BigQuery visualization tools: Data Studio, Looker, Sheets, Power BI, Tableau and Excel compared

Every chart is a query

Before the tools, the part that decides your bill. None of these tools stores your BigQuery data by default (Power BI Import, Tableau extracts and Sheets extracts are the exceptions). A chart sends SQL to BigQuery, BigQuery scans the columns it needs, and the project the tool is set to bill pays for the bytes.

Here is what one ordinary chart, users and purchases by device for January, sends to Google’s public GA4 sample:

SELECT
  device.category AS device,
  COUNT(DISTINCT user_pseudo_id) AS users,
  COUNTIF(event_name = 'purchase') AS purchases
FROM `bigquery-public-data.ga4_obfuscated_sample_ecommerce.events_*`
WHERE _TABLE_SUFFIX BETWEEN '20210101' AND '20210131'
GROUP BY device
ORDER BY users DESC

Three rows on screen; 50,963,783 bytes processed, 51,380,224 billed. Without the _TABLE_SUFFIX filter, the same chart over the whole sample processes 181,623,036 bytes, 3.6 times as much. A dashboard date control that is not wired to the table’s date filter is the most common way to pay for that difference on every view.

Google’s on-demand price is $6.25 per TiB after the first free TiB each month (BigQuery pricing, read in October 2026). One run of this chart costs about $0.0003. Put it on a page with ten charts, open it 50 times a day for 22 working days, and change one filter each time: 11,000 runs, about 0.51 TiB, about $3.20 a month once the free TiB is used up. The sample is small; a real GA4 export is often a hundred times larger, and the bill scales with it. More on the price model in the BigQuery pricing guide.

Two things reduce it:

• The tool’s cache. Data Studio keeps query results for the freshness interval (12 hours by default for BigQuery), but Google says any new query, such as a changed filter, goes to the data source again (Manage data freshness, read in October 2026).
• BI Engine. Google describes it as an in-memory service that caches frequently used data and works with Looker, Data Studio, Tableau and Power BI. You pay for a reservation of memory in a region, not per query. It does not accelerate queries on wildcard tables, external tables or results over 1 GiB (BI Engine introduction, read in October 2026). The events_* query above would not be accelerated.

The cheapest fix is upstream of any tool: point charts at a small summary table, not at raw events.

Data Studio

Google renamed it: “Looker Studio is now called Data Studio” (Google’s docs, October 2026). It is Google’s free dashboard tool and the shortest path from BigQuery to a shared report.

• Connector: built in. Pick a table, or choose CUSTOM QUERY and paste SQL. A Billing Project setting decides which project pays for custom query costs (Connect to Google BigQuery, read in October 2026).
• Credentials: with the owner’s credentials (the default), only the data source owner needs BigQuery roles, and viewers see charts without their own BigQuery access. With viewer’s credentials, every viewer needs the roles.
• Refresh: cached. For BigQuery the freshness can be set from 1 minute to 12 hours; 12 hours is the default.
• Price: “available at no charge for creators and report viewers”. Data Studio Pro, with support and team content management, is $9 per user per project per month (Data Studio product page, read in October 2026).
• Trap: for date-sharded tables such as events_YYYYMMDD, Data Studio “automatically queries only the last 28 days of data” unless you change the chart’s date range. A year-over-year chart shows four weeks and no error.

Step-by-step setup is in how to connect BigQuery to Data Studio.

Looker

Looker is a different product from Data Studio: a modeling layer plus BI. Analysts define tables, joins and measures once in LookML, and every Explore, Look and dashboard generates SQL from that model.

• Connector: Google BigQuery Standard SQL dialect, with a service account or per-user OAuth (Google BigQuery connection, read in October 2026). Persistent derived tables need a scratch dataset Looker can write to.
• Version control: “Looker uses Git to record changes and manage file versions”; each LookML project is a Git repository (version control docs, read in October 2026). If you searched for a browser-based BI tool with version control on top of BigQuery, this is the one in this list.
• Refresh: queries run against BigQuery when a user opens content; results are cached. With OAuth, Google notes caching is per user, so two people running the same query each pay for it.
• Price: the Standard edition is sold on an annual commitment, priced through Google sales; there is no list price on the page (Looker pricing, read in October 2026).
• Trap: it is not the cheap option the name suggests. If the question is “free dashboards on BigQuery”, that is Data Studio. The two are compared in Looker vs Data Studio.

Google Sheets with Connected Sheets

Connected Sheets links a sheet to a BigQuery table or query. Google lists what you need as access to Google Cloud, BigQuery access and a project with billing (Get started with BigQuery data in Google Sheets, read in October 2026).

• Rows: the sheet shows a 500-row preview, but pivot tables, formulas and charts run on the full table in BigQuery. A pivot table returns at most 200,000 rows. An extract of up to 50,000 rows has no cell limit; up to 500,000 rows it is capped at 5 million cells; above 500,000 rows it is not supported (Connected Sheets, read in October 2026).
• Refresh: by hand or on a schedule. Each refresh is a query billed to the project the user selected.
• Trap: every person with edit access can write their own pivot over raw tables, each one a separate query with its own definition of “revenue”. Users with Sheets-only access cannot refresh at all.

