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Microsoft Fabric or Google BigQuery for a small company?

Published 31 October 2026

Short answer. Pick the one that matches the stack you already work in and the place your accounting data lives. If you work in Microsoft 365 and keep your books in Business Central, Fabric is the natural choice: Microsoft gives it a ready connector to Business Central, and Power BI is part of it. If you work in Google Workspace, or your main sources are ones Google already connects, such as Shopify or Google Analytics, BigQuery is the natural choice, and you pay per query by default. If you have only a few systems and a spreadsheet that still works, you need neither yet.

This page compares the two on what decides the choice for a small company: how your data gets in, how billing works, which AI is built in, the skills it takes, and how easy it is to leave. The facts come from Microsoft’s and Google’s own documentation, checked in October 2026.

What Fabric and BigQuery are

Both can hold the one data warehouse of a data foundation: the single place where the figures from all your systems are collected, so every report and every AI agent reads the same numbers.

Microsoft Fabric is Microsoft’s analytics platform, delivered as a service. It stores data in OneLake, a data lake that comes with every Fabric tenant, and Power BI is built in. (Microsoft Learn: What is Microsoft Fabric)

Google BigQuery is Google Cloud’s fully managed, serverless data warehouse. Google runs the servers, so you do not size or maintain any. (Google Cloud: BigQuery overview)

In both cases the data sits in your own cloud: for Fabric, your company’s Microsoft tenant; for BigQuery, your company’s Google Cloud project. You control access.

Fabric and BigQuery side by side

	Microsoft Fabric	Google BigQuery
The stack it sits in	Microsoft: your Microsoft 365 sign-ins, Power BI built in. [1]	Google Cloud: your Google Workspace sign-ins; Google Sheets reads BigQuery data through Connected Sheets. [2]
Business Central data in	A ready Business Central connector in Dataflow Gen2. Mirroring from Business Central, which copies tables into OneLake and keeps them up to date, is a preview at Microsoft. [3][4]	No ready Business Central connector. A pipeline reads the Business Central APIs, which Microsoft names as the only supported way to read Business Central online. [5][6]
Other sources	More than 200 connectors, including Salesforce, Google BigQuery and Google Sheets. [1][3]	Scheduled transfers from Google’s own services, Salesforce, Shopify (preview), Stripe (preview), HubSpot (preview), databases and cloud storage. [5]
How billing works	You buy a capacity of a chosen size and pay for it per second while it runs, or reserve it for a year. Storage is billed separately. [7][8]	By default you pay for the data each query reads. You can switch to paying for reserved capacity. Storage is billed separately, and a monthly free tier applies. [9]
AI built in	Copilot in Fabric, on a paid capacity. [10]	Gemini in BigQuery; core features at no extra cost on all BigQuery compute options. [11]
Skills it takes	Power Query, T-SQL and Power BI; Spark notebooks for heavier work. [12]	SQL (GoogleSQL) and Python; a pipeline for sources without a connector. [13]
Getting data out	Tables are stored in open formats (Delta Parquet or Iceberg), readable with standard data lake tools. [14]	Tables export to Cloud Storage as Parquet, Avro, CSV or JSON. [15]

The numbers in brackets refer to the Sources list at the foot of the page.

How your ERP data gets in

Your ERP is the system that holds your books and orders, such as Business Central. A connector is a ready-made link that copies data from one system into another on a schedule. Where no connector exists, a pipeline does the same job: a small program that reads the system and loads the data.

Business Central and Fabric. Fabric has a ready Business Central connector in Dataflow Gen2. Microsoft also offers mirroring as a preview: you choose the companies and tables in Business Central, and it copies a first snapshot into OneLake and then keeps it up to date with the changes. Microsoft describes mirroring as a preview, under preview terms. (Fabric connector overview; Data mirroring to Microsoft Fabric from Business Central)

Business Central and BigQuery. BigQuery’s Data Transfer Service has no Business Central connector. A pipeline reads Business Central through its APIs, which Microsoft names as the only supported way to read data from Business Central online. (BigQuery Data Transfer Service data sources; Extract data from Business Central)

Other accounting systems. Neither vendor’s list of ready connectors includes e-conomic or Dinero, as checked in October 2026. For those systems, both stacks need a pipeline that reads the system’s API, so the choice between them does not change.

Shop and marketing data. BigQuery has scheduled transfers for Shopify, Stripe and HubSpot (all three in preview) and for Google Analytics 4 and Google Ads. Check the list for your own sources before you choose.

How each one bills

Both vendors bill for computing and for storage separately. They differ in how they bill the computing.

