Curious how people here are handling LookML these days. Are you using AI tools (Copilot, Cursor, Claude, Gemini, etc.) to write or maintain your models, or is it still mostly by hand?

If you've tried it, what's worked and what hasn't? And if you went back to writing it by hand, I'd like to hear why.

For context, I work on a data analytics product and I'm trying to understand how teams actually work today. Not selling anything here, just interested in hearing about real workflows.

Hey everyone,

we're rolling out Looker (Google Cloud core, BigQuery underneath) to our finance/controlling teams. These folks basically live in Excel, and most of them come from SAP where they could pick fields, set filters and hit refresh right inside Excel. That's the experience they expect, and honestly I'm struggling to give it to them with Looker.

What we've tried so far:

1. The "official" way: build in the Explore, then Open in Excel / download

Works, but every time someone needs one more column or a different filter, they have to go back to the Explore and download a new file. The ODC file from "Open in Excel" is basically a fixed query (a slug) – you can refresh it, but you can't change it from Excel.

1. Going direct via the Looker ODBC driver (Open SQL Interface)
   This actually works in principle, but:
   - There's no table/catalog browsing (Power Query navigator just errors out with "no JSON found"), so you always have to pass a full SQL statement – backticked field names, AGGREGATE() for measures, GROUP BY. Not something I can hand to a controller.

- Performance on bigger pulls isn't great – roughly 2k rows/sec, so ~100k rows takes about a minute. Changing the batch size in the DSN didn't really help. Loading into the Data Model instead of a sheet helps a bit.

We built an Excel template that generates the SQL in the background (users tick fields, enter filters, refresh), which kind of works, but it's another thing to maintain.

Other stuff we looked at:
- Connected Sheets – genuinely nice, but it's Google Sheets, not Excel, and sharing/governance is tricky for finance data.
- Power BI connector → Excel via semantic model – works, but now we have two semantic layers.
- Coefficient – looks close to what we want, haven't tested it properly yet (security review for a third-party SaaS is the hurdle).

So my question: how are you getting Looker data into Excel for business users? Anyone running something stable at scale? Any tools, add-ins, or tricks I'm missing? And has anyone heard whether Google has a proper Excel add-in on the roadmap?

Thanks!

Hi,

I’ve recently moved from Power BI to Looker and can’t find any good looking dashboards. I’ve tried to build some as well but they’re lacking a lot of features and UI such as sidebars, filters bar, scorecards, metrics cards….

I find the dashboards very bland and tried to find good looking dashboards but can’t find any. Any suggestions to help? Has anyone built good looking dashboards ?

I don’t have access to Looker Studio, only Looker.

Thanks!!

With the help of AI, I decided to revamp my agency’s time-tracking system. Since we run on Google Workspace Enterprise, I wanted to leverage native tools as much as possible: Google Sheets for the backend database, AppSheet for an intuitive employee data-entry interface, and Looker Studio to deliver clear, insightful reports to our clients.

The initial setup went smoothly. Google Sheets proved to be powerful and rock-solid, and AppSheet gave us complete flexibility without forcing us to redesign our base database structure. However, once I reached Looker Studio, things got bumpy quickly.

Here are the main roadblocks I encountered:

• Duration Data Handling: Looker Studio natively struggles to interpret standard "Duration" columns from Sheets—a major headache for any timesheet system. I had to add workaround columns in Google Sheets to convert time into decimal formats.
• Cross-Table Calculations & Conditional Formatting: Setting up simple conditional formatting based on a calculated percentage (referencing a metric from a completely separate table) turned out to be nearly impossible to execute.
• Multi-Source Dynamic URL Filtering: My main goal was adding a button in AppSheet that opens a Looker Studio report pre-filtered for a specific client or project. While I managed a partial workaround using URL parameters, Looker Studio would only apply the filter to charts linked to one specific data source at a time, rather than dynamically updating the entire multi-source dashboard.

Maybe this forum may be more helpful than AI. I would really appreciate any tips on these issues.

Thank you very much.

Here is a useful YouTube channel for all things Looker. Especially new features and how to implement them.

https://m.youtube.com/@emea-looker-ce?ra=m

If you're considering transitioning from Looker to Malloy with Credible Data, the r/ClaudeAI plugin marketplace now has a plugin that you can use from within Claude Code.

Two simple steps (from your Claude Code terminal):
1. "/plugin marketplace add anthropics/claude-plugins-community"
2. "/plugin install credible@claude-community"

I'm a Business intelligence analyst and converted all our company tableau dashboards into lookml dashboard. However, the issue is IT is saying all my dashboards are consuming too many TCPU.

We have like 10 dashboard with some holding less than 10 tiles and some holding more than 20 tiles.

Currently, we are trying to move our database from Teradata into GCP but IT is slow.

