Marketing data warehouse implementation
A marketing data warehouse consolidates ad platform, analytics, call tracking, and CRM data into one queryable store — normally BigQuery — with scheduled ingestion and a semantic layer that fixes what each metric means. Kodelytics designs the connector architecture, builds the pipelines, and defines the models the reporting layer reads from.
Reporting assembled from platform exports is expensive in the least visible way: someone spends the first week of every month pulling, aligning, and reconciling spreadsheets, and the result can't be queried, versioned, or audited. It also caps what you can ask. Questions that span platforms — spend to booked revenue by location and service line — are unanswerable without a place where the data sits together.
There's also a durability argument. Platform retention windows expire, connectors change formats, and agencies get replaced. A warehouse in the client's own cloud project means the historical record survives all three, which is usually the argument that persuades a CFO rather than a marketing lead.
A semantic layer is the set of versioned definitions — in SQL, one place — that turn raw platform data into the metrics an organization reports. It exists so that a metric name means exactly one thing regardless of which dashboard displays it.
What's actually wrong
These are the symptoms buyers of this service recognize before they can name the problem.
- Monthly reporting starts with several CSV downloads and a pivot table.
- The same metric is calculated slightly differently in two dashboards.
- Historical data is limited to whatever each platform's retention window allows.
- Cross-platform questions are answered with an estimate, or not answered.
- A change in a platform's export format silently breaks a report.
- Nobody can reproduce last quarter's number from source data.
What the engagement includes
- Warehouse design in BigQuery: dataset structure, partitioning, and a raw-staging-modelled separation.
- Connector architecture — managed connectors, platform APIs, or scheduled exports, chosen on cost and reliability rather than novelty.
- Scheduled ingestion with defined refresh windows, backfills, and failure alerting.
- Normalization across platforms: currency, time zone, campaign taxonomy, and location identifiers.
- A semantic model — the metric definitions, in SQL, that every report reads from.
- Data quality checks: row counts, freshness, and reconciliation against the source platform.
- Cost controls: partitioning, clustering, scheduled query budgets, and a monthly cost estimate before build.
- Documentation of lineage, so any number can be traced back to its source.
Raw, staging, modelled
Ingested data lands raw and immutable, so a platform's own restatement of history is visible rather than silently absorbed. Staging normalizes the awkward parts — currency, time zone, campaign taxonomy, location identifiers — and modelled tables carry the definitions reporting reads.
That separation is what makes a number reproducible. When a figure is questioned six months later, the raw record still exists and the transformation that produced the figure is in version control.
Cost control is part of the design
Marketing datasets are small by warehouse standards, and most cost overruns come from unpartitioned tables scanned by scheduled queries several times a day. Partitioning, clustering, bounded scheduled queries, and a monthly budget alert are set at build time, not after the first surprising invoice.
Connector licensing, not compute, is usually the larger line. Managed connectors are used where the licence is justified by the source's complexity; where a platform API or a scheduled export does the job, that's what gets built. Kodelytics resells no connector and takes no vendor commission.
Exports, connector-to-dashboard, or a warehouse
| Manual exports | Connector → dashboard | Warehouse | |
|---|---|---|---|
| Setup cost | None | Low | Project |
| Monthly effort | Days | Low | Low |
| History | Whatever you saved | Platform limits | Unlimited |
| Cross-platform joins | By hand | Limited | Native |
| Auditability | None | Weak | Full lineage |
| Right for | One-off analysis | Single-source reporting | Multi-source, multi-location, finance-grade |
How it's scoped and priced
Scoped as a fixed-fee project for the initial build, with the source count and the number of locations as the main cost drivers. A three-source warehouse for a single business is a short engagement; a multi-location operator with ad, analytics, call, and booking data is a phased build.
Ongoing pipeline maintenance is a small monthly retainer, or handed to your team with the documentation. Warehouse infrastructure cost is billed by Google directly to your cloud project — never marked up, and estimated before the build starts.
Kodelytics does not publish rates. Every engagement is quoted after a discovery call, because the same service name covers materially different amounts of work.Ask for a quote.
What you get at the end
- A provisioned BigQuery project with documented datasets.
- Scheduled pipelines with alerting on failure and staleness.
- A semantic model in version-controlled SQL.
- A metric definitions document tied to that model.
- A cost estimate and monitoring configuration.
Questions
Do we need a data warehouse, or is a dashboard enough?
If your reporting reads from one platform and history beyond that platform's retention doesn't matter, a dashboard is enough. A warehouse earns its cost when you need cross-platform questions answered, retention beyond platform limits, per-location rollups, or numbers that can be reproduced and audited.
What does a BigQuery marketing warehouse cost to run?
For most mid-market marketing datasets, storage and query costs are modest — commonly tens of dollars a month, not thousands — provided tables are partitioned and scheduled queries are bounded. The larger cost is usually managed connector licensing, which is why connector choice is part of the design work rather than an afterthought.
Which connectors do you use?
Whatever survives cost and reliability review for your source list: managed services where the licence is justified, platform APIs or scheduled exports where it isn't. Kodelytics has no reseller relationship with any connector vendor.
Can it feed Looker Studio, Tableau, or Power BI?
Yes. The warehouse is deliberately BI-agnostic — the semantic model is the contract, and any reporting layer reads from it.
Who owns the cloud project?
You do. It's provisioned in your Google Cloud organization and billed to you directly.