Map the sources
GA4, ad platforms, CRM, product database and spreadsheets listed with owners, fields, refresh needs and the key that joins them.
4.9 / 5Google Cloud data pipelines from your website to BigQuery.
Google Cloud data pipelines that bring website, marketing and operational data into BigQuery, validated and ready for reporting.
Selected work, in motion.
Digital experiences with connected systems behind them.Reports stitched by hand.
When reporting depends on spreadsheet exports, the numbers are always late and rarely agree.
BeforeAfter
GA4 sampling and interface limitsRaw GA4 events in BigQuery through the daily export
Ad spend copied from five platforms each weekScheduled transfers landing spend data in BigQuery
CRM and web data that never meetJoined tables linking campaigns to leads and revenue
Nobody sure if yesterday's data arrivedFreshness and row count checks with alerts
What's included
From source to BigQuery.
We build the pipelines that turn scattered website and marketing data into one reporting layer in BigQuery.
Implement the pipelines
BigQuery Data Transfer Service, the GA4 export, Datastream or Cloud Run jobs, chosen per source and scheduled with Cloud Scheduler or Workflows.
Validate the data
Row counts, freshness and totals checked against each source, with alerts when a load is late, empty or out of line.
Model for reporting
Dataform or SQL models that turn raw tables into clean, documented reporting tables for Looker Studio or your BI tool.
Security, by consideration.
Trust is part of the build.
For your Google Cloud project, we consider service accounts, workloads, data stores and deployment permissions as part of one connected build.
See our approachConnected systems. Considered safeguards.
Changes you can trace.
GitHub workflows, peer review and dependency checks bring decisions into the release process.
Protected in transit.
Certificate configuration, HTTPS and secure headers help protect the path between your users and your systems.
Intelligence with limits.
Scoped tool access, validated inputs and human approval where automated actions carry consequences.
Source, pipeline, report.
Every pipeline is checked against its source before any report uses it.
- 01
Map
Sources, join keys, owners and reporting needs documented.
- Source map
- Join keys
- Reporting requirements
- 02
Build
Transfers, exports and custom jobs built into BigQuery.
- Pipelines
- Raw datasets
- Scheduling
- 03
Validate and model
Checks against every source, then reporting tables built and documented.
- Validation checks
- Reporting tables
- Data dictionary
Built for what’s next.
Built to driveMore sign-ups.
Built to driveMore memberships.
Built to driveBetter-qualified leads.
Built to driveMore qualified candidates.
Built to driveMore enquiries.
Built to driveMore qualified demos.
Built to driveMore workplace demos.
Built to driveMore business accounts.
Built to driveMore free trials.
Built to driveMore building owners.
Built to driveMore free sign-ups.
Private Villas MexicoBuilt to driveMore direct bookings.
Built to driveMore committed players.
Built to driveMore returning customers.
Built to driveMore meaningful demos.
Built to driveBetter talent connections.
Built to driveMore course creators.
Built to driveMore product discovery.
Trusted across our practice
Working together. In their words.
Independent reviews. Long-term working relationships.
“We’ve been working with Stega & Gabriel for the last two years. Cannot recommend them enough. They’re responsive, professional & an asset.”
“Stega Creative team not only understood our needs and vision for the websites, but they also came up with improvements.”
“After a website (re)launch, they are also always accessible and happy to help. I highly recommend everyone who is looking for a new and more stable website in Webflow, to work with Stega!”
Google Cloud data pipeline questions.
A few things worth discussing before your Google Cloud data pipelines takes shape.
How do you set up the GA4 export to BigQuery?
The GA4 export to BigQuery is set up by linking your GA4 property to a Google Cloud project in GA4 admin. The daily export comes with GA4, with BigQuery storage and queries billed separately, and standard properties are capped at one million events a day. We then flatten the nested tables for reporting.
Which Google Cloud tools do you use for data pipelines?
Our Google Cloud data pipelines use BigQuery Data Transfer Service for Google and ad sources, Datastream for databases and Cloud Run jobs for APIs without a connector. Cloud Scheduler or Workflows run them, and Dataform models the results. We pick managed options first, because they need less upkeep.
Is BigQuery expensive for marketing data?
BigQuery is rarely expensive for marketing data at typical website volumes, because storage is cheap and on-demand queries are billed by data scanned. Costs rise when dashboards scan raw event tables over and over. We partition tables, build smaller reporting tables and set query limits, which keeps monthly bills predictable.
How long does it take to build a Google Cloud data pipeline into BigQuery?
Native connectors such as the GA4 export are quick. Custom API sources and identity joins between web and CRM data take longer, and validation runs through every stage.
Revamp today.The 
way.
Your ambition. Our collective craft.
Strategy, design and technology, working as one.