in time; pair Watch Query Jobs Completed with Get Query Results by Job ID instead.
BigQuery10

Update a Dataset

Action

In Make“Updates information in an existing dataset.”

Changes the information of an existing dataset without recreating it, so its description or settings stay in step with your other tools.

When to use it
a project is renamed or changes owner in your project tool, and the dataset should reflect it.
Watch out
check the current state with Get a Dataset first, so you know what the update will overwrite.
BigQuery11

Update a Table

Action

In Make“Updates information in an existing table.”

Edits the information of a table that already exists, while its data stays where it is. Useful to keep table details consistent without opening the console.

When to use it
a table's purpose changes and its description should say so for the people who query it.
Watch out
this touches the table's information, not its rows; to add data, use Upload Data (Streaming).
BigQuery12

Upload a File

Action

In Make“Uploads a new file.”

Sends a new file to BigQuery from the scenario, taking the file that an earlier module received. No download and manual upload in between.

When to use it
a partner emails a data file, and it should reach BigQuery as soon as the scenario picks up the email.
Watch out
on the Free plan, Make handles files up to 5 MB, so large exports may need a paid plan.
BigQuery13

Upload Data (Streaming)

Action

In Make“Creates a new row of data in a table.”

Writes one new row into a table for each bundle it receives, which keeps your warehouse filled as records happen elsewhere. This is the module most first scenarios start with.

When to use it
every form answer, signup or payment from another app should appear in a BigQuery table.
Watch out
one row means one operation, so a scenario that receives many records at once uses your plan faster.
BigQuery14

List Datasets

Search

In Make“Lists all datasets in the specified project to which the user has been granted the READER dataset role.”

Returns the datasets of a project that your account is allowed to read, one bundle per dataset. A simple way to take stock before acting on them.

When to use it
you want an inventory of datasets written into a sheet, or you need to find an ID for the next module.
Watch out
datasets your account has no reader role on stay out of the list, even if they exist.
BigQuery15

List Jobs

Search

In Make“Lists all jobs that user started in the specified project.”

Lists the jobs your account started in a project, each as its own bundle, so you can see what ran and follow up.

When to use it
you need the ID of a past query to feed Get Query Results by Job ID, or want a log of recent jobs.
Watch out
it only covers jobs started by the connected account, not those of your colleagues.
BigQuery16

List Table Data

Search

In Make“Lists the content of a table in rows.”

Reads the content of a table row by row, without writing any SQL. Each row becomes a bundle that the next modules can send or transform.

When to use it
a small reference table, such as price lists or targets, has to be copied into another tool.
Watch out
a search returns zero, one or many bundles, and each one costs operations further down the scenario.
BigQuery17

List Tables

Search

In Make“Lists all tables in the specified dataset.”

Shows every table inside a dataset you pick, one bundle each. It answers the question of what a dataset actually contains.

When to use it
you document a dataset in a sheet, or you look for a table ID before calling Get a Table.
Watch out
a search can return zero bundles, so plan what the scenario should do when a dataset holds no table yet.
Need help

Need help automating BigQuery with Make?

A person reads every message.

FAQ

BigQuery and Make: frequent questions

01Is the BigQuery Make integration free?
Yes, BigQuery is a standard app, so it works on the Free plan of Make. That plan allows 2 active scenarios, a minimum of 15 minutes between two scheduled runs, 5 minutes of execution per run, files up to 5 MB and 512 MB of data transfer. Paid plans (Core, Pro, Teams and Enterprise) bring the interval down to 1 minute and remove the cap on active scenarios. Usage is counted in credits, which are operations: one module running on one bundle.
02What do you need to connect BigQuery to Make?
A BigQuery account and access to authorize Make. The documentation lists no other prerequisite. Add any BigQuery module, click Create a connection, give it a name if you like, then authorize Make on the BigQuery page or paste the key the app provides, and click Save. The connection is stored once and every other BigQuery module in your scenarios can reuse it, so you do not repeat the process for each module you add.
03Do the BigQuery triggers in Make fire instantly?
No. Watch Query Jobs Completed, Watch Rows and Watch Tables are all scheduled triggers: Make checks BigQuery on the schedule of the scenario, marked by the clock badge on the module. A new scenario checks every 15 minutes by default, which is also the shortest interval on the Free plan. Paid plans can go down to 1 minute. Rows, tables or jobs that appear between two checks are picked up at the next one.
04What if a BigQuery action is missing from Make?
Use Make an API Call. This module reaches any endpoint of the BigQuery API with the connection you already created, so an operation missing from the 20 listed modules can still be done in the scenario. You will need the BigQuery API documentation to know which endpoint to call and what to send. No numeric limit is documented for this app. Before you switch the scenario on, click Run once and read the bundle the call returns, so you know the answer looks the way the next module expects.
05Should you pick Make or n8n for BigQuery?
It depends on what your scenarios need. BigQuery exists in both tools: Make offers 20 modules, including 3 scheduled triggers and a generic API call, and n8n has a BigQuery node too. Compare the operations each one covers with the actions you actually plan, then choose the editor your team already knows. The hub comparison of the two tools goes through the criteria one by one. Both let you start small and test before you commit a whole workflow.
Same platform

Other docs you might need on this stack.

Browse all integrations