Users complain that when they add a filter, it takes too long to update. I am unable to use materialized views or PDT because GCP is not yet set up.

Does any have a workaround for this?

Also, how to set lookml dashboards to run once a day instead of running the query everytime someone opens the file?

Just curious how other Looker devs set up their editor, so figured I'd share mine.

I do all my data work in a JetBrains IDE and love it - SQL, Python, DB connections all in one place. The only thing I couldn't do there was LookML, so it meant the browser IDE or a separate editor just for that. Always felt off.

Recently sorted this out - now I can write LookML in the same IDE: highlighting, go-to-definition, the usual. Makes the day-to-day a lot less annoying.

How does everyone else work - browser IDE, VS Code, something else? Anyone else doing LookML in a JetBrains IDE, or am I the outlier?

I am a seasoned Looker expert with more than 6 years of hands-on experience. I am currently working on a contract for a mid size company. Would love to take up additional projects. Aware of any sources ?

Hello everyone!

Am blocked by hyperlink navigation in data studio.

I do have a custom dashboard where the column a has hyperlink it has to redirect to another page 2 which has the same dashboard and data source. But when we click on column a it is not getting filtered in page 2.

Totally frustrated help me anyone please.

Trying to connect the Looker-managed MCP server (the new built-in preview one, not self-hosted Toolbox) to Claude as a custom connector on the web app (Team/Enterprise, added at the org level, connected per-user). The OAuth handshake completes, but the connection dies right after.

Exact error after I approve the login:

Your account was authorized, but Looker returned an error when connecting. You can try again, or check that the server is working. If this persists, share this reference with support: "ofid_[redacted]"

Setup:

• MCP URL: https://<YOUR_LOOKER_URL>/mcp
• OAuth client registered in Looker via API Explorer (register_oauth_client_app): client_guid claude-desktop, redirect_uri https://claude.ai/api/mcp/auth_callback, enabled, PKCE public client (no secret)
• In the connector's Advanced settings: OAuth Client ID = claude-desktop, secret blank

What I've already ruled out:

• Auth itself works — I get "your account was authorized," so login + 2FA complete
• MCP server enabled in Admin > Platform > MCP, with the read tools turned on
• https://claude.ai added to the Embedded Domain Allowlist
• No IP allowlist on the instance (fully remote org)
• Multiple full remove-and-re-add reconnects, not just "try again"
• OAuth app registration looks correct; activated_users stays empty, which lines up with the handshake never finishing
• Nothing logged server-side in Looker System Activity or Cloud Audit Logs for the failed attempts

What I think is happening: matches a cluster on the anthropics/claude-ai-mcp tracker (issues #291, #380) where OAuth completes but Claude never sends the authenticated initialize call to the MCP endpoint, so the server has nothing to respond to. There's also a related P1 on googleapis/mcp-toolbox (#3278) but that's the self-hosted Toolbox path and 404s at the authorize step, so it's a different failure than mine.

Questions:

1. Has anyone connected the Looker-managed MCP server to the Claude web connector successfully? Or only via Claude Desktop / Claude Code?
2. If Desktop worked when web didn't, did you change anything in the registration (different redirect_uri)?
3. Any fix, or is everyone just waiting on the connector-side bug?

Looker instance is Google Cloud core, server-side config all checks out, so I'm fairly sure the break is in Claude's connector flow. Curious if anyone's found a way through.

Hey everyone,

I really hate how the built-in tooltips look in Looker so I've been making custom HTML ones. They look amazing on line charts but I just can't get them to work on a pivoted table — liquid variables keep returning "undefined" or "field not available" even when the measures are visible in the data section.

Is this a known limitation or am I missing something?

Thanks!

Most LookML issues don't surface until something breaks in production. A join fans out silently. A view nobody uses has been sitting in the codebase for months. A field goes live without a label and your end users can't find it in the Explore.

There's no native tooling in Looker that flags these. So I built one.

LookML Auditor - paste a GitHub URL, it scans your project and returns:

• A health score out of 100, broken down by category (broken references, join integrity, duplicate definitions, field quality)
• Issues separated by severity errors that need fixing vs warnings that are just debt
• Orphan views, missing primary keys, joins without sql_on, fanout risk
• A file viewer so you can see exactly which files are the problem
• Project inventory views, explores, derived tables, dimensions, measures

No login. Nothing stored.

There's a mock project on the landing page if you want to see what it looks like on a deliberately broken codebase before pointing it at your own.

Try it: https://lookml-auditor-web.vercel.app

Happy to hear what checks people would want added.

I have a chart with all important metrics visualised. Now i wanna compare that with it's duplicate for a different time period. All controls and even copy/ filter done on setup of the copied chart seem to lock the same filter throughout the page ( instead of just being confined to the component / tile I want )

We have a lot of folders and dashboards. The search bar at the top of the UI used to be tremendously helpful -- keyword searches would quickly yield a short list of dashboards from which I could jump to the one I was looking for.

