A data warehouse for Data Studio, designed and run by our team

How do you know your data has outgrown connectors?

The signs are usually errors, slowness and arguments about numbers. Direct connectors are fine for one or two sources, but they query the source system every time someone opens a page, and that does not scale.

These are the symptoms we see most often when companies come to us:

• GA4 quota errors, since the standard Data API limit is 14,000 tokens per project per property per hour.
• "Too many rows" errors on large tables, or pages that take many seconds to load.
• Blends that need more than 5 sources, or joins that are not simple equality matches.
• Fixed-schema connectors capping a table at 10 dimensions and 20 metrics.
• The same KPI calculated in three reports, giving three different answers.

What does a data warehouse for Data Studio look like in BigQuery?

It has three layers: raw data as it arrives, cleaned and standardized tables, and reporting tables shaped for dashboards. Data Studio only ever reads the last layer.

Raw tables hold each source unchanged, so nothing is lost and any calculation can be rerun. The clean layer renames fields, fixes types, converts currencies and removes duplicates. The reporting layer holds tables designed around questions: daily spend and conversions by channel, order lines with margin, a monthly finance table by account group. Because BigQuery is a flexible-schema source, those tables can carry up to 100 dimensions and 100 metrics per Data Studio table, well beyond the fixed-schema caps.

How do you get data from SaaS tools into BigQuery?

We use the simplest reliable route for each source. Google sources such as GA4 and Google Ads have their own exports and transfers into BigQuery. For Meta, LinkedIn, TikTok, Shopify, HubSpot and similar tools, partner connectors such as Supermetrics, Windsor.ai, Funnel or Power My Analytics can write to BigQuery on a schedule.

Accounting systems and internal tools often only offer file exports, so we land those in Google Drive or Cloud Storage and load them on a schedule. Each pipeline gets a freshness check that records the latest date loaded, because a pipeline that fails quietly is worse than one that fails loudly.

We also load history where the source allows it, so trend charts start with useful context rather than on the day the warehouse went live. Where a source only keeps a limited window, we say so up front and start collecting from the first day.

How do partitioning and scheduled queries keep costs down?

BigQuery charges mainly for the data each query scans, so the model is built to scan as little as possible. Partitioning tables by date means a dashboard showing the last 30 days reads 30 days of data, not five years.

We add clustering on the fields people filter by most, such as channel, region or customer, and use scheduled queries to build compact reporting tables once a day instead of letting every chart run heavy joins live. Date range controls in Data Studio default to sensible periods. We also set budget alerts and review the most expensive queries in the first month, so costs are understood before they grow.

How is access governed in the warehouse?

Access is set at the dataset and table level in Google Cloud IAM, and Data Studio respects it. Raw datasets are restricted to the pipeline and our team; reporting datasets are opened to the people who need them.

Where viewers should only see their own rows, such as a regional manager seeing one region, BigQuery sources run on viewer's credentials with row-level rules, and the dashboard shows each person only their data. With Data Studio Pro, reports are owned by the company through a Google Cloud project and IAM, with audit logging, CMEK and data residency options for companies that need them.

Does a warehouse help with Conversational Analytics?

Yes, and it is a requirement. Conversational Analytics in Data Studio, generally available since July 30, 2026, answers questions through data agents built in BigQuery and published to Data Studio.

An agent can only be as right as the tables it is pointed at. Clear table names, documented columns, one row per clearly defined grain and agreed metric definitions are exactly what a well-built warehouse already has. Companies that model their data first are the ones that get useful answers when they turn the feature on.

Is this a project or ongoing work?

Both, in sequence. The build is a fixed-price project: pipelines, model, reporting tables and the dashboards that read them, plus documentation of every table and field.

After launch, sources change. Ad platforms rename fields, a new store or entity is added, and leadership asks new questions. Many clients keep us on a monthly managed basis to watch pipelines, extend the model and keep costs in check. Others take it over with our documentation and call us when something new is needed.