The other ways into Sheets are in how to connect Google Sheets to BigQuery, and Connected Sheets is set against a published Data Mart in OWOX Data Marts vs Connected Sheets.

Power BI

• Connector: “Google BigQuery”, generally available, in Power BI semantic models and Power Query Online. It supports Import and DirectQuery, with OAuth or a service account (Microsoft Learn, page dated September 2026). Microsoft is moving it from an ODBC driver to a new ADBC driver, which it recommends for new workloads.
• Refresh: in the Power BI service, Microsoft says the connector is reached through a cloud-to-cloud connection, and scheduled refresh runs there; Microsoft advises staggering refreshes, because many at once can hit a Google API quota.
• Price: Power BI Desktop is a free download; sharing a report needs Pro at $14 per user per month or Premium Per User at $24, both paid yearly (Power BI pricing, read in October 2026).
• Traps: in DirectQuery, every visual interaction is a BigQuery query; Import runs one query per refresh and serves the rest from Power BI’s memory. Microsoft lists a known issue that the DateTime type is not supported in DirectQuery, nested fields arrive as JSON text, and materialized views do not show in the Navigator. Set the Billing Project in the advanced options, or users without bigquery.jobs.create on the first listed project get “Access Denied”.

Tableau

• Connector: “Google BigQuery”, in Tableau Desktop, Tableau Cloud, Tableau Server and Tableau Prep, with OAuth or a service account JSON file (Tableau help, read in October 2026).
• Refresh: Tableau’s own advice is “Because of the large volume of data in BigQuery, Tableau recommends that you connect live.” Live means every view and filter is a BigQuery query; an extract copies the data into Tableau and refreshes on its schedule.
• Price: licensed per user by role (Creator, Explorer, Viewer). Tableau’s pricing page refused our reader in October 2026, so we quote no figure; check it before you budget.
• Traps: you cannot mix standard SQL and legacy SQL views in one workbook, nested data has limited support, and web authoring cannot use two BigQuery accounts in the same workbook.

Excel

Excel has no built-in BigQuery connector: Microsoft’s product list for the Power Query BigQuery connector names Power BI and Power Query Online, not Excel. The routes that work are a CSV export, the BigQuery ODBC driver with Power Query (refresh by hand, on opening, or on a timer while the workbook is open), or the OWOX Data Marts add-in. All of them are walked through in how to connect BigQuery to Excel.

The add-in, from OWOX’s Microsoft Excel destination docs (read in October 2026):

• installs from Microsoft Marketplace or by an admin through the Microsoft 365 admin center;
• runs in Excel on the web, Microsoft 365 on Windows or Mac, and Excel 2019 or later on Mac, but not perpetual Excel 2019 and 2021 on Windows;
• binds one report to one worksheet; a refresh overwrites only the imported columns, so your formulas to the right survive;
• refreshes by hand only, while the workbook is open; there is no scheduled refresh, and Excel’s limit of about 1,048,576 rows per sheet applies.

Which one to choose

• Free, shared dashboards on BigQuery: Data Studio, with owner’s credentials and a summary table underneath.
• One governed model, version-controlled, many teams: Looker, if the budget is a contract.
• People who think in spreadsheets: Connected Sheets for analysts; a published Data Mart for business users who should not write their own pivots over raw tables.
• A Microsoft 365 company: Power BI, Import mode unless the data must be live.
• Analysts who explore visually and have licences: Tableau.
• Finance packs in workbooks: Excel through ODBC or the add-in.

If you want an AI assistant answering questions over BigQuery rather than a dashboard, see AI analytics on BigQuery and the AI data analyst tools comparison.

One query, every tool

Most teams do not pick one tool. Marketing reads Data Studio, finance wants Excel, the CEO wants a number in Slack, and each tool gets its own copy of the SQL. Three months later the copies disagree, and the analyst is asked which one is right.

In OWOX Data Marts you publish the SQL once as a Data Mart, with its columns described, and deliver that same result to Google Sheets, Data Studio dashboards, the Excel add-in, email and Slack. A change to the query changes every report that reads it. OWOX Data Marts runs your SQL as written, in your own BigQuery project, and does not transform the data; the cost rules above still apply, so publish the query that reads the summary table, not the raw events. The approach is the data model layer between the warehouse and the tools.

Google BigQueryBigQuery aggregate functions: COUNT, SUM, STRING_AGG, ARRAY_AGG and moreOctober 16, 2026→Google BigQueryBigQuery Conversion Functions: Every Type-to-Type ConversionOctober 6, 2026→Google BigQueryBigQuery DML: INSERT, UPDATE, DELETE and MERGE with tested examplesOctober 16, 2026→

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