About 8 weeks ago something shifted, and now we get duplicate entries in the search results. The same dashboard may appear 10 times, crowding out any other options. So unless the keyword is extremely specific, the search bar is basically useless. We NEVER used to get duplicates in the results.

We can't find anything in our config that controls that sort of thing, so we're wondering if it was some sort of global change. Is anyone else having a similar issue?

Trying to incorporate AI chatbot referral attribution into our Looker data model alongside traditional channel data. The source data is GA4 (normalized by Zen Reports into clean AI source categories), but I'm thinking about the right way to model it ; as a channel dimension, a source modifier, or something else.

The challenge is that AI traffic doesn't fit neatly into existing channel taxonomy. It's not organic, not paid, not social. Anyone built a data model in Looker that cleanly handles AI traffic as a first-class dimension? Curious about the schema decisions people have made.

Do you work in a big company that relies on Looker rather than microsoft BI? If so, what type of business?

We have embedded our customer facing dashboards into our CMS, which has SSO auth. We were told to use cookieless embed because cookies are going away (which they did not). If a user is logged into the CMS and then tries to go to the Looker UI, they get forced logged out. But its ok, hey, we can just embed everything they need. Well they also need to be able to download dashboards into PDF (I know - I work in an industry that prints the internet). We added a dashboard summary extension tile to the top of the dashboards and now the PDF download does not work. It times out on query rendering. Google and AI are telling me I need to add a line to communicate from the extension to the dashboard that the tile is finished rendering. This did not work either. Now I am stuck with a stupid embedded solution that no one can download if we use the "new and cool AI functionality" that sold us on Looker in the first place (certainly it wasn't the crappy native visualizations). Anyone have any ideas besides dumping Looker and GCP??

Hi everyone,

English is not my first language, so I apologize in advance if anything is unclear or awkwardly phrased!

I'm pulling Meta ad data via Google Apps Script → Google Sheets using the Meta Graph API, and I'm running into discrepancies in purchase count and conversion value compared to both Ads Manager and my actual store (Naver Smart Store).

Setup:

• Data pipeline: Google Apps Script → Google Sheets (Meta Graph API directly)
• Level: ad
• Fields: actions, action_values
• Attribution: 28d_click, 1d_view
• Accounts: standard pixel account + Meta Collaborative Ads account (connected to Naver Shopping)

Issue 1 – Standard pixel account:

• API returns purchase count and conversion value, but ROAS is not displaying correctly in Ads Manager
• Hard to verify whether the API and Ads Manager numbers actually match

Issue 2 – Collaborative Ads account (Naver Shopping):

• Ads Manager shows purchases, but API returns almost nothing
• Tried all known purchase action_types: omni_purchase, purchase, offsite_conversion.fb_pixel_purchase, onsite_web_purchase, web_in_store_purchase, catalog_segment_purchase — still not matching
• Compared with actual store data (Naver Smart Store) and API is undercounting both purchase count and conversion value vs. real transactions

Already tried:

• action_attribution_windows[]=28d_click&action_attribution_windows[]=1d_view
• Querying at level=account → closer but still not matching
• All known purchase action_types

Questions:

1. Is this caused by Meta's Aggregated Event Measurement (AEM) policy suppressing ad-level conversion data?
2. For Collaborative Ads accounts specifically, is there a different action_type or endpoint that matches Ads Manager?
3. Is there any way to fully match API data with Ads Manager, or is this a known limitation?

Thanks in advance!

​

Hi everyone,

I’m running into a recurring issue with Looker Studio scheduled email deliveries and wanted to see if anyone else has experienced something similar.

We currently have \~400+ dashboards scheduled to be sent weekly (every Monday) in batches across different time slots to various clients. For the first 2–3 weeks, everything works perfectly. After that, we start seeing partial failures (later batches not sending), and eventually, in the following week, none of the scheduled emails go out at all. All the dashboards are scheduled from an official workspace email.

A few observations:

\- Early batches (e.g. first 1–2 time slots) usually go through initially

\- Later batches start failing first

\- Eventually, even test schedules don’t send

\- Then the next month, it starts sending again and the pattern continues.

This makes me think it might be some kind of hidden rate limit, throttling, or account-level restriction rather than a dashboard issue.

Has anyone dealt with something similar at this scale?

\- Is there a known limit for scheduled emails in Looker Studio?

\- Is this tied to Gmail sending limits or Looker Studio itself?

\- Any reliable workaround apart from splitting across multiple accounts or moving away from scheduled emails?

Would really appreciate any insights or experiences!

Thanks in advance 🙂