	Microsoft Fabric	Google BigQuery
Computing	A capacity: a fixed amount of computing power you choose the size of. Azure bills it per second while it runs, with no commitment, or you reserve it for a year at a lower rate. [7]	On-demand by default: you pay for the data each query reads. Or capacity: you reserve processing power and pay for it over time, with optional one- or three-year commitments. [9]
Pausing	You can pause a capacity to stop paying for computing. While it is paused, the content on it is unavailable. [8]	On-demand has nothing to pause; you pay when a query runs. [9]
Storage	Billed per GB stored, apart from the capacity, and still billed while the capacity is paused. [16]	Billed per GB stored; data left unchanged for 90 days moves to a lower long-term rate. [9]
Viewing reports	On the smaller capacities, everyone who views Power BI reports needs a Power BI Pro licence. [17]	Viewers of a shared Google Sheet do not need BigQuery access; refreshing the data does. [2]
Trying it out	A 60-day trial capacity, without Copilot. [18]	The BigQuery sandbox, with no credit card; tables expire after 60 days. [19]

For a small company this means two different risks. With Fabric, the cost is predictable, but you pay for the capacity while it runs, whether anyone uses it or not. With BigQuery on-demand, a month with few queries costs little, but a report that reads a large table on every refresh costs more each time; set a cost limit on the project. Current prices are on Microsoft’s Fabric pricing page and Google’s BigQuery pricing page.

The AI built into each

Copilot in Fabric helps you transform data, write queries and build Power BI reports. It needs a paid capacity of size F2 or larger; it does not run on the trial. It is switched on by default for paid capacities, and the company decides in the tenant settings who may use it. Copilot’s use counts against your capacity. (Overview of Copilot in Fabric; Enable Copilot in Fabric)

Gemini in BigQuery helps you write and explain SQL, prepare data, explore it with data insights and chat with it through data agents. Google offers its core features at no extra cost on all BigQuery compute options. Google notes that Gemini in BigQuery does not support all the compliance offerings BigQuery itself supports, so check that before you switch it on. (Gemini in BigQuery overview; Gemini for Google Cloud pricing; Set up Gemini in BigQuery)

Both assistants help the people who build and read the data. AI agents you build yourself can read either warehouse, so the built-in assistant does not decide which agents you can have later.

The skills each one takes

Fabric suits a company where someone already builds Power BI reports. Data comes in through Power Query (Dataflow Gen2), the warehouse speaks T-SQL, and the reports are Power BI. Someone also has to size the capacity and watch its use. (What is Fabric Data Warehouse; Plan your capacity size)

BigQuery suits a company that is comfortable with SQL and has someone, in-house or a partner, to run pipelines for the sources Google does not connect. There is no capacity to size on on-demand billing. (Introduction to SQL in BigQuery)

In both, someone sets up access: your IT, in-house or your IT partner.

Getting your data out again

Both keep your data in forms other tools can read. Fabric stores tables in OneLake in the open Delta Parquet or Iceberg formats, which other data lake tools read directly. BigQuery exports tables to Cloud Storage as Parquet, Avro, CSV or JSON. Fabric can also mirror data from BigQuery, a feature Microsoft lists as generally available, so the two can work together. (Mirroring Google BigQuery in Fabric; OneLake overview; Export table data to Cloud Storage; Get started with mirroring in Fabric)

The harder part to move is the logic: the queries written in one dialect of SQL, the pipelines and the report models. Keep that logic as code in a repository your company owns, and a later move means rewriting parts you can see.

Which one fits your company

Your situation	The fit	Why
You work in Microsoft 365, keep your books in Business Central and report in Power BI	Fabric	The Business Central connector is ready, mirroring is a preview at Microsoft, Power BI is built in, and your people already sign in to Microsoft.
You work in Google Workspace, or your main sources are Shopify, Google Analytics or Google Ads	BigQuery	Google connects those sources, Connected Sheets puts the data in the spreadsheets you already use, and with on-demand billing a month with few queries costs little.
You use both, for example Microsoft 365 with a Google-connected shop	Follow your accounting data and your reports	Put the warehouse where the books and the reports live. BigQuery works with Power BI, and Fabric can read from BigQuery, so the other side can still connect.
A few systems and a spreadsheet that works	Neither yet	See below.

BigQuery lists Power BI among the business intelligence tools it supports. (BigQuery overview)

When you need neither yet

A data warehouse earns its keep when figures from several systems must be matched. You probably do not need one yet if all of these are true:

Your figures come from one accounting system, perhaps with a shop that already posts into it.
The reports in your accounting system answer the questions you ask.
One spreadsheet does the rest, and the person who keeps it can trust it.
No AI agent needs to read figures across systems.

In that case, start inside the systems you have: clean up customer and supplier records, keep one chart of accounts, and write down what each key figure means. That is getting your data in order, and it needs no warehouse. You do not need Gathena for it either.

The answer changes when a second system’s figures must be matched by hand, when month-end means exporting to a spreadsheet, or when an AI agent should answer questions from your figures. Then the choice on this page becomes worth making.

Checked against the vendors’ documentation on 31 October 2026.

Sources

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