In April 2026 Google quietly did something it had spent four years undoing: it renamed Looker Studio back to Data Studio. If your team spent 2022 updating slide decks from "Data Studio" to "Looker Studio," you are allowed a moment of exasperation. Then it is worth understanding why the name went back, because the rename says something real about how Google now positions its three analytics front-ends: Looker, Data Studio and BigQuery Studio.
The naming timeline, for the record
- 2016: Google launches Data Studio, a free, self-serve visualization tool that connects to Google Sheets, Google Analytics, BigQuery and a long list of third-party connectors.
- February 2020: Google closes its acquisition of Looker, the governed-BI platform built around the LookML semantic layer.
- October 2022: Google rebrands Data Studio as Looker Studio, and the paid Looker Studio Pro tier appears. The stated idea was "one Looker family": Looker for governed enterprise BI, Looker Studio for self-serve, with Looker Studio able to consume Looker explores as a data source.
- 2023 to 2025: Gemini arrives across the lineup: Gemini in Looker (assistants for LookML, visualizations and dashboard summaries), Conversational Analytics, and natural-language features inside Looker Studio. BigQuery gains BigQuery Studio, a notebook-and-SQL workspace inside the console.
- April 2026: Looker Studio becomes Data Studio again. The Looker name is reserved for the governed platform; Data Studio is the free and Pro self-serve product; the Conversational Analytics and Gemini features continue across both.
Nothing about your existing reports changed with the rename. Report URLs, connectors, Pro licensing and the Looker connector all carried over. What changed is how Google wants you to think about the products.
What each product is actually for
Looker is a semantic-layer platform. You model your warehouse once in LookML (joins, metrics, row-level security), and every dashboard, embed, scheduled report, API call and, now, AI assistant queries through that model. Its strengths are governance, consistency, embedding into products, and the API. Its costs are a real license, a development workflow with Git, and developers who know LookML. Looker is for the numbers the company is going to argue about.
Data Studio is a visualization and reporting tool. It connects to data sources directly (BigQuery tables, Sheets, GA4, hundreds of partner connectors) or to Looker explores, and lets anyone build a report in an afternoon. There is no semantic layer of its own; metric definitions live inside each report, or in the Looker model if you connect through Looker. Its strengths are speed, price (free, with a Pro tier for team features, Google Cloud support and enterprise admin), and ubiquity among marketers and analysts. It is for exploratory reporting and for audiences who would never log in to Looker.
BigQuery Studio is not a BI tool at all. It is the workspace inside BigQuery where analysts and data scientists write SQL, run Python notebooks, build data canvases and call Gemini to generate queries. It is where data is prepared and investigated, not where it is published to executives.
A decision matrix
| Need | Looker | Data Studio | BigQuery Studio |
|---|---|---|---|
| Governed company-wide metrics | Yes | Only via the Looker connector | No |
| Customer-facing embedded analytics | Yes (signed embed, Embed SDK) | Limited (public or Pro embedding) | No |
| Self-serve reports by non-technical users | Possible, with curation | Yes | No |
| Free or low-cost departmental reporting | No | Yes | N/A |
| Ad-hoc SQL, notebooks, ML | No | No | Yes |
| Row-level security tied to the user | Yes (access_filter, user attributes) | Via BigQuery policies or Looker | BigQuery policies |
| Natural-language questions grounded in a semantic layer | Yes (Gemini in Looker, Conversational Analytics) | Partly (Gemini on the report's fields) | SQL generation |
| Scheduled delivery and alerts | Yes | Yes (email; Pro adds more) | Scheduled queries only |
The pattern that works in most organizations we see: BigQuery Studio for preparation and investigation, Looker as the source of truth and the thing you embed, and Data Studio for the long tail of lightweight reports, ideally connected through Looker so the metrics still match.
What the split means if you are a Looker customer
- The Looker name now means the platform. When Google documentation, release notes or sales say "Looker," they mean the LookML product. Less ambiguity, fewer support tickets opened against the wrong product.
- The Looker connector for Data Studio is the integration to invest in. If departments are building Data Studio reports straight against BigQuery tables, their revenue figure and your Looker revenue figure will diverge. Pointing those reports at Looker explores fixes the divergence without taking the tool away.
- Gemini features are being developed on both sides, but the semantic layer is what makes them trustworthy. Conversational Analytics against a curated Looker explore produces answers that match the dashboards. The same feature on a flat table produces answers that match the table, whatever that table happens to mean. This is the single strongest argument for keeping Looker at the center.
- Budget lines move. Data Studio Pro is billed per user through Google Cloud; Looker (Google Cloud core) is billed to the project. Finance will ask why there are two BI line items. The answer is the matrix above.
- Expect the names in old content to be wrong for a while. Including, until recently, on this site.
Our recommendation
Do not consolidate onto one tool; they are not substitutes. Instead, decide explicitly which product owns which job, write it down, and make Looker the semantic layer the other two consume. If you would like help drawing that boundary for your organization, or want a second opinion on whether your Looker model is ready to be the source of truth for Gemini, our Looker consultants are happy to talk. Get